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TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO THE AGRICULTURAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES PROJECT IADB ATN/OC – 10064 – RG

Funded by: Inter-American Development Bank Regional Public Goods Executing Agency:

INTERNATIONAL RESEARCH CENTER ON “EL NIÑO”

Escobedo #1204 y 9 de Octubre Edificio Fundación El Universo, 1er piso Phone: (593 4) 2514770 Fax: (593 4) 2514771 P.O. Box # 09014237 Guayaquil-Ecuador

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International Research Center on “El Niño” (2009) It is allowed to reproduce and communicate this guide as long as the source is referenced correctly and it is not used for commercial purposes. Some copyrights http://creativecommons.org/licenses/by-nc/3.0/ Concept, Design & Infographics 2009, by Leonardo Briones Rojas Title page José Benito Valarezo Loor Photo Abigail Alvarado, Patricio López, Borja Santos Print Gráficas Hernández Cía. Ltda. Cuenca, Ecuador December, 2009 To refer the whole Technical Guide: Martínez, R., Mascarenhas, A., Alvarado, A., (ed)., 2009. Technical Guide for the Implementation of a Regional Climate Information System Applied to the Agricultural Risk Management in the Andean Countries. International Research Center on “El Niño” –CIIFEN, p 1-160. To refer one chapter of the Technical Guide: Ycaza P., Manobanda N., 2009. Implementation of Agro-climatic Risk Maps, p 50-62. On the Technical Guide for the Implementation of a Regional Climate Information System Applied to the Agricultural Risk Management in the Andean Countries., Martínez, R., Mascarenhas, A., Alvarado, A., (ed)., 2009. International Research Center on “El Niño” –CIIFEN, p 1-160. ISBN: 978-9978-9934-1-5 This publication was made by the International Research Center for “El Niño” –CIIFEN under the Project ATN/OC 10064-RG “Climatic information Applied to Risk Management in Andean Countries”, funded by the Inter-American Development Bank, IDB, under the initiative of Regional Public Goods (2006).

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TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTURAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES


TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTURAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES PROJECT IADB ATN/OC – 10064 – RG

Funded by: Inter-American Development Bank Executing Agency:

INTERNATIONAL RESEARCH CENTRE ON EL NIÑO And The National Meteorological and Hydrological Services of Bolivia, Chile, Colombia, Ecuador, Peru and Venezuela

OCTOBER, 2009


Editorial Team: Rodney Martínez Güingla Affonso Da Silveira Mascarenhas Jr. Abigail Alvarado Almeida

Project Team: General Coordinator Rodney Martínez Güingla Financial Administration and Acquisitions Roma Lalama Franco

Systems Engineering Katiusca Briones Estebanez

Information Assistant Nadia Manobanda Herrera

Risk Maps Harold Troya Pasquel

Regional Agriculturist Risk Angel Llerena Hidalgo

Climatical Data Processing Pilar Cornejo Rodriguez

Local Risk Experts Bolivia Silvia Coca Uzuna Chile Claudio Fernandez Pino Colombia José Boshell Villamarín Ecuador Emilio Comte Saltos Perú Oscar Quincho Ramos Venezuela Pedro Rodriguez González

Local Experts on Information Management Bolivia Javier Caba Olguín Chile Miguel Egaña Colombia Juan Gómez Blanco Ecuador Emilio Comte Saltos Perú Juan Ramos Escate Venezuela Pedro Rodriguez González

Data Digitalization Bolivia José Valeriano Maldonado Luis Bustillos Paz Chile Viviana Urbina Guerrero Patricia Berrios Leiva Colombia Carlos Torres Triana Paola Bulla Portuguez Ecuador Carlos Naranjo Silva Ana Zambrano Vera Perú Luis Zevallos Carhuaz Juan Bazo Zambrano Venezuela Vickmary Nuñez Oropeza Gabriel Diaz Loreto

Statistical Modeling Marco Paredes Riveros Numerical Modeling Ángel Muñoz Solórzano Numerical Modeling Ricardo Marcelo Da Silva Virtual Core Red de Universidades del Eje Cafetero Alma Mater Data Base Centro de Tecnologías de la Información - ESPOL

CIIFEN Staff – Project Counterpart International Director Patricio López Carmona 2006-2007

International Director Affonso Da Silveira Mascarenhas Jr. 2008-2009

Geographic Information Systems Pilar Ycaza Olvera Mishell Herrera Cevallos Carlos Meza Baque Carlos Zambrano Alcívar

Research Systems Abigail Alvarado Almeida Alexandra Rivadeneira Uyaguari

Administration Mayra Mayorga López Evelyn Ortíz Sánchez Victor Hugo Larrea Alvarado

Informatic Support Alberto Abad Eras

Data and Products Management Juan José Nieto López


NATIONAL METEOROLOGICAL SERVICES

BOLIVIA

METEOROLOGICAL AND HYDROLOGICAL NATIONAL SERVICE - SENAMHI

Director Carlos Díaz Escobar Statistical Modeling Gualberto Carrasco Yaruska Castellón Nidia Zambrano Virginia Rocha

CHILE

Focal Point for the Project Pablo Elmer Dynamic Modeling Gualberto Carrasco Erick Pereyra Ramiro Solíz

Agro-Climatic Risk Maps Yaruska Castellón Oscar Puita

www.meteochile.cl

METEOROLOGICAL DIRECTION OF CHILE - DMC

Director Myrna Araneda Fuentes

Statistical Modeling Juan Quintana

COLOMBIA

Focal Point for the Project Gualterio Hugo Ogaz

Agro-Climatic Risk Maps Patricio Lucabeche José Curihuinca

Dynamic Modeling Claudia Villarroel Roberto Hernández

Information Systems Miguel Egaña

INSTITUTE FOR HYDROLOGICAL, METEOROLOGICAL AND ENVIRONMENTAL STUDIES - IDEAM

Director Carlos Costa Ricardo Lozano Modeling Gloria León Aristizábal

www.senamhi.gov.bo

Focal Point for the Project Ernesto Rangel Mantilla Christian Euscátegui Quality Analysis Ruth Correa Amaya

www.ideam.gov.co

Agro-Meteorological Analysis Gonzalo Hurtado Moreno Ruth Mayorga Márquez


NATIONAL METEOROLOGICAL SERVICES

ECUADOR

NATIONAL INSTITUTE OF METEOROLOGY AND HYDROLOGY - INAMHI www.inamhi.gov.ec Focal Point for the Project Raúl Mejía Flavio Ramos

Director Carlos Lugo

Statistical Modeling Cristina Recalde

PERÚ

Agro-Climatic Risk Maps Fanny Friend

Dynamic Modeling Jaime Cadena

NATIONAL SERVICE OF METEOROLOGY AND HYDROLOGY - SENAMHI

Director Wilar Gamarra Molina

Statistical & Dynamic Modeling Carmen Reyes Bravo Juan Bazo Zambrano

VENEZUELA

Focal Point for the Project Darío Fierro Constantino Alarcón Information Systems Luis Zevallos Carhuaz

METEOROLOGICAL SERVICE OF THE BOLIVARIAN NATIONAL AVIATION

Director Ramón Velásquez Araguayan Statistical & Dynamic Modeling Luis Monterrey Alexandra Mata Elddy Anselmi

www.senamhi.gob.pe

Agro-Climatic Risk Maps Darío Fierro Zapata Kevin Sánchez Zavaleta Nelly Perez Díaz

www.meteorologia.mil

Focal Point for the Project Alexander Quintero

Data Systems and Digitalizing Richard Núñez Jenny Castillo Manuel González

Agro-Climatic Risk Maps Carlos Ojeda Luis Monterrey César Yauca


INTRODUCTION

S

ince it was established in January 2003, one of the most important mandates of the International Research Center on El Niño (CIIFEN) has been to build the necessary bridges between the climate information suppliers and the users from other sectors of the society.

The ultimate goal is to use the benefits provided by earth observation, science and predictions in order to allow our society to live better. When we talk about risk management, it results in reduced loss of life and goods for development support. To review all the climate information to make it a tool for human welfare is not easy, it requires a holistic vision, inter-and trans-disciplinary dialogue and, above all, it requires breaking several paradigms. In 2003 the World Meteorological Organization, through its Division of Services and Climate Applications, organized, in alliance with CIIFEN, a regional workshop to identify the needs of climate information for the agricultural sector. This meeting provided us with essential information to generate (after several years) a regional proposal that addresses the needs of this important sector. In 2006, the Inter-American Development Bank (IADB), under the modality of Regional Public Goods, approved the project entitled “Climate Information Applied to Agricultural Risk Management in the Andean countries” to be carried out by CIIFEN and the National Meteorological Services of Bolivia, Chile, Colombia, Ecuador, Peru and Venezuela. After three years of efforts, regional cooperation and with the trust and support of the IADB, we can bear witness to this important initiative through this Technical Guide, which describes step by step how we implemented the system in each one of its components, including the learned lessons, sustainability strategies and future challenges. With deep gratitude to the Inter-American Development Bank (IADB)The World Meteorological Organization (WMO), The National Meteorological and Hydrological Services (NMHSs) and the International Research Centre on El Niño (CIIFEN), we present the “Technical Guide for the Implementation of a Regional Climate Information System Applied to Agriculture Risk Management in the Andean Countries”. We hope that it can be replicated in other places around the world for the benefit of our society.

Dr. Affonso Mascarenhas

Oc. Rodney Martinez Güingla

International Director CIIFEN

Project Coordinator ATN/OC 10064-RG INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN

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INDEX Introduction

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Chapter I: Implementation of the Virtual Core for Climate Applications (VCCA)

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1.1 Conceptual model

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1.2 Technologic Platform

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112 112 112

1.2.1 VCCA Architecture 1.2.2 Physical Infrastructure 1.2.3 Logical Infrastructure

1.3 Applications That make up the VCCA

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113 114 115 115 116

1.3.1 Regional Climate Data Base 1.3.2 Map Server 1.3.3 Display of Climate Model Products 1.3.4 Virtual Library

1.4 Implementation Process of Regional Climate Data Base

Chapter II: Implementation of statistical modeling for climate prediction

117 118

2.1 Conceptual and methodological elements 119 2.2 Management to update the information of the Predicting Variables 119

2.2.1 How to perform the Alternative Method for Forecast Updates. 119

2.2.1.1. Procedure for obtaining the sea surface temperature (SST) variable. 2.2.1.2 Procedure for obtaining the wind variable at altitude, geo-potential, temperature at mandatory levels.

121 122

2.3 Operating simultaneous predictors with CPT. 122

2.3.1 Climate Forecasting with Simultaneous Predictors 123

2.4 Decision criteria for managing the CPT results. 127 2.5 Considerations for the interpretation of terciles 127 2.6 FAQs related to CPT operation.

Chapter III: Implementation of numerical models for climate prediction

131 132

3.1 Step by step installation and implementation procedures for MM5 and WRF models in Climate Mode.

3.1.1 Operating System 3.1.2 Atmospheric Models

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3.1.2.1 MM5 3.1.2.2 CMM5 3.1.2.3 WRF 3.1.2.4 CWRF

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3.1.3 Oceanographic Models

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3.1.3.1 ROMS

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3.1.4 Displayers

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3.1.4.1 GRADS 3.1.4.2 Vis5D

3.2 Implementation of Numerical Modeling for Climate Forecasting The Regional Group of Numerical Modeling

Chapter IV: Implementation of Agro-Climatic Risk Maps

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4.1 Definition of Risk

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4.2 Conceptual Mathematical Model of Agro-Climatic risk

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4.3 Components and Variables of Agro-Climatic Risk

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147 147

4.3.1 Thread 4.3.2 Vulnerability

4.4 Project Application Areas

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4.5 Information Requirements

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4.5.1 Agro-ecological 4.5.2 Base mapping 4.5.3 Thematic mapping 4.5.4 Treatment of information 4.5.5 Soil and climate characteristics in pilot areas

4.6. Agro-Climatic Risk Calculation

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4.7. Agro-Cimatic risk in the Andean Countries

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Chapter V: Implementation of local systems of climate information

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5.1 Conceptual and methodological elements.

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5.2 Identification and mapping of key components.

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5.3 Strategic Alliances

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5.4 Strategic alliances with local authorities.

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5.5 Strategic alliances with the private sector. 5.5.1 Journals Specialized in Agriculture 5.5.2 Cell phone Company

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5.6 Strategic alliances with the media.

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5.7 Training Strategies

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Chapter VI: Capacity building in Western South America

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6.1 Regional Training Workshop on Climate Modeling statistics

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6.2 Regional Training Workshop on Numerical Modeling for Climate predictors

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6.3 Regional Training Workshop for Agro-Climatic Risk Mapping

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6.4 Regional Workshop on Numerical Modeling of Weather and Climate II

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6.5 International Training Workshop on Climate Data Processing

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Chapter VII: Performance Indicators.

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Chapter VIII: Learned Lessons

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Chapter IX: Future Actions

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Chapter X: Elements of Sustainability

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Bibliographic References

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CHAPTER I Implementation of the Virtual Core for Climate Applications (VCCA)


CHAPTER I

Katiusca Briones k.briones@ciifen-int.org

1.1 CONCEPTUAL MODEL

1.2.3 Logical Infrastructure

The Virtual Core for Climate Applications (VCCA) consists of a basic computing infrastructure to run climate applications designed to provide climate information through an easy to use and easy access format through the Internet.

The client-server architecture requires a server capable of conducting central processing of the applications running on it, while clients “ask for” information without having to process it internally. On this logic infrastructure scheme, a centralized databases is created according to the application, so the information is kept in one place with the particularity of being accessible for viewing and/or maintenance, depending on the type of user (end user or administrator).

Under this philosophy, the VCCA centralizes all the necessary functionalities for different types of advanced WEB applications, such as: products presentation, users’ control, management of geographical information system, bibliographic information and search for information.

1.2 TECHNOLOGIC PLATFORM

The developed applications communicate with the corresponding database independently, placing the visual interface over the one that is displaying the information requested by the user (Fig. 2).

1.2.1 VCCA Architecture The main purpose of the applications running on the VCCA is to provide information to end users, without requiring the installation of any special software. This established the client-server architecture, in which CIIFEN would be in charge of the central server, and the end users would have access through web interface using the Internet. This allows simultaneous user connectivity and protection of published information. Figure 1 shows VCCA architecture graphically, in which the physical infrastructure (servers), the logic infrastructure (software) and the end users intervene.

Figure 2. Software Infrastructure of Virtual Core for Climate Applications (VCCA) The technical details of the software used in the VCCA are described as follows: Operating Systems Management and applications servers, run on Linux SUSE Operating System V.10, which has been demonstrated to be sufficiently stable, ensuring the availability of permanent climate applications over time.

Figure 1. Virtual Core for Climate Applications Architecture (VCCA)

1.2.2 Physical infrastructure The VCCA was implemented with two servers, for the administration of CIIFEN’s internal network and VCCA installation. The servers used are: Dell PowerEdge 2950 with a Xeon Dual Core 2.66GHz processor with 4GB of memory and a disk capacity of 600GB (primary server), 300GB (secondary server) and RAC type servers.

Database Management System (DMS) Database Management Systems DMS are intended to support the tasks of defining, creating and manipulating relational databases; for which that allows for features such as: concurrency control, data backup methods and access control applying user profiles. The virtual core operates with the DMS, called PostgreSQL 8.3; this is a Relational Object type system, and is used extensively due to the characteristics of the standards applied, the securities and the capability of communicating with various types of applications, among which is the ability to store spatial data, which is needed for applications of the geographical information systems. Spatial Information Management System (SIMS) The web visualization of cartographic information, agricul-

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ture climate risk and geographic information system requires the use of several special-purpose tools that together allows the full functionality of the map display system. The tools used are:

tem applied to agriculture climate risk management in the Andean countries as a Regional Public Good that contributes to the understanding of past climate and its important role of evolution over time.

PostGIS: Modules under GNU license provide the PostgreSQL Database management system with the ability to manage spatial information.

Figure 3 shows the Database which is available at http://vac. ciifen-int.org and contains records from 170 meteorological stations from 1952 until 2008 and is the start of a data exchange system without precedent, which will also improve climate forecasting services in the region. It contains daily records of Precipitation, Maximum Temperature, Minimum Temperature, Basic Data Stations and also displays climate products as time series or spatial graphics.

MapServer 5: CGI Aplication (Common Gateway Interface) which is a standard for communicating between a Web server and a program, so that the user can interact through the Internet (in the case of dynamic maps). Grass: Geographical Information System, that handles the information on the web. Web Server Apache 2.0: HTTP SERVER (the protocol that defines the semantics used for clients and servers to communicate with each other); it is multi-platform open code. Its architecture allows the addition of modules to provide several functions, such as dynamic webpage support and message encryption. Application Support Java Application Platform (SDK): Platform on which certain components (climate database) of the VCCA are run. Perl: Program to run certain components of the applications (display of numerical modeling products).

Figure 3. Startup Screen of the Regional Climate Database

End User One of the goals outlined in the development of VCCA, was to eliminate the need for the user to install any special software. To access any of VCCA applications, the user needs only to have an Internet connection, run his preferred browser program and access the appropriate link.

The application allows to view different types of graphs (time series, contour plots, histograms) and to check meteorological stations (location, general information). For the creation of the Databse and its updating, a Protocol between National Meteorological Services and CIIFEN was signed (Annex I).

1.3 APPLICATIONS THAT MAKE UP THE VCCA

Available chart types: The application provides three sets of information:

The project developed the applications in the VCCA: • Regional Climate Data Base: http://vac.ciifen-int.org • Map Server: http://ac.ciifen-int.org/sig-agroclimatico • Display of Climate Modeling Products: http://ac.ciifen-int.org/modelos • Virtual Library: http://ac.ciifen-int.org/biblioteca/

Data Search: allows us to select the graphic display of time series and histograms, and also to download the data in text format of the CPT model1 in maximum / minimum / accumulated monthly, bimonthly, quarterly or yearly. (Fig. 4) (Fig. 5)

1.3.1 Regional Climate Data Base The Regional Climate Data Base corresponds to an application for displaying climate data of temperature and precipitation in the Andean countries (Bolivia, Chile, Colombia, Ecuador, Peru and Venezuela). The Regional Climate Data Base for Western South America, is an cooperative effort without precedents among the National Meteorological Services of the region and is a giant step towards the integration of climate data to be used use in regional forecasting and also to contribute to atmospheric sciences research. This information resource is made possible thanks to the unflinching support and hard work of the NMHSs of Bolivia, Chile, Colombia, Ecuador, Peru and Venezuela. The database is one of the columns of the climate information sys-

Figure 4. Screen of Data Search by Regional Climate Database Stations 1. Climate Prediction Tool, http://portal.iri.columbia.edu

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Figure 8. Graphics screen of Climate Products of Regional Climatic Database by country Figure 5. Display Screen of Time Series of the Regional Climate Database Stations: Displays a list of all the weather stations involved in the ATN / OC 10064-RG project, identifying details of its basic information, location and additional information for each of these. (Figure 6)

Data Update The Climate Database application is fully upgradeable; to allow this it has an administrator interface, in which each country can connect through the same interface and upload the data files.

1.3.2 Map Server The Map Server Application aims to provide the user with the ability to visually manipulate different levels of GIS information through a friendly web interface, without running any specialized software in the computer. This Web-based interface provides the ability to view any final product of a GIS, as is the case of the agriculture climate Risk GIS, the initial product placed on the display. We need to indicate that a pre-processing of the levels is necessary in order to publish them from shape to xml format.

Figure 6. Detail display screen of the Regional Climate Database Stations

Graphic Interface The graphical interface of the Map Server allows end user to select the different Andean countries involved in the project. For each one of them, available information is displayed. (Figure 9).

Climate Products: Displays spatial graphics using a format of isolines for precipitation and temperature, in which is possible to select an area of a country or of the South American Continent (Figure 7) (Figure 8).

Figure 9. Map Server Startup Screen

Figure 7. Selection screen of Climatic Products of the Regional Climate Database

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Each link within countries displays a list of layers and topics developed in the project. The selected layers and selected topics are displayed in a GIS management interface, in which the user can hide/display layers, zoom in/out, display information from the components of each layer, select components, measuring tools, insertion of points of interest,

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and download images in GeoTIFF format (geo-referenced image). (Figure 10) The application has been developed for the user to load new layers of information, for which it is necessary to transform each layer from shape format to xml format.

Figure 12. Display screen of climate model products, Accumulated Precipitation variable

Figure 10. Map download tool in GeoTIFF format from the geographic information system based on the Web.

1.3.3 Display of Climate Model Products The goal of the Climate Modeling Products display is to create an application on which the end user can display products from different numerical climate models (Fig 11).

Figure 13. Display Screen Climate Model Products, Air Temperature variable since its creation, the goal of the application is to publish books, magazines, reports, presentations, CDs, and more free access information sources and to disseminate it to the general public. (Figure 14)

Figure 11. Home Screen Numerical Modeling Products Display The developed Web interface allows the user to choose the climate model he wants to view and select the dates on which forecasts have been made. Once the user has chosen the date, he can select the domain and the climate variable, and then, the corresponding product will be displayed. (Figure 12) The interface on which numerical modeling products are published is Google Earth satellite images interface, which makes this application a topographic information tool that is allows to visualize areas of different altitudes when analyzing climate forecasts. (Figure 13)

1.3.4 Virtual Library The purpose of the Virtual Library is the systematization of the vast amount of information that CIIFEN has collected

Figure 14. Startup screen Digital Library The virtual library is published at: http://ac.ciifen-int.org/ biblioteca. There are two search options: by Books and Digital Archives: Books Section 窶「 Contains information of books, magazines, journals, atlases, and other paper publications. INTERNATIONAL RESEARCH CENTRE ON EL NIテ前 - CIIFEN

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Digital Archives Section • Contains information of presentations, CDs and DVDs of applications, reports, data and projects which CIIFEN has compiled from the various events in which it has participated. These archives are available for free. The application has a search interface in which the user can enter keywords and select the search type. As a result, it will show all matches found in the library, identifying the information of each publication. (Figure 15)

each country can access using the appropriate username and password to manage their data and also to add new information. • The Regional Climate Database contains 3,876,035 climate records and will maintain up to date according the established protocol.

Figure 15. Display screen Digital Library publications Administrative interface The application has an administrative interface through the Control Panel option, in which the library administrator has several management options, such as adding categories, add/edit/delete/reserve publications and add/delete users.

1.4 IMPLEMENTATION PROCESS OF A REGIONAL CLIMATE DATA BASE Integrating climate information from the Andean countries was a fully coordinated joint effort in which the NMSs provided maximum collaboration to the compilation of national databases. This process was executed in five stages: • Collection of information: In order to determine the availability of information in different existing formats within each National Weather Service, they proceeded to perform a survey on the staff, which determined the amount of digital data and hard copy information • Acquisition of computer equipment: The digitization of information required the acquisition of computer equipment; two computers for each NMHS were designated for this purpose. • Hiring of operators: The amount of information to be loaded was based on surveys, and it was coordinated with each NMS to hire two operators for each institution. They processed the information in the appropriate formats. • Compilation of information: The digital information was added to the databases of each NMS, increasing the density of climate data in each institution. • WEB Application Development: Based on information gathered by each NMS, the Web application was developed with the data of precipitation, maximum and minimum temperature. In the application, a database maintenance module was developed in which a representative of

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CHAPTER II Implementation of statistical modeling for climate prediction


CHAPTER II 2.1 CONCEPTUAL AND METHODOLOGICAL ELEMENTS The tool used for the implementation of statistical modeling in every country was the Climate Predictability Tool (CPT), developed by the IRI. The flow chart of actions designed for the region is explained in the Figure16.

Development of CPT Data

Regionalization of variables Climate monitoring

Marco Paredes m.paredes@ciifen-int.org

The Sea Surface Temperature (SST), due to variable inertia, experiences slowly changes in its physical patterns. Under this premise, any change in the next four or five days will not be significant on the monthly average, which is carried out by averaging the first 27 days of the current month and is attached to the time series of sea surface temperature that can be obtained from NOAA/NCD/ERSST. At the end a complete and updated historical series is obtained, which serves as the final predictor. Atmospheric variables, such as zonal wind, southern wind, temperature at high levels, specific humidity, among others, we must take with extreme caution the changes in these last five days of the month. They can be significant and may modify the average. It is therefore advisable to monitor climatic conditions globally and in particular South America or the region of interest. The analysis of the monitoring of various oceanic and atmospheric variables should be conducted every two weeks. If possible, it is recommended to do it on a weekly basis, as shown in Figure 17:

Updating of predictors

Regionalization of predictor Run of CPT Analysis of results Assemblies

Spatial Graphics

Dissemination Figure 16. Process for the realization of the seasonal forecast, using the CPT For the purpose of using the CPT, the information from the National Meteorological Services is collected on the 30th of each month (28th in February), before the update done by the Global Forecast Center. This will be basic information that serves as a predictor, under the following assumptions:

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Figure 17: Pressure Monitoring conducted at sea level by NOAA / NCEP / NCAR It shows the average to date. In the right corner it shows the anomalies that occurred during that period and at the bottom the climate for the same period, to compare them. Likewise, we should keep in mind the monitoring of SST

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and its anomalies and their influence on changes in climate patterns.

2.2 MANAGEMENT INFORMATION UP TO DATE FOR VARIABLE PREDICTORS 2.2.1 How to perform the Alternative Method for Forecast Updates The monthly predictive indices of global forecasting centers provide information that is available in the IRI’s Data Library. They are updated on the 10th of each month with information from the previous month. As a result, the statistical model run are delayed until a date after the 10th for SST and the 15th for other predictors.

Figure 19 Press the weekly data (weekly): (Figure 20).

To avoid this we have chosen an action that allows an update a few days in advance of some of the necessary predictors, especially sea surface temperature (SST) under the following circumstances: • It is considered that the predictor to be analyzed will not undergo significant changes when it is completed with the missing data until the end of the month. • 75% of the days of the month are averaged so as to be considered representative. This means that at least 21 days of the month must have already passed. • The changes in the SST’s values have no abrupt behavior because of the ocean’s inertia (specific heat, which allows a delay in heat loss by up to 5 times longer than on land).

Figure 20 Choose the option Sea Surface Temperature (SST) (Figure 21).

2.2.1.1 Procedure for obtaining the sea surface temperature (SST) variable In this case, there must be prior historical information from the SST variable obtained from NOAA / NCDC / ERSST in order to predict the “Y” variable. For weekly SST data, the corresponding search in the IRI data library is performed in the Air-Sea interface category. (Figure 18).

Figure 21 Choose to download the data of the weeks of interest (Figure 22).

Figure 22 The process is the same that was used to download information on any other variable. The only difference occurs in the window Time (symbolized by the letter T). It places the weeks of the month, considering that it begins Sunday and ends on a Saturday, for example, for the weeks of February are: (Figure 23)

Figure 18. Air-Sea interface Data in the IRI Data Library. The data belongs to the NOAA / NCEP / EMC global CMB, Reynolds Smith research center. It should be looked for weekly data by entering to version 2 of the Reynolds data (Reyn Smith IOv2). (Figure 19)

Figure 23

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You get 03 sets of data (03 weeks); therefore you should perform filtering of information with an average time (called T in CPT), which shows the average of 03 weeks elapsed, corresponding in this example to February. From this part, there are two ways to standardize the resolution between the two data sets from different research centers, which are detailed below: First way: (Figure 24)

Figure 26 Then, enter on page 2 of the same file and save as text file (which is already transformed). (Figure 27)

Figure 24 Download the monthly average data.

Figure 27 The format obtained is the following: (Figure 28)

Note: Keep the following in mind before the process: the resolution of the Reynolds data is 1° x 1° and is not compatible with the data of CPT when it runs (Source: NOAA / NDCD / ERSST whose resolution is 2° x 2°). To solve this incompatibility a spreadsheet has been compiled (called transformation) which converts the Reynolds data to ERSST data. The chart below shows the format obtained through the process described above, where the first line and first column indicate resolutions in longitude and latitude respectively (1° x 1°). (Figure 25) Figure 28 Save it to add it to the February predictor history, with a single copy. (Figure 29)

Figure 25 Copy from the second line all the obtained information in the file and take it to the spreadsheet 1 of the file TRANSFORMATION, on the yellow background area (copy and paste), leaving the first row empty. (Figure 26).

PASTED FILE (Weekly averages in the interest month)

Figure 29 Then record it.

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Note that will have information, it starts in one year 1960 and ends with the year 2008, which is ready for inclusion on the CPT. (Figure 30)

Delete date dashes by blank spaces

Look for resolution changes in first row and column: 2º x 2º. Delete date dashes by blank spaces

Figure 30 Figure 32

Second way: Login to expert mode after obtaining the average week (found with a resolution of 1° x 1°), perform the following commands. X 0 2 358 GRID Y -88 2 88 GRID Then download it, save it and paste it on the base obtained from the initial historical series (procedure described in item 2.3.1.1), which is ready for use as a predictor.

2.2.1.2 Procedure for obtaining the wind variable at altitude, geo-potential and temperature at mandatory levels If is the case to work with an atmospheric variable at altitude, there is a practical procedure to work with averages of days elapsed. An option is to use information NOAA NCEP-NCAR CDAS-1 which lies within the model simulations (HISTORICAL MODEL SIMULATIONS). (Figure 31 and 32).

Figure 33 Choose the daily data (DAILY) and subsequently the INTRINSIC mode. (Figure 34)

Figure 34 Figure 31 It is necessary to previously have monthly historical data of the variable of interest from the same research center (NOAA NCEP-NCAR CDAS-1) so they have the same resolution in order to fit together more easily. (Figure 33)

When information regarding altitude level is required, choose the Pressure Level option, which lets you choose the level of interest. Variables that can be provided are multiple; however, the most common are: geo-potential height, zonal wind, south wind and temperature. They can be seen in the following screen (Figure 35)

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Figure 35 The available levels in the library are 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20 and 10 mb. (Figure 36)

Place the maximum number of modes for the variable X, which corresponds to fewest number obtained between the number of years in historical series and the number of grid points or seasons. The maximum number of variable X is required, so you take 43 (according to the example), although in reality the maximum number for X is 44 (n final initial n + 1), only that the ordered common pair 1965-2007 is considered so there is an additional process for calculating the weight of 2008, which is described below (Figure 38).

Figure 38 You should obtain from the menu FILE/OPEN FORECAST, and then place the start year and number of end years (including 2008). (Figures 39 - 40) Figure 36 Perform the filtering with T (average time due to the availability of 22 series, one for each day) and then proceed to download data. Note: for these variables it is not necessary to change the resolution, or use the TRANSFORMATION spreadsheet. (Figure 37)

Figure 39

Figure 37

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C Display the forecasts through the menu: FILE/FORECAST/ SERIES (Figures 41-42)

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In the CPT, the forecasting process for the year of interest is the same as is performed with individual predictors, by placing the X EOF option in MATRIZ VARIANZA–COVARIANZA (MATRIX VARIANCE – COVARIANCE) to preserve the relative importance of the EOF (Figures 45-46-47).

Figure 41

Figure 45

Figure 42 And you obtain the file under the following format: (Figure 43)

Figure 46

Figure 43 Continue with the same procedure for second or more variables (or the second area as the case may be) and then group into a single file, which will act as a predictor for the variable under discussion (Figure 44).

Figure 47

2.4 DECISION CRITERIA FOR MANAGING THE CPT RESULTS 1. One of the first indicators to be displayed is the GOODNESS INDEX which is the result of the first interaction between the predictors and predicting variables; this is the first condition to follow. If we obtain a negative value, it indicates no correlation or linearity between the information from both variables; for this we must find a better area. Preferably, this value should be positive and higher (tendency to have a value of 1). (Figure 48)

Figure 44

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First run of the CPT program, where the index is located.

Figure 50 4. Check the canonical correlation coefficient, which is the degree of relationship between the predictor and predictant variables (jointly). (Figure 51)

Figure 48. Display in GOODNESS INDEX

2. One of the most important criteria to be considered in the forecasts is the definition of the work period to be used, which is defined by two things: • The “LENGTH OF TRAINING” PERIOD and, • The “FIRST YEAR OF X TRAINING” PERIOD. 3. In the definition of the climatological period to work on, usually the program defines by default the start and end years of the historical series (in many cases exceeds 30 years). When considering different periods, there will be different results. The climatologic reference period considered was 19712000; many researchers considered the normal since the start of the historical series until the year preceding the forecast. The change can be accomplished through the following steps: Enter the CUSTOMIZE menu (configuration), then “Climatological Period”. (Figure 49)

Figure 51 5. Only if the previous step is satisfactory, proceed to evaluate the statistical indicators through individual assessment by station, considering the following route: TOOL/VALIDATION/CROSS VALIDATED/ PERFORMANCE MEASURES / The analysis is done station by station; at this stage you can not see the stations that exceed the permissible limit of missing data (% MISSING VALUES) (Figure 52) First, display the chart and compare the red line (observed values) and the green lines (forecast values); highlight if the curves follow the same characteristic pattern, i.e. if a curve rises, the other has to rise and vice versa.

Figure 49

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The second display is done in the ROC graph (Relative Operating Characteristic) where you can see the curves that are found above the diagonal. If the curve is red, it refers to the predictions made by the model to the “below normal”

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7. The other values refer to categorical measurements, i.e. the level of accuracy of the model with historical data. Hit Score: The percentage of hits of the model relative to total predictions made from historical series. Good forecast

The optimum is to have a value close to 100% which would indicate a perfect model.

First Step

Hit Skill Score: Is the indicator for evaluating the skill of the model, the percentage of times the result corresponds to a coincidence. The optimum is to have a value close to ±100% which would indicate a perfect model.

Bad area

LEPS Score (Linear Error in Probability Space), That calculates a defined result using a table that shows different results of accuracy, depending on the category observed and the previous probabilities of the categories. The probability distribution is transformed to a cumulative probability function. (Figure 54)

Figure 52 category; if it is blue, it refers to the predictions made by the model in the “above normal” category. It is appropriate that the two curves be above the diagonal and approaching the upper left corner. 6. Second step, although the statistical indicators are a technical reference, you should fully understand their meanings. The first coefficient of Pearson’s1 and the one of Spearman2 indicate the degree of association that the observed values have with forecast values, and should be approximately 1; the higher these values are, the more favorable the results will be (not good to get values close to -1). (Figure 53)

Figure 54 Gerrity score: Calculates a definite result by using a result table alternative to that used for LEPS results. (Figure 55) Figure 53 The mean squared error and root mean squared error have the same meaning: they represent the sum of deviations between observed and forecast values, i.e. the error that exists so that predicted values to try to reach the observed value. In a practical way, if the observed and predicted values are similar or nearly identical, means that the error will be zero or nearly zero, hence also its square root. It should be considered that this indicator is very relative: it is not the same to find a difference between both values (observed and predicted) in a rainy area than in a dry area, for example:

Forecast Precipitation 430 mm / month 10 mm / month

Observed Error Precipitation 380 mm / month 50 mm 0.0 mm / month 10 mm

Observed Error Precipitation 380 mm / month 50 mm 0.0 mm / month 10 mm

Observations Wet zone Dry Zone

Observations Wet zone Dry Zone Figure 55 1. Randall E et al. A beginner’s guide to structural equation modeling pg. 38. 2. William H. Press. Numerical recipes: the art of scientific computing pg. 349.

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ROC area (below-normal): Represents the value of the area below the red curve. Defines the area below the ROC curve for forecasts of the below normal category; shows the proportion of times that below normal conditions can be successfully distinguished over other categories. A maximum value and optimal in the model should be 1 (meaning 100%). ROC area (above-normal): Represents the value of the area under the blue curve. Defines the area below the ROC curve for forecasts of the above normal category and shows the proportion of times above normal conditions can be successfully distinguished over other categories. A maximum value and optimal in the model should be 1 (meaning 100%).

Scheme N째 02. - Process Assessment and Decision Making of the results obtained by the CPT

Run of CPT

no Goodness index -1

Scheme N째01.- Previous processes for the run of the Climate Predictability Tool (CPT)

yes

Joint Evaluation CCA-1/ high?

Entry formats of the CPT: Quarterly, Bimonthly, Monthly

no

yes Determination of new variable and/or predictor area

Is the predictor updated?

Evaluation of models for each station

NO

Initiate alternative process of variable update

YES Ready data Start year, missing data. If the variable = PP-Y bound=0 Number of years = total of the serie

Configuration of the CTP

Number of modes: X=10 Y=10 CCA=10

Graphic evaluation. Observations vs Similar forecasts?

no

no

yes

Graphic evaluation. ROC: above the diagonal?

no

yes

Evaluation of Good Categorical measurements

no

yes

Use for individual forecast

Nota: The symbol approach

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If the requirements of Schedules 1 and 2 are met, we are able to use the model to forecast the year preceding each station (individually) that met all these requirements. For this, we carry it out through the menu: (Figure 56)

Figure 56 Place the year to be forecast: 2008 (first year of data in file). (Figure 57)

Figure 59 probabilistic values of an above normal condition (superior). (Figure 60)

ABOVE

50% Limit

Figure 57 In the menu: TOOL/FORECAST/MAPS/PROBABILITIES.

BELOW

Figure 60 If you have values below 50% in category B (below normal) and A (above normal), they are considered normal, as for example, a probability of 25 - 30 - 45, for the CPT to be considered very close to the upper limit but within the “Normal” category. It is noteworthy that many researchers find no significant differences between the values of 25-30-45, considered as either 03 possible cases.

2.6 FREQUENTLY ASKED QUESTIONS RELATED TO CPT HANDLING Figura 58 In the probabilistic outcomes only the seasons that met all as indicated in Figures 1 and 2 should be considered. The rest of the values will not be considered for making the forecast table and will be determined with other indexes. (Figure 59)

1. What do I do if one of the requirements of Schedule 1 and 2 is not met? In that case you should discard the values of the station; therefore, it is not considered in the final results.

2.5 CONSIDERATIONS FOR THE INTERPRETATION OF TERCILES

2. How do I consider in the event that the CCC is favorable and in the individual analysis by stations only a few are favorable?

The CPT considers among its results by categories values above 50% as extreme (superior and inferior). The value of normal condition is the same as saying the likelihood of the climatology. For example, the following graph shows

In that case, only those that are both favorable to the canonical correlation coefficient (CCC) and individual station statistical indicators will be considered in the eventual outcome. INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN

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3. How do I consider if the Pearson and Spearman coefficients are high but negative? They are not considered in the analysis. The values by station are discarded and not considered in the final grouping of forecasts. 4. How do I obtain the limits of the climatology values? There are two ways to get the climatology: The first comes from the same original data (data format of the CPT entry corresponding to the variable you want to predict= Y); you should add to each column the percentile values 33 and 66, which corresponds to the limits of the terciles. This value is variable depending on whether the limits of the probabilities are changed. The second way is provided by the CPT program, with the command TOOL/FORECAST/SERIES/ top part climate where the period assumed in the calculation is shown (Figure 61).

Period of the climatology Low climatology level, in values. High climatology level, in values. Low climatology level, in probabilities. High climatology level, in probabilities.

Figure 61 5. Does the CPT provide deterministic values in its forecasts? The CPT has the advantage of performing multiple operations; therefore, it provides multiple results: one is estimating the values of quantitative forecasts under a given confidence level (by default the program calculates with a 68.3% confidence level).

Example: Year of the forecast:2008 Value of the forecast: 4.527ºC Confidence Interval: Low 2.980ºC

High 6.075ºC

Figure 62 This means that the interval between 12.5 and 30.2 has a 0.95 probability of containing m. We can also say that if the procedure for calculating the confidence interval of 95% is used many times, 95% of the time the interval will contain the parameter. Interpretation 2 It is called confidence interval in Statistics to an interval of values around a sample parameter where, with a determined probability or confidence level, the population parameter to be estimated will be situated. If α is the random error that you want to assign, the probability will be 1 - α. At a lower level of confidence, the interval will be more precise, but it will commit a greater error. To understand the following formulas, it is necessary to understand the concepts of parameter variability, error, confidence level, critical value and α value. A confidence interval is thus an expression of the type [θ1, θ2] or θ1 ≤ θ ≤ θ2, where θ is the parameter to be estimated. This interval contains the estimated parameter with a given certainty or confidence level 1-α. Upon providing a confidence interval, it is assumed that population data are distributed in a certain way. It is customary to do so by normal distribution. The construction of confidence intervals is performed using the Chebyshev inequality. (Figure 63)

This can be displayed after enabling the forecasting by series, following the completion of the run: TOOL/FORECAST/SERIES / (Figure 62) 6. What is meant by confidence intervals? Interpretation 1 A confidence interval is a range of values that has a given probability of containing the parameter being estimated. The 95% and 99% confidence intervals, which have a 0.95 and 0.99 probability of containing the parameter, respectively, are the most used. If the parameter being estimated were m, The confidence interval of 95% will be:

Figure 63

12.5 ≤ m ≤ 30.2

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9. When do we consider a forecast with uncertain results? Uncertainty is an expression of lack of knowledge of a future condition.

This point is the number such that: P[x ≥ X ] – P[z ≥ X ] – α/2 α/2 α/2 And in the standardized version: Z-

= -Zα/2 α/2

Así: P

[

≤ –Z α/2

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=1–α

Performing operations it is possible to clear μ to get the range: σ σ P x–Z =1–α α/2 √ n ≤ μ ≤ x + Zα/2 √n

[

]

Result is the confidence interval: ( x–Z

σ α/2

√n

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σ √n

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If σ is not known and n is large (i.e. ≥ 30): ( x–Z α/2

s √n

,x+Z α/2

s √n

)

s is the standard deviation of a sample. Approximations for the value Zα/2 for standard confidence levels are 1.96 for 1 − α = 95% and 2,576 for 1 − α = 99%. 7. Where can I change the confidence level of my forecasts? Once the CPT is running, proceed to the following route: (Figure 64) CUSTOMIZE/FORECAST SETTING/

It can result from an absence of information or even because there is disagreement on what is known or what could be known. It can have multiple types of sources, from quantifiable errors in the data to ambiguously defined terminology or uncertain projections of interpretation. Uncertainty can therefore be represented by quantitative measures (i.e., a range of values calculated by various models) or by qualitative statements (i.e. reflecting the opinion of a group of experts). Within the CPT all results that have an obtained value correspond to the forecaster’s discretion. 10. How does the CPT consider a probabilistic outcome of 30%-40%-30%? As explained in the previous question, these values are considered as uncertainties, i.e., any of the categories or conditions can occur under these conditions. 11. What is the cause of obtaining results with uncertainty? It could be many causes, including: Bad decision-making in the predictors used; which physically explains the variability on the prediction (value to be predicted). The CPT is based on the premise of the existence of a linear relationship between predictors and predicting, which doesn’t always exist, which can be a cause of uncertainty. The predictors are not defined because they are in a phase of changing astronomical station. The poor quality of the information. In many cases the information from weather stations has fractures in the historical series because of significant changes in their location. Statistically speaking this means that we have practically two different series that have been grouped for the run process with the CPT. (Figure 65)

Figure 64 8. How does the CPT consider probabilistic outcome 50% -10% -40% or 50% -0% -50%? It is an ambiguity in which any of the scenarios is both possible and not feasible, so it will only be regarded as uncertain. The CPT considers it with the average or normal value (normal category), but physically is not acceptable.

Red Line: 10 followed years off

Figure 65 The data series have many gaps; missing data also plays an important role in generating forecasts. The CPT program replaces the missing data values by mean values, medians, nearest station and at random.

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The modes are not adequate. Each mode carries a part of the variance to be explained from primary data (self values). Sometimes the number of adequate modes is not sufficient (usually within the top 5 modes is the explanation of a large percentage of the total variance). However, sometimes it is necessary to increase the number of modes to a recommended number of 10 (optional), with which the results are improved. Rainfall in the countries near the equator is influenced by several simultaneous changes affecting precipitation and temperature variables. This requires working simultaneously with multiple predictors (or different areas with only a single predictor). 12. How do we consider two opposite results obtained from two different predictive variables? First verify if both have high CCC, and if they statistically acceptable; if both are correct, it is advisable to make an assembly with the predictors jointly, which we will have a result containing the two involved parameters in the variable to be predicted. Otherwise take the information from the higher CCC value.

13. How to perform simultaneous testing with two or more predictors? The CPT is designed to take only one field of predictors at a time, but it is possible to obtain software in order to produce results with multiple fields. Run the software using one of the fields of predictors, and with the number of X EOF modes at maximum (this will be the minimum number of grid points and the length of test period). Then proceed to record the scores of principal components using Data Output. Repeat procedure for other predictor fields. Then we proceed to combine multiple output files of the principal components scores so that the main components for all the predictor fields are in a file. CPT then can be run with this new file, as the variables read as a non-reference data set. Place in the X EOF the option “covariance matrix� to maintain the relative importance of EOFS. Although it will not be possible to see the maps for the merged fields, all validation results and forecasts will be as if the software would have been controlled with multiple input fields. Some of the seasonal forecasts in the countries are shown in Figure 66.

Figure 66

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CHAPTER III Implementation of numerical models for climate prediction


CHAPTER III

Angel Mu単oz a.munoz@ciifen-int.org

3.1 STEP BY STEP PROCEDURES FOR INSTALLATION AND IMPLEMENTATION OF MM5 AND WRF MODELS IN CLIMATE MODE 3.1.1 Operating System The installation process (with images step-by-step) and the implementation of Scientific Linux, Rocks Cluster and Configuration and installation of a computer node is available at: Scientific Linux: http://mediawiki.cmc.org.ve/index.php/ imagen:Scilinux00.png Rocks cluster and computer nod: http://mediawiki.cmc. org.ve/index.php/%E2%97%A6_Rocks_Cluster

3.1.2 Atmospheric Models The atmospheric models considered in the Project are the fifth generation of the Mesoscale Model (MM5) and the Weather and Research Forecast model (WRF). The following pages show their installation and configuration. The same models, with appropriate modifications, are set as climate versions. These versions have been called CMM5 and CWRF. The MM5 model is divided into multiple modules and subprograms. Figure No. 66 presents a schematic diagram of MM5. The same way, in Figure No. 67 presents a diagram of the WRF model.

Fig. 68 WRF2 System common to request a library libstdc + +. It is necessary to download (for example from pbone.net) and install it with a simple rpm. 2. Download and install NCAR www.ucar.edu Installation is simple. It is required to follow the Setup instructions. Note: It is suggested that you install it in: /usr/local/ncarg. 3. Download MM5 The packages needed are: TERRAIN, REGRID, LITTLE_R, INTERPF, MM5. ftp://ftp.ucar.edu/mesouser/MM5V3 4. Edit the /etc/bashrc The last lines should say:

Fig. 67 System Model MM51

export PATH=$PATH:/opt/intel/fc/9.1.036/bin:/usr/ local/ncarg/bin exportLD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/intel/ fc/9.1.036/lib:/usr/local/ncarg/lib export NCARG_RO OT=/usr/local/ncarg

1. Download and install Intel Fortran

The situation showed above corresponds to an example. It is necessary to set the paths to the correct directories of the compiler. To load the newly introduced environment variables, it is enough to write: source/etc/bashrc.

www.intel.com Note: There is a free non-commercial license. It is relatively

5. To verify that the process is correct, consider the following steps:

3.1.2.1 MM5

1. University Corporation for Atmospheric Research, Weather Research and Forecasting Model users`s guide. Chapter 1 http://www.mmm.ucar.edu//wrf/users/docs/user_guide_V3.1/ users_guide_chap1.htm 2. University Corporation for Atmospheric Research http://www.mmm.ucar.edu//wrf/users/docs/user_guide_V3.1/ users_guide_chap1.htm#WRF_Modeling_System

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5.1. IFC: write ifort-v (It should display the installed version). 5.2. NCAR: idt (It should open a graphical window) 6. Create a directory (e.g. /datos/MM5) and decompress TERRAIN:

TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTURAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES


C H > cd /datos > mkdir MM5 > tar -xvzf TERRAIN.TAR.gz

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(clearly this file MUST be in

this directory) 7. Verify if the libg2c library is installed If the libg2c library is not installed, proceed to install it now. If it has a different name, create the symbolic link. Note: This library can be downloaded online, it is also available in the gfortran. For instance, in Aquila@cmc.org.ve: Another way: you can download it from: http://www.cmc. org.ve/descargas/libg2c.so [root@Aquila TERRAIN]# find “*libg2c*” This search the library

/usr

¬name

/usr/local/matlab/sys/os/glnx86/libg2c.so.0 /usr/local/matlab/sys/os/glnx86/libg2c.so.0.0.0 /usr/lib/libg2c.so.0 /usr/lib/gcc/i386¬redhat¬linux/3.4.3/libg2c.so /usr/lib/gcc/i386¬redhat¬linux/3.4.3/libg2c.a /usr/lib/libg2c.a /usr/lib/libg2c.so.0.0.0 [root@Aquila TERRAIN]# ln -¬s /usr/lib/gcc/ i386¬redhat¬linux/3.4.3/libg2c.so /usr/lib/libg2c. so

Place it in /usr/lib and perform an additional symbolic link as follows: > ln -s /usr/lib/libg2c.so /usr/lib/libg2c.so.0

8. Edit the TERRAIN Makefile Find the line that corresponds to the intel compiler and modify the PATH to lg2c: > vi Makefile > /intel This finds the appearance of the word after the

slash. The paragraph should be as follows: intel: echo “Compiling for Linux using INTEL compiler” ( $(CD) src ; $(MAKE) all \ “RM = $(RM)” “RM_LIST = $(RM_LIST)” \ “LN = $(LN)” “MACH = SGI” \ “MAKE = $(MAKE)” “CPP = / lib/cpp” \ “CPPFLAGS = -I. C traditional D$(NCARGRAPHICS) “ \ “FC = ifort “ “FCFLAGS = -I. -w90-w95-convert big_endian “\ “LDOPTIONS = -i_dynamic” “CFLAGS = -I. “\ “LOCAL_LIBRARIES=-L$(NCARG_ROOT)/lib -L/usr/X11R6/ lib -lncarg -lncarg_gks-lncarg_c-lX11-L/usr/lib -lg2c” ) ; \ ( $(RM) terrain.exe ; $(LN) src/terrain.exe.) ;

> make intel > make terrain.deck

10. Download the necessary data for TERRAIN as follows and decompress it > cd /datos/MM5/DATOS > wget ftp://ftp.ucar.edu/mesouser/MM5V3/TERRAIN_ DATA/* > ls-1 > gunzip *.gz > tar-xvf archivo.TAR

10.1. Modify terrain.deck.intel > vi terrain.deck.intel

And modify: > > > >

set ftpdata = false Set the following for ftp ’ in g30 sec elevation data from USGS ftp site set Where30sTer = /mnt/data/terrain_data

The result should be as follows: #set ftpdata =true set ftpdata = false #set Where30sTer = ftp set Where30sTer = /datos/MM5data/DATOS

Then proceed to link: > ln -s /datos/MM5data/DATOS/* TERRAIN/Data/

11. Compile TERRAIN again and run > make terrain.deck > ./terrain.deck.intel

Note: This compiles the code again. When finished, log into terrain.print.out and make sure the two last lines show: > tail 2 terrain.print.out

If the process is correct, at the end of the run this should appear: == NORMAL TERMINATION OF TERRAIN PROGRAM == 99999

Then write idt TER.PLT

12. Create a download folder for “TERRAIN DATA” Download from there as needed. $cd $LOQUESEA/mm5 $mkdir DATOSls $ cd DATOS $ wget ftp://ftp.ucar.edu/mesouser/MM5V3/TERRAIN_ DATA/* INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN

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$ for x in ‘ls 1 *.gz‘; do gunzip $x; done

$ make plotfmt

13. Unzip REGRID

If there are no errors:

In /datos/DatAquila/Meteo/mm5 and compile

$./plotfmt ../ON84:1993¬03¬14_00 $ idt gmeta

$ make intel

Go to the regridder directory and:

Then download the data: NCEP_ON84.9303 in /datos/ Meteo/DatAquila/mm5/DATOS, which is an input file for pregrid.

$ ./regridder

wget c –passiveftp ftp://ftp.ucar.edu/mesouser/ MM5V3/TESTDATA/NCEP_ON84.9303

17. If everything is correct, in the last step the following file will be created: REGRID_DOMAIN1 18. For LITTLE_R first proceed to decompress it

14. Log into pregrid folder

$ tar xvzf LITTLE_R.TAR.gz

Edit pregrid.csh the lines that follow

(The file created should be placed in /datos/DatAquila/ Meteo/mm5)

set DataDir =/datos/DatAquila/Meteo/mm5/DATOS

15. Run pregrid.csh

19. Log into the Makefile of LITTLE_R (in the intel options)

$ ./pregrid.csh

-L/usr/lib/gcclib/i386redhatlinux/3.3.2

It should read:

To

********** Normal termination of program PREGRID_ON84 ********** mv SNOW:19930313_00 ../ON84_SNOW:19930313_00 mv SNOW:19930313_12 ../ON84_SNOW:19930313_12 mv SNOW:19930314_00 ../ON84_SNOW:19930314_00

Now cd on84/..

/datos/DatAquila/Meteo/mm5/REGRID/pregrid/

If the process is right, this should appear in the pregrid directory (the result of ls-l): Doc/ nise/ ON84_SNOW:19930313_00 pregrid.csh* era/ nnrp/ ON84_SNOW:19930313_12 pregrid_era40_int. csh* grib.misc/ on84/ ON84_SNOW:19930314_00 pregrid. namelist Makefile* ON84:19930313_00 ON84_SST:19930313_00 README_ERA40 navysst/ ON84:19930313_12 ON84_SST:19930313_12 toga/ ncep.grib/ ON84:19930314_00 ON84_SST:19930314_00 util/

16. Find in the pregrid directory the useful directory; there should be a file called plotfmt. To compile the file, you should make the following changes to the Makefile: NCARG_LIBS= ?L$ (NCARG_ROOT) /lib \ ?lncarg ?lncarg_gks ?lncarg_c \ ?L/usr/X11R6/lib ?lX11 ?lm \ ?L/opt/intel/fc/9.1.036/lib ?L/usr/lib ?lg2c

Then

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-L/usr/lib lg2c.

It should read as follows: “LOCAL_LIBRARIES= -L$(NCARG_ROOT)/lib -L/usr/ X11R6/lib -lncarg -lncarg_gks -lncarg_c -lX11 -L/usr/lib - lg2c” >> macros_ little_r ; \ ( $(CD) src ; $(MAKE) $(PROGS) )

Note: Where the above was now reads -L/usr/lib -lg2c. 20. Download test data for LITTLE_R. wget c –passiveftp ftp://ftp.ucar.edu/mesouser/ MM5V3/TESTDATA/input2little_r.tar

Proceed to place it in: /datos/DatAquila/Meteo/mm5/ DATOS then decompress the files as follows: $ tar xvf input2little_r.tar

and the following files should be obtained: (ls-l) Test_data Test_data/REGRID_DOMAIN1.gz Test_data/surface_obs_r:19930313_21.gz Test_data/obs13_00.gz Test_data/obs14_00.gz Test_data/obs13_06.gz Test_data/surface_obs_r:19930313_18.gz Test_data/surface_obs_r:19930313_15.gz Test_data/surface_obs_r:19930313_12.gz Test_data/obs13_18.gz Test_data/obs13_12.gz Test_data/surface_obs_r:19930313_09.gz Test_data/surface_obs_r:19930313_06.gz Test_data/surface_obs_r:19930313_00.gz

TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTURAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES


C H Test_data/surface_obs_r:19930314_00.gz Test_data/surface_obs_r:19930313_03.gz

Place the data that you created in the folder TEST_ and write:

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Note: for each data it is necessary to modify namelist. input. 23. OPTIONAL: Install RAWINS

$ gunzip *.gz

The installation will not be explained in this guide.

It will get the following files:

24. Install INTERPF

obs13_00 obs14_00 obs13_12 obs13_06 obs13_18 REGRID_DOMAIN1 surface_obs_r:19930313_06 surface_obs_r:19930313_18 surface_obs_r:19930313_09 surface_obs_r:19930313_21 surface_obs_r:19930313_00 surface_obs_r:19930313_12 surface_obs_r:19930314_00 surface_obs_r:19930313_03 surface_obs_r:19930313_15

INTERPF is responsible for doing pressure interpolations.

All these files will be located in: /datos/DatAquila/Meteo/ mm5/DATOS/Test_data 21. Modify namelist.input The result should be: &record2 fg_filename = ‘../REGRID/regridder/REGRID_ DOMAIN1’ obs_filename= ‘/datos/DatAquila/Meteo/mm5/ DATOS/Test_data/obs13_00’ ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/ obs13_12’ ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/ obs14_00’ sfc_obs_filename= ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/surface_obs_r:19930313_00’ ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/surface_obs_r:19930313_03’ ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/surface_obs_r:19930313_06’ ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/surface_obs_r:19930313_09’ ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/surface_obs_r:19930313_12’ ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/surface_obs_r:19930313_15’ ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/surface_obs_r:19930313_18’ ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/surface_obs_r:19930313_21’ ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/surface_obs_r:19930314_00’ /

22. Run the test: $ ./little_r

Go to the MM5 directory and write (in this case the tar.gz is in the directory immediately above). $ tar xvzf ../INTERPF.TAR.gz

25. Now simply $ cd INTERPF $ make intel $ ./interpf

The above should create the files: MMINPUT_DOMAIN1, LOWBDY_DOMAIN1 y BDYOUT_DOMAIN1

That will be used by MM5. 26. MM5:

It should begin by decontaining and decompressing: Go to the MM5 directory and $ tar xvzf ../MM5.TAR.gz

Now go to the Run directory (which is within the MM5) and perform the following symbolic links: $ $ $ $

ln ln ln ln

s s s s

../../INTERPF/MMINPUT_DOMAIN1 . ../../INTERPF/BDYOUT_DOMAIN1 . ../../INTERPF/LOWBDY_DOMAIN1 . ../../TERRAIN/TERRAIN_DOMAIN2 .

27. Go back to the MM5 directory And edit the section corresponding to 3I2 (INTEL with Intel Fortran Compiler) of configure.user. The result should be shown as follows: # # 3i2. PC_INTEL (LINUX/INTEL) # RUNTIME_SYSTEM = “linux” FC = ifort FCFLAGS = I$(LIBINCLUDE) O2 tp p6 pc 32 convert big_endian CPP = /lib/cpp CFLAGS = O CPPFLAGS = I$(LIBINCLUDE) LDOPTIONS = O2 tp p6 pc 32 convert big_endian LOCAL_LIBRARIES = MAKE = make i R

After a few minutes a pair of files will be created. In particular, LITTLE_R_DOMAIN1 is necessary to run MM5.

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28. Compile and run: $ make $ make mm5.deck $ ./mm5.deck

If the process is successful this should be displayed: Make [1]: Leaving directory `/datos/DatAquila/Meteo/mm5/MM5/Run’ This version of mm5.deck stops after creating namelist file mmlif. Please run code manually. vie mar 30 17:39:26 VET 2007

OROSHAW = 0, ;include effect of orography shadowing; ONLY has an effect if LEVSLP is also set; 0=no effect (default); 1=orography shadowing taken into account - NOT AVAILABLE FOR MPI RUNS. IMOIAV = 1, 1, Schematic of variable humidity. Depending on the case, for weather, select 1 or 2; 0 - not used; 1 - is used without additional data; 2 - used with data from additional moisture. OROSHAW controls whether or not to include shadow effects due to orography in executions. Obviously, it is more physical, and costs more. If you wish to activate it , set LEVSLP, indicating the nest (1 = father, 2 = child, 3 = grandson, etc.) from which OROSHAW begins to be used.

Now: 4.- Initial conditions: $ cd Run $ ./mm5.exe

An important aspect corresponds to the way of the analysis data is assimilated for the initial conditions. This is performed as follows:

1.- Settings: In configure.user (/datos/CMM5/MM5/configure. user) all the information about the settings can be found (Section 6 of the file). In Section 5 of that file the parameters have to be considered carefully: MAXNES = N (Here, the maximum number of domains to run in mm5.exe should be set). MIX,MJX is the pre-dimensioning that is done for the arrays along the north-south and east-west axes. If a domain has been created in which the dimensions north-south or east-west exceed these parameters, you must increase MIX and MJX. IMPORTANT: each time you change the configure.user should type: make clean; make (for the changes to take effect). 2.- On the other hand there is the mm5.deck (/datos/ CMM5/MM5/mm5.deck). Most important aspects to consider: TIMAX = NNN (Total number of minutes that the forecast will last: NNN minutes to the future). TISTEP = (is the delta T, in seconds the temporal integration step. If CFL violations occur, this step should be decreased, and is linked to the spatial resolution chosen. The recommendation is to use a little less than 3 times the distance between the points assumed in TERRAIN for the thicker domain-the one with lower resolution). 3.- Other important options: RADFRQ = 30. (Indicates how often atmospheric radiation subroutines are calculated in minutes. This value is appropriate in order to start). LEVSLP = 9, ;nest level (correspond to LEVIDN) at which solar radiation needs to be taken into account for orography; set the large to switch off; only have an effect for very high resolution model domains.

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IBOUDY = 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, ;boundary conditions ; (fixed, time-dependent, relaxation -0,2,3)

IIf the domain is too large (all of Brazil, all of South America, etc ...) you should use a relaxed scheme of the boundary conditions (for references see the Online MM5 Manual or refer to Davies & Turner, Quart. J. Roy. Meteor. Soc, 103, 225-245 (1977)). For the remainder ones the time-dependent scheme can be used. 5.- The TSM item variable throughout the execution is also important. It should be turned it in on in the following option: ISSTVAR= 1,

6.- This might be useful: IFSNOW = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ;SNOW COVER EFFECTS - 0, 1, 2 ; ;0 - no effect, 1 - with effect, 2 - simple snow model

7.- Now proceed to seek this section: NEST AND MOVING NEST OPTIONS LEVIDN = 0,1,2,1,1,1,1,1,1,1, “NESTED” LEVEL NUMNC = 1,1,1,3,1,1,1,1,1,1, IDENT. MOTHER DOMAIN NESTIX = 39, 13, 19, 46, 46, 46, 46, 46, 46, 46,NORTH-SOUTH SIZE NESTJX = 45, 22, 13, 61, 61, 61, 61, 61, 61, 61,EAST-WEST SIZE NESTI = 1, 20, 18, 1, 1, 1, 1, 1, 1, 1, ORIGIN IN NORTH-SOUTH NESTJ = 1, 13, 9, 1, 1, 1, 1, 1, 1, 1, ORIGIN IN EAST-WEST XSTNES = 0., 0.,900., 0., 0., 0., 0., 0., 0., 0., MINUTE THIS DOMAIN IS INITIALIZED XENNES =259920.,259920.,1440.,720.,720.,720.,720.,7 20.,720., MINUTE THE CORRESPONDING EXECUTION ENDS.

TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTURAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES


C H It is necessary to proceed to adjust each requirement that is requested, in accordance with the provisions in terrain. namelist

And just below, put the options as follows: IOVERW = 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, ; overwrite nest input ; 0=interpolate from coarse mesh (for nest domains); ; 1=read in domain initial conditions ; 2=read in nest terrain file

3.1.2.2 CMM5 1.- TERRAIN Check that terrain. namelist registers as “NSTTYP” 1 for the first domain and 2 for others who are using it. This ensures two-way feedback in the mesh. 2.- PREGRID (inside REGRID): /datos/CMM5/REGRID/ pregrid/pregrid.csh The first is “decontain” (spread) the files to work on. For example, tar tar tar tar

-xvf -xvf -xvf -xvf

archivo.pgb.f00.tar archivo.grb2d.tar A##### A#####

After this step, the following changes occur (this illustrates just one example, it should be adjusted according to the needs of the users): set DataDir = /datos/2005/1ero

Here is PATH where the data is. set InFiles = ( ${DataDir}/pgb.f00####*)

Instead of ### place the beginning of the numbers of the year in question. Ex: pgb.f000506*. set SRC3D = GRIB # Many GRIB-format datasets set SRCSST = $SRC3D set InSST = (${DataDir}/grb2d0506*)

As before. Indicate the beginning of the files to be used. The * takes all related. In this section, adjust the dates: START_YEAR START_MONTH START_DAY START_HOUR

= = = =

2005 06 01 06

# # # #

Year (Four digits) Month ( 01 - 12 ) Day ( 01 - 31 ) Hour ( 00 - 23 )

Note: It should begin in 06 END_YEAR END_MONTH END_DAY END_HOUR

= = = =

2005 06 30 18

# # # #

Year (Four digits) Month ( 01 - 12 ) Day ( 01 - 31 ) Hour ( 00 - 23 )

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Define the time interval to process. INTERVAL = 21600 # Time interval (seconds) to process. # This is most sanely the same as the time interval for # which the analyses were archived, but you can really # set this to just about anything, and pregrid will

The (INTERVAL) step usually takes 6 hours. You can check the directory listing directly. Finally: set set set set

VT3D = ( grib.misc/Vtable.NNRP3D ) VTSST = ( grib.misc/Vtable.NNRPSST ) VTSNOW = ( grib.misc/Vtable.xxxxSNOW ) VTSOIL = ( grib.misc/Vtable.xxxxSOIL )

3.- REGRIDDER (inside REGRID): /datos/CMM5/REGRID/regridder/namelist.input As previously discussed, if you carried out the exercises described, you should proceed to set dates in a basic way. And the ptop_in_Pa, which must match with the first_guess. If the installation process was followed correctly, the program should work without changes. REMEMBER: Regridder is run once per domain 4.- INTERPF (In /datos/CMM5/INTERPF/namelist.input): The first two lines must show the following: &record0 input_file= ‘../REGRID/regridder/REGRID_DOMAIN1’ /

Here, later you vary the domains, an interpf run for each. The following section may vary for some cases. &record3 p0 = 1.e5 ! base state sea-level pres (Pa) tlp = 50. ! base state lapse rate d(T)/d(ln P) ts0 = 275. ! base state sea-level temp (K) tiso = 0./ ! base state isothermal stratospheric temp (K)

This corresponds to the definition of the base state from which MM5 defines number of other variables/parameters. Detailed explanation with the equations can be found at: www.mmm.ucar.edu/mm5/documents/MM5_tut_Web_ notes/INTERPF/interpf.htm

3.1.2.3 WRF 1.- Downloads: www.mmm.ucar.edu/wrf/src/WRFV2.2.1.TAR.gz (WRF as such). www.mmm.ucar.edu/wrf/src/WPSV2.2.1.TAR.gz (WPS, the preprocessor). Topography data: www.mmm.ucar.edu/wrf/src/wps_files/geog.tar.gz

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Additional necessary libraries: www.mmm.ucar.edu/wrf/src/wps_files/jasper-1.701.0.tar.gz www.mmm.ucar.edu/wrf/src/wps_files/libpng-1.2.12.tar.gz www.mmm.ucar.edu/wrf/src/wps_files/zlib-1.2.3.tar.gz

/usr/local/lib/libnetcdf.a /usr/local/lib/libnetcdf_c++.la /usr/local/lib/libnetcdf_c++.a /usr/local/lib/libnetcdf.la

2.- These files should be stored, for example, in a folder called tars in /. Start decompression. The first things are extra in this case:

5.- Proceed now to /datos and create the CWRF folder and unzip it:

ZLIB: ----cd /opt tar -xvzf /TARS/zlib-1.2.3.tar.gz cd zlib-1.2.3 ./configure make make install

6.- Locate the WRFV2 folder cd WRFV2

It is needed to add new lines to /etc/bashrc. These are described below:

JASPER: ------cd /opt tar -xvzf /TARS/jasper-1.701.0.tar.gz cd jasper-1.701.0 ./configure make make install

export JASPERLIB=/opt/jasper-1.701.0 export JASPERINC=/opt/jasper-1.701.0 ulimit -s unlimited

To update the environment variables, proceed as usual: source /etc/bashrc

LIBPNG: -------

From now on, you may follow one of two options. The first is to set from zero WRF and the second is to download the configuration file. In the same directory you should write:

cd /opt tar -xvzf /TARS/libpng-1.2.12.tar.gz cd libpng-1.2.12 ./configure make make install

./Configure

The following appears:

3.- Now proceed with netcdf. To avoid confusion with the versions, we suggest downloading the version available on the server: www.cmc.org.ve/descargas/netcdf.tar.gz 3.1.- Place it in /TARS (or wherever you are placing tar containers). Unzip:

write

4.- Check that the last step is correct, it is crucial for WRF. An ls /usr/local/include/netcdf* debe mostrar: /usr/local/include/netcdfcpp.h /usr/local/include/netcdf.inc /usr/local/include/netcdf.h /usr/local/include/netcdf.mod /usr/local/include/netcdf.hh

Perform: ls /usr/local/lib/libnetcdf*

It should show:

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** WARNING: No path to NETCDF and environment variable NETCDF not set. ** would you like me to try to fix? [y]

Choose “y” and include the PATH: /usr/local/include /usr/local/lib

Every time that it asks. If the process is successful, a menu appears (at the beginning it indicates that it recognizes the paths to the JASPER library):

tar -xvzf netcdf.tar.gz cd netcdf-3.6.2 export FC=ifort ./configure make; make install

If the tar does not work with netcdf.tar.gz, netcdf.tar.Z

mkdir CWRF cd CWRF tar -xvzf /TARS/WRFV2.2.1.TAR.gz tar -xvzf /TARS/WPSV2.2.1.TAR.gz

Please select from among the following supported platforms. 1. PC Linux i486 i586 i686, PGI compiler (Singlethreaded, no nesting) 2. PC Linux i486 i586 i686, PGI compiler (single threaded, allows nesting using RSL without MPI) 3. PC Linux i486 i586 i686, PGI compiler SMParallel (OpenMP, no nesting) 4. PC Linux i486 i586 i686, PGI compiler SM-Parallel (OpenMP, allows nesting using RSL without MPI) 5. PC Linux i486 i586 i686, PGI compiler DMParallel (RSL, MPICH, Allows nesting) 6. PC Linux i486 i586 i686, PGI compiler DMParallel (RSL_LITE, MPICH, Allows nesting) 7. AMD x86_64 Intel xeon i686 ia32 Xeon Linux, ifort compiler (single-threaded, no nesting) 8. AMD x86_64 Intel xeon i686 ia32 Xeon Linux, ifort compiler (single threaded, allows nesting using RSL without MPI)

TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTURAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES


C H 9. AMD x86_64 Intel xeon i686 ia32 Xeon Linux, ifort compiler (OpenMP) 10. AMD x86_64 Intel xeon i686 ia32 Xeon Linux, ifort compiler SM-Parallel (OpenMP, allows nesting using RSL without MPI) 11. AMD x86_64 Intel xeon i686 ia32 Xeon Linux, ifort+icc compiler DM-Parallel (RSL, MPICH, allows nesting) 12. AMD x86_64 Intel xeon i686 ia32 Xeon Linux, ifort+gcc compiler DM-Parallel (RSL, MPICH, allows nesting) 13. PC Linux i486 i586 i686, g95 compiler (Singlethreaded, no nesting) 14. PC Linux i486 i586 i686, g95 compiler DMParallel (RSL_LITE, MPICH, Allows nesting) Enter selection [1-14] : 10

The selection must be “10”. If you desire to test WRF, select 7 (makes it impossible to create nesting) or 8 (with nests). 7.- Compiling the WRF. Once the above steps are done, proceed with the compiling: ./compile em_real > log.log

WRF is legendary for having a very long compilation. Wait at least 40 minutes. If you wish verify the state of compilation, perform a vi log.log in the same directory. Some notifications of compilation can be seen directly in the directory when you send it to do the job. These are important, particularly if there is an error, a screen will appear where we ordered the “compile em_real. 8.- Testing the WRF. If the process is successful, a way to do preliminary test is:

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ress (24 hours total) can be seen on the screen. This example performs downscaling time mode with two domains. If at the end we can see the message: COMPLETED SUCCESFULLY

Then it means the installation of the WRF is correct. 9.- WPS (WRF PREPROCESING SYSTEM) Proceed to compile the WPS. cd /datos/CWRF/WPS

You will be able to distinguish in these executable some directories similar to those used in the WRFV2 runs. First run: ./configure

After selecting the appropriate option, run: ./compile

The compilation is considerably shorter than the WRF. 10.- Domain Wizard. There is a multiplatform application (in Java) that can be used to perform WRF preprocessing. It is a kind of GUI for WPS. It can be downloaded at: http://wrfportal.org/domainwizard/WRFDomainWizard.zip You should place it either in the WPS or WRF and proceed to decompress: gunzip WRFDomainWizard.zip

Then proceed to run:

ls run

./run_DomainWizard

You should see some symlinks: nup.exe, ndown.exe and especially real.exe y wrf.exe. If they are highlighted in red, something in the process has failed. Then you proceed to perform an additional test: a short run of WRF.

It is important to remember and know precisely where each file is. We recommend creating, on WRF directory level (which contains the WPS and WRFV2), a directory called Domain (Dominios), where you can place the various domains that are created.

To do this, you need to download some test data in WRF intermediate format, available at: http://www.mmm.ucar.edu/wrf/src/data/jan00_wps.tar.gz

Hereafter it will be possible to perform a forecast run in mode time with WRF.

Another method is to do it directly into the terminal:

3.1.2.4 CWRF

cd test/em_real wget -c http://www.mmm.ucar.edu/wrf/src/data/jan00_ wps.tar.gz tar -xvzf jan00_wps.tar.gz

Conceptually, the climate mode configuration is similar to the CMM5.

The following is the initialization of WRF for testing. If the process is correct, these commands will not generate errors: cp namelist.input.jan00 namelist.input ./real.exe

1.- The first is to tell the WRF to update TSM throughout an execution. This will create even additional files that may be read on the way. Go to the WRFV2 directory and edit the namelist.input. The lines to be modified in each record are (if they do not exist, you must create them)

./wrf.exe

&time_control auxinput5_inname = “wrflowinp_d<domain>”, auxinput5_interval = 180, io_form_auxinput5 = 2

The process will take a few minutes. The execution prog-

&physics

When finished, run the WRF itself.

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sst_update = 1,

= 99, GFDL (Eta) scheme; adjust co2tf = 1

With this, the real.exe will write a wrflowinp_d## file type for each active domain (DO NOT change the <domain>) with TSM information. The interval is in minutes.

ra_sw_physics (max_dom) option longwave radiation = 0, without longwave radiation option = 1, Dudhia scheme = 2, Goddard short wave = 3, cam scheme also must set levsiz, paerlev, cam_ abs_dim1/2 (see below) = 99, GFDL (Eta) longwave (semi-supported) also must use co2tf = 1 for ARW

If the process is right, after running real.exe you will observe: wrfinput_d01 wrfbdy_d01 wrflowinp_d01

If there is only a single domain, those corresponding to other domains will appear. 2.- There is available documentation on the parameterizations of the WRF by J. Dudhia3. The changes to the physics of WRF (and CWRF) set out are shown below: REMEMBER: different settings can be placed between domains, but you should always check the compatibility between them. In some items the same settings are placed for all. This is not necessarily correct, but may be considered as an option: &physics mp_physics (max_dom) micro physics options = 0, without micro physics = 1, Kessler scheme = 2, Lin et al. scheme = 3, WSM 3-class simple ice scheme = 4, WSM 5-class scheme = 5, Ferrier (new Eta) micro physics = 6, scheme for graupel WSM 6-class = 8, Thompson et al. scheme = 98, scheme (to disappear) of simple ice NCEP 3-class = 99, scheme (to disappear) NCEP 5-class

The following are valid if mp_physics =! 0, to maintain Qv > = 0, and adjust the other fields of humidity to be less than or equal to a determined critical value.

mp_zero_out = 0, ; without adjustment of any humidity field = 1, ; except for Qv, all the other arrangements of humidity will be nulled = 2, ; Qv >=0, Every other arrangements of humidity will be nulled at certain limit. mp_zero_out_thresh = 1.e-8 ; Critical value, under the same, all the humidity arrangements, ; except Qv, will be nulled (kg/kg) ra_lw_physics (max_dom) option longwave radiation = 0, without longwave radiation = 1, rrtm scheme = 3, CAM scheme (adjust levsiz, paerlev, cam_abs_ dim1/2 below) 3. J. Michalakes, J. Dudhia et al. The weather Research and forecast model: Software architecture and performance. 11th ECMWF workshop on the Use of High Performance Computing in Meteorology, Reading U.K., 2004

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radt (max_dom) = 30, ; minutes between radiation physics calls recommend 1 min per km of dx (e.g. 10 for 10 km) nrads (max_dom) = FOR NMM: number of fundamental timesteps between calls to shortwave radiation; the value is set in Registry.NMM but is overridden by namelist value; radt will be computed from this. nradl (max_dom) = FOR NMM: number of fundamental timesteps between calls to longwave radiation; the value is set in Registry.NMM but is overridden by namelist value. co2tf CO2 transmission function flag only for GFDL radiation = 0, read CO2 function data from pre-generated file = 1, generate CO2 functions internally in the forecast ra_call_offset radiation call offset = 0 (no offset), =-1 (old offset) cam_abs_freq_s = 21600 CAM clearsky longwave absorption calculation frequency (recommended minimum value to speed scheme up) levsiz = 59 for CAM radiation input ozone levels paerlev = 29 for CAM radiation input aerosol levels cam_abs_dim1 = 4 for CAM absorption save array cam_abs_dim2 = e_vert for CAM 2nd absorption save array s f_sfclay_physics (max_dom) surface-layer option (old bl_sfclay_physics option) = 0, no surface-layer = 1, Monin-Obukhov scheme = 2, Monin-Obukhov (Janjic) scheme

3.1.3 Oceanographic Models 3.1.3.1 ROMS The Agrif version of Rom is easy to use, and is not very demanding in the amount of data to start the runs. Agrifer

TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTURAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES


C H version has the advantage of being easy to install. It is necessary to copy the files downloaded from Romstools page and to copy them to HDD. After running matlab from the /Roms/Romstools/Run folder, almost everything can be done with a significantly good level of feedback. As soon as the Rutgerts version is fully understood, some particular comparisons will be made. First you must go to ../Roms/Romstools/Run and modify via terminal using the preferred text editor, the romstools_ param.m file within it. The first thing to edit is: ROMS_title = ‘Pacifico’; % It is recommended to place the name of the area to be studied for better control of the ROMS_config = ‘CMC’; %. runs. A name for the type of configuration. (Later there will be other files that will have the same ROMS_title and ROMS_config). Then proceed to place the mesh dimensions of the area to be studied by placing the coordinates of the place: % Grid lonmin lonmax latmin latmax

dimensions: = -148; % = -75; % = -10; % = 10; %

% Minimum Maximum Minimum Maximum

longitude longitude latitude latitude

[degree [degree [degree [degree

east] east] north] north]

The resolution of the grid in degrees: % Grid resolution [degree] % dl = 1; %maximum is 1, minimum used by the CMC, dl=1/32; Number of vertical levels (must be the same in param.h) % N = 32;

Then: % Minimum depth at the shore [m] (depends on the resolution, % rule of thumb: dl=1, hmin=300, dl=1/4, hmin=150, ...) % This affect the filtering since it works on grad(h)/h. % hmin = 300; % % Maximum depth at the shore [m] (to prevent the generation % of too big walls along the coast) % hmax_coast = 500; % Slope parameter (r=grad(h)/h) maximum value for topography smoothing % rtarget = 0.02; %0.025; % GSHSS user defined coastline (see m_map) % XXX_f. mat Full resolution data % XXX_h.mat High resolution data % XXX_i.mat Intermediate resolution data % XXX_l.mat Low resolution data % XXX_c.mat Crude resolution data % coastfileplot = ‘coastline_l.mat’; coastfilemask = ‘coastline_l_mask.mat’;

Finally the last section to modify in order to meet the minimum requirements for a run is: % 6 Temporal parameters (used for make_tides, make_ NCEP, make_OGCM) Yorig = 2008; % reference time for vector time % in roms initial and forcing files % Ymin = 2008; % first forcing year Ymax = 2008; % last forcing year Mmin = 1; % first forcing month

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Mmax = 2; % last forcing month % Dmin = 1; % Day of initialization Hmin = 0; % Hour of initialization Min_min = 0; % Minute of initialization Smin = 0; % Second of initialization % SPIN_Long = 0; % SPIN-UP duration in Years

To execute the run, it is necessary to conduct the following analysis (not all are essential but it is recommended to perform all of them, to ensure greater accuracy in results) ROMS file names (grid, forcing, bulk, climatology, initial). Then from Matlab: >>start >>make_grid >>make_NCEP >>make_clim >>make_bry

(in the case that make_NCEP it was not possible to do the forcing and the bulk) >>make_forcing >>make_bulk

you return to the terminal where you run the executable jobcomp ./jobcomp

and finally ./roms roms.in

If the process is correct, from matlab write >>roms_gui

and through the menu the roms_avg.nc file opens in ROMSFILES

3.1.4 Displayers 3.1.4.1 GrADS 1.- Create a directory in /usr/local/GrADS 2.- Download wget ftp://ftp.ucar.edu/mesouser/MM5V3/MM5toGrADS. TAR.gz

3.- Unzip tar -xvzf MM5toGrADS.TAR.gz

3.1.4.2 Vis5D TOVIS5D 1.- $tar -xvzf tovis5d. $tar.gz This creates the TOVIS5D folder. 2.- Edit the Makefile as follows: linux: cd src/ ; $(MAKE) target \ INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN

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“FC = ifort” \ “FCFLAGS =free DLINUX I. convert big_ endian” \ “CCFLAGS =g DLITTLE DUNDERSCORE c” \ “LIBS =Vaxlib “ $(RM) tovis5d ; $(LN) src/tovis5d .

if ( ! e $1 ) then echo “The file $1 does not exist”

3.- Then proceed to the compilation: $ make linuxx $ make linuxopengl

Then tovis5d.csh must show as follows: if ( ! e $1 ) then echo “The file $1 does not exist” exit 1 endif tovis5d $1 >&! tovis5d.log

If the machine has NvidiaTMGraphics Card, place: $ make linuxnvidia.

4.- Access the /datos/MM5Vis directory to type:

3.- Compile $vis5d vis5d.file

$ make linux

The models shown are running experimentally in the various countries, some of which are shown in Fig 69.

The options for the prediction: !/bin/csh f set echo cat >! user.in << EOF &userin view_times=0.,3.,6.,9.,12.,15.,18.,21.,24.,27., gracetime_in_seconds=300., model_version = ‘mm5v3 output’, new_fields = ‘the’, discard_fields = ‘RAD’, ‘PP ‘, interp_2_height = .true., output_terrain = .false. / &end

4.- Go to /datos/MM5Vis to type $tovis5d MMOUT_DOMAIN1

It should show ======================== === normally ended === ========================

Vis5D 1.- The program can be downloaded from the following links: ftp://ftp.ssec.wisc.edu/pub/vis5d5.1/vis5d5.1.tar.Z ftp://ftp.ssec.wisc.edu/pub/vis5d/vis5ddata.tar.Z 2.- Create the folder to be installed in: /usr/local/vis5d $tar -xvzfvis5data.tar.Z

This is created: EARTH.TOPO vis5d5.1

LAMPS.v5d

OUTLSUPW

OUTLUSAM

SCHL.v5d

$tar -xvzf vis5d5.1.tar.Z clone.tcl label.tcl lui5 movie2.tcl README spin.tcl trajcol.tcl contrib highwind.tcl Makefile movie.tcl README.ps src userfuncs convert import listfonts Mesa NOTICE PORTING trajcol2.tcl util wslice. tcl

Figure 69

then tovis5d.csh must show the text as follows:

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3.2 IMPLEMENTATION OF NUMERICAL MODELS FOR CLIMATE PREDICTION The Regional Group of Numerical Modeling The NMSs of the Andean countries have given a written approval to officially belong to a Regional Numerical Modeling Group (RMG). This group was created in Guayaquil in June 2008, and it is at the moment under the Technical Coordination of Prof. Angel G. Munoz (attached to the Scientific Modeling Center of The University of Zulia and CIIFEN research associate), and under the institutional coordination of CIIFEN. The Group constitutes an efficient mechanism to consolidate the technical capabilities of those who use models in NMSs and thus sustain and improve what is obtained throughout this regional project. Annex II includes the GRM Reference Terms and letters of support signed by the 6 Directors of the Meteorological Services. Additionally, a wiki was developed for the installation of the operating system, available at: http://www.cmc.org.ve/wiki/

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CHAPTER IV Implementation of Agro Climatic Risk maps


Pilar Ycaza p.ycaza@ciifen-int.org

CHAPTER IV

Nadia Manobanda n.manobanda@ciifen-int.org

4.1. DEFINITION OF RISK1 Risk is defined as the combination of the probability of occurrence of an event and its negative consequences1. The factors that comprise it are the threat and vulnerability. Thread is a phenomenon, substance, human activity or dangerous condition that can cause death, injury or other health impacts, as well as property damage, loss of livelihoods and services, social and economic disruption or damage to the environment1. Threat is determined by the intensity and frequency. Vulnerability the characteristics and circumstances of a community, system or asset that make it susceptible to the damaging effects of a threat1. With the mentioned factors is obtained the following formula. RISK= THREAD . VULNERABILITY2 The factors that make up the vulnerability are exposure, susceptibility and resilience, expressing their relationship in the formula. VULNERABILITY= EXPOSURE . SUSCEPTIBILITY RESILIENCE

2

Exposure is the disadvantaged due to location, position or location of a subject, object or system at risk. Susceptibility is the degree of inherent fragility of a subject, object or system to counter a threat and receive a possible impact due to the occurrence of an adverse event. Resilience is the ability of a system, community or society exposed to a threat to resist, absorb, adapt and recover from the effects of timely and effective manner, including the preservation and restoration of its basic structures and functions.

4.2. CONCEPTUAL MATHEMATICAL MODEL OF AGRO-CLIMATIC RISK For agriculture climate risk estimation, the following formula was used: AGROCLIMATIC RISK

=

CLIMATE THREAD

CROP SUSCEPTIBILITY

EXPOSURE

CROP RESILENCE CROP * VULNERABILITY

*Vulnerability = [Susceptibility / Resilience]. Exposure. The threat is made up of the relation of three climatic parameters: precipitation, maximum temperature and mini1. UNISDR, Terminology on Disaster Risk Reduction 2009 for the concepts of risk, vulnerability and threat.

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mum temperature in a seasonal period (three months) and is based on the output of the statistical model. These parameters are considered as external factors affecting the crop phenological development, adverse effects of increased intensity and frequency with which they produce floods, drought, frost and excessive heat events whose effects are negative for most crops. As internal vulnerability elements of directly proportional crops, we considered that the exposure and susceptibility of the crop is inversely proportional to its resilience. The exposure of the crop was determined considering the location and environmental conditions in which it grows, and that for this case were: climate agriculture floor, season, texture, slope, soil retention capacity, areas prone to erosion, flooding, landslides, frost and other specific conditions in the pilot area to determine how much the crop is exposed to the climate threat. On the other hand, crop resilience is determined by the degree of weakness in the face of adversity climate at different stages of development; for example in the case of corn, high temperatures stop the growth of the plantation, during flowering it can suffer more damage because high temperatures increase the number of sterile plants and decreases the number of kernels per cob , i.e. that the climate damage leads to reduced growth of crops per hectare and a reduction in their field. As the last component and inversely proportional in the agriculture climate risk measurement is the ability to cope with adverse weather conditions, expressed in this study by management practices that farmers have to deal with environmental hazards; an example is the development of drainage and irrigation canals to offset deadly floods. In conclusion, agriculture climate risk estimation is established by the relationship of probable climatic effects. This is determined by the parameter of precipitation and temperature on crops, whose vulnerability is represented by the susceptibility of the crop at different development cycles as well as the ability to cope with adversity represented by farmer’s management practices and its relationship along with the crop’s exposure. This is represented mainly by the soil grain size characteristics, the presence of the crop in areas of recurrent adverse events such as floods and frost

4.3. COMPONENTS AND AGRICULTURE CLIMATE RISK VARIABLES Agriculture climate risk components are borrowed from the general formula of risk calculation2, these components being threat and vulnerability. They are in turn composed by exposure, susceptibility and the ability of the crop to face the threat. Each component is described as follows:

2.Marti Ezpeleta, A., 1993. Cálculo del Riesgo de Adversidades Climáticas para los Cultivos: Los Cereales de Verano en Montenegros. p.264 Dpto. de Geografía y Ordenación del Territorio, Universidad Zaragoza.

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The areas prone to floods were evaluated as follows:

The most adverse weather threats to crops are extreme events or persistent rainfall and temperature, with which floods, drought and frost is associated.

FLOOD FREQUENCY

To evaluate the threat, the following values were considered: precipitation, maximum temperature and minimum temperature above and below normal, subject to gradations for threat assessment introduced by each. These variables were assigned proportionate with the level of prediction, according with what is generated from statistical models for seasonal forecasting. THREAT SCENARIOS

P

VALUE

VALUE

Very often

5

Often

4

Regularly

3

Little

2

Slightly

1

No

0

Table 3.- Evaluation of frequency of flooding. Project ATN/ OC-10064-RG The altitude is valued based on a level that divides the upper zones from the lower zones in the area of interest, as follows:

> 50% of normal

5

50% above normal

4

40% above normal

3

ALTITUDE

VALUE

30% above normal

2

High zone

1

Between 10 and 20% above normal

1

Lower zone

2

Normal

0

Between 10% and 20% below normal

1

30% below normal

2

40% below normal

3

50% below normal

4

Less than 50% of normal

5

Table 1.- Evaluation of the climate threat. Project ATN/ OC-10064-RG

4.3.2. Vulnerability As the general formula of vulnerability states2, we calculated the components of vulnerability, i.e. exposure, susceptibility and resilience, in this way: Exposure Floods To evaluate the exposure we considered soil texture (to infer the water-retaining capacity), flood risk areas and altitude. Depending on the capacity of soil to retain water and considering the texture as the central element related to this ability, the following values were assigned for different textural types: TEXTURE

VALUE

Very Fine

5

Fine

4

Media

3

Moderately coarse

2

Coarse

1

Table 2.- Evaluation of texture. Project ATN/OC-10064-RG

Table 4.- Evaluation of altitudinal zones to floods. Project ATN/OC-10064-RG Frost To evaluate the exposure, altitude and frost-prone areas were considered. Frost-prone areas were evaluated as follows: FREQUENCY OF FROST

VALUE

Very often

5

Often

4

Regularly

3

Little

2

Slightly

1

No

0

Table 5.- Evaluation of frequency of frost. Project ATN/ OC-10064-RG The altitude is valued based on a level that divides the zones of the area of interest, as follows: ALTITUDE

VALUE

High zone

2

Lower zone

1

Tabla 6.- Valoraciテウn de pisos altitudinales ante heladas. Proyecto ATN/OC-10064-RG Susceptibility Susceptibility was valued according to the phenological stage of the crop, for different possible climate conditions predominant development stage in which the crop is found for the month or period of interest. The assessment was performed considering the levels of precipitation and temperatures above and below normal.

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PRECIPITATION STAGE

% Above normal

Normal

Resilience For the assessment of resilience we considered the irrigation and drainage infrastructure, whose presence allows crops to reduce the impacts caused by adverse climate events; it is evaluated as follows:

>50

50

40

30

20

10

SowingGermination

4

4

3

2

1

1

0

GrowthTillering

5

4

4

3

2

1

0

Flowering

5

5

4

4

3

2

0

Grain filling

5

5

5

4

4

3

0

IRRIGATION AND DRAINAGE INFRASTRUCTURE

MaturationHarvest

5

5

5

5

5

4

0

Presence

1

Abscence

2

Table 7. Susceptibility rating of phenological phases to above normal rainfall. Project ATN/OC-10064-RG

VALUE

Table 11. Assessment of irrigation and drainage infrastructure. Project ATN/OC-10064-RG

4.4. PROJECT APPLICATION AREAS PRECIPITATION STAGE

Normal

For the definition of pilot areas the following criteria were considered:

4

0

4

3

0

4

3

2

0

3

2

1

0

• Existence of an acceptable spatial coverage of meteorological stations. • Agricultural activity relevant in social and economic terms. • Farming activity with a level of vulnerability. • Available information base.

2

1

1

0

% Bellow normal >50

50

40

30

20

10

SowingGermination

5

5

5

5

4

GrowthTillering

5

5

5

4

Flowering

5

5

4

Grain filling

5

4

4

MaturationHarvest

4

4

3

Table 8. Susceptibility rating of phenological stages to below normal rainfall. Project ATN/OC-10064-RG

The pilot areas designated for the project by each of the countries with the selection of crops, are shown in the table below.

TEMPERATURES STAGE

% Above normal

Normal

>50

50

40

30

20

10

SowingGermination

4

4

3

2

1

1

0

GrowthTillering

5

5

4

3

3

2

0

Flowering

5

5

4

4

3

3

0

Grain filling

5

5

4

3

3

2

0

MaturationHarvest

4

4

3

2

1

1

0

Table 9. Susceptibility rating of phenological stages at temperatures above normal. Project ATN/OC-10064-RG

TEMPERATURES STAGE

% Bellow normal

Normal

>50

50

40

30

20

10

SowingGermination

5

5

4

4

3

3

0

GrowthTillering

5

5

4

3

3

2

0

Flowering

5

5

4

3

3

2

0

Grain filling

4

3

3

2

1

1

0

MaturationHarvest

3

3

2

2

1

1

0

Table 10. Susceptibility rating of phenological stages at temperatures below normal. Project ATN/OC-10064-RG

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Pilot Area

Crops

Venezuela

Portuguesa State

Rice, corn, sesame, sorghum

Colombia

Bogota and Tolima

Flowers, rice

Ecuador

Guayas, Manabí, Los Ríos

Corn, rice, soybeans

Perú

Mantaro Valley

Potato, corn, artichoke

Bolivia

Highland Region

Potatoes, lima beans, quinoa

Chile

Valparaíso Region

Citrus, avocado

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IV

Table 12. Pilot Areas and Crops for Country. Project ATN/OC-10064-RG

Figure 70. Pilot Areas of Project ATN/OC-10064-RG. INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN

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4.5. INFORMATION REQUIREMENTS

4.5.3. Thematic mapping

Information was requested in tabular format for qualitative and quantitative information related mainly to agro-ecological characteristics of crops and in digital format [shapefile] for the base and thematic mapping.

Thematic information, that serves as input for this calculation, should be obtained in digital format [shapefile] and be properly geo-referenced. Thematic Mapping

4.5.1. Agro-ecological

Floods Erosion

The data required as inputs to evaluate the crops considering their agro-ecological characteristics are:

Landslides Droughts

• Predominant varieties. • Phenological stages. • Threshold precipitation [mm]. • Threshold temperature [° C]. • Annual Season. • Soil Texture.

Frost Current land use Vegetal cover Soil texture

We worked with the dominant variety that was the most representative of the crop in the area, and the precipitation and temperature thresholds that are required to find the optimal conditions for crop development. We included other requirements as the periods of the year for planting and harvesting and optimal soil texture. An example of agro-ecological requirements is shown in Table 14. REQUIREMENTS AND AGRO-ECOLOGICAL PARAMETERS Country

Crop location Table 16. Requirements for thematic mapping. Project ATN/OC-10064-RG The forecasts of precipitation, maximum and minimum temperature, are generated by NMHSs and must be converted to digital format and georeferenced. Climate Information

Pilot zone

Precipitation forecast Maximum temperature forecast Minimum temperature forecast

Cultivation

Table 17. Requirements of climate forecasting. Project ATN/OC-10064-RG

Predominant varieties Phenological stages Threshold precipitation (mm)

From:

To:

Threshold temperature (ºC)

From:

To:

It requires for the satellite images of the area of interest to be updated, preferably within the last two years. The best image resolution required is10 m.

Economic threshold (%) Annual season Satellite Images

Soil texture Table 14. Requirements and agro-ecological parameters. Project ATN/OC-10064-RG

4.5.2. Base mapping Digitalized basic information was required to put together the base mapping. This information had to have official status and be as updated as possible. Basic Mapping

National Political Limit Provincial or Departmental Political Boundaries Municipal and Cantonal Political Boundaries

Water System

Area of interest

Table 18. Requirement of satellite images. Project ATN/OC10064-RG

4.5.4. Treatment of Information The information collected must go through a validation process, correction, editing and standardization, which is known as information processing. In this process all the errors and discrepancies that exist on the provided information are corrected. It was necessary to standardize the layers to the same reference and projection system [WGS 84 - UTM].

4.5.5. Soil and climatic characteristics in pilot areas

Road System Populated Centers Urban Areas

As a result of data gathering, the edapho-climatic characterization was also obtain in each area.

Contours Topography

Table 15. Requirements of basic cartography. Project ATN/OC-10064-RG

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Venezuela Country

Zone

Venezuela Portuguesa State

Altitute (m.o.s.l.)

Normal Precipitation (mm)

Normal Maximum Temperature (ºC)

High Zone

<200

1350

31

22

Sandy, clay, silty clay, clay

Low Zone

>200

1250

32

22

Clay loam, silty clay, clay

Altitudinal Zones

Normal Minimum Temperature (ºC)

Soil Texture

Table 19. Soil and climatic characteristics of the state of Portuguesa, Venezuela. Project ATN/OC-10064-RG

Colombia Country

Zone

Bogota Savannah Colombia Tolima

Normal Maximum Temperature (ºC)

Normal Minimum Temperature (ºC)

220

22

8

2540

200

22

8

North Savannah

2580

200

22

8

South Tolima

425

550

35

22

Clay loam

Center Tolima

431

525

34

20

Sandy

South Tolima

450

500

34

20

Sandy clay loam

Altitudinal Zones

Altitute (m.o.s.l.)

Normal Precipitation (mm)

Southwest Savannah

2543

Center Savannah

Soil Texture

Being flowers in greenhouses, for the Bogota area this parameter was not required

Table 20. Soil and climatic characteristics for the Bogota Savannah and Tolima, Colombia. Project ATN/OC-10064-RG

Ecuador Country

Ecuador

Zone

Coast Region

Normal

Altitudinal Zones

Altitute (m.o.s.l.)

Upper Basin

>40

*1500

Lower Basin

<40

1250

Normal

Normal Maximum Minimum Precipitation Temperature Temperature (ºC) (ºC) (mm)

Soil Texture

31

22

Clay loam, sandy loam

32

22

Sandy clay loam, silty loam, sandy loam

Table 21. Soil and climatic characteristics for Coast Region, Ecuador. Project ATN/OC-10064-RG * In the rainy season

Peru Country

Perú

Zone

Mantaro Valley

Altitudinal Zones

Altitute (m.o.s.l.)

Upper Basin

>3350

Lower Basin

<3350

Normal

Normal

*1100

19

5

Clay loam, sandy loam

*1000

20

6

Sandy clay loam, silty loam, loam, sandy loam

Normal Maximum Minimum Precipitation Temperature Temperature (ºC) (ºC) (mm)

Soil Texture

Table 22. Soil and climatic characteristics for the Mantaro Valley, Peru. Project ATN/OC-10064-RG * In the rainy season

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Bolivia

Zone

Plateau Region

Altitudinal Zones

Altitute (m.o.s.l.)

Normal

Normal

Normal Maximum Minimum Precipitation Temperature Temperature (ºC) (ºC) (mm)

Soil Texture

Northern Highlands

4000

*660

10.7

6.8

Silty loam, clay loam

Center Highlands

3500 to 4500

*429.2

11.9

5.7

Sandy, sandy loam, silty loam-clay

Southern Highlands

3500 to 4500

*247.8

16.8

7.9

Sandy loam

Table 23. Soil and climatic characteristics for Region Altiplano, Bolivia. Project ATN/OC-10064-RG * Annual Average ture periods (3 months). The first step was to summarize or simplify if necessary the description field of the attributes table of the corresponding variable. After this a new field was created, in where the values previously presented in Table 1 were assigned. Once they were evaluated, it was proceeded to simplify the attribute table for each variable, leaving only the fields of description and evaluation. To this point, the subsequent operations conducted with them were facilitated [union].

Chile The soil and climate parameters in Valparaiso were provided in digital data layers (shapefile format). Agro-climatic zones in the Valparaiso Region belonging to semi-arid and temperate system are: • Semiarid Andes Mountains • Semiarid Middle Mountain • Semiarid Northern Coastal • Temperate Andean Cordillera • Temperate Intermediate Depression • Temperate Coastal Range • Temperate Coastal Central • Temperate Southern Coast

With the simplified attribute tables, we proceeded to join the three variables, obtaining a new layer of climatic threat conditions for the corresponding period, which summarizes in each polygon a homogeneous condition of precipitation, maximum temperature and minimum temperature (as can be illustrated in Figure 72).

In the Valparaíso Region there are four types of climate: a dry steppe climate which is the continuation of the climate in the IV Region and three temperate climates that are distinguished from each other by the characteristics of clouds and the length of dry periods. Its average annual rainfall varies in its various zones between 260 and 560 mm. Textured soils are mostly sandy-clay and silty-sandy.

Then, in the table resulting from the union of the three variables, a new field is introduced, where the sum of the value of each variable[the three areas of evaluation] is made; this implies the threat level that each area has and thus the component threats are now ready. Exposure While the parameters involved in the exposure to rain or extreme temperatures are more or less stable over time, an exposure map was prepared, which will be considered as a constant for the next few months.

4.6 AGRO-CLIMATIC RISK CALCULATION For the agricultural climate risk evaluation, a GIS tool was used to calculate all its components, using the variables inherent to each of them as illustrated in Figure 71.

In the case of texture, records were generalized (summarized) based on the field that describes the texture and then a new field was created for the assessment of each texture category, using the values in Table 2.

Thread Map The calculation of the climate thread starts with the rainfall forecast, maximum temperature and minimum tempera-

CLIMATIC DATA (Thread evaluation)

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ALTITUDINAL FLOORS (Level curves) SOIL TEXTURE, EXPOSED ZONES TO FLOODS AND FROSTS (Exposure evaluation)

FENOLOGIC STAGE (Susceptibility evaluation)

IRRIGATION AND DRAINAGE INFRASTRUCTURE (Resilience evaluation)

Figure 71. Variables for agriculture climate risk assessment. Project ATN/ OC-10064-RG

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or non-existence of infrastructure. Later a new field is created for the assessment of the two infrastructure categories [whether or not it exists], using the values set forth in Table 11. In all cases after the evaluation, we must simplify the attribute tables so as to show only those essential elements, or those fields related to both, the description of each parameter and its value, in order to simplify the calculations on which these variables intervene.

Figure 72. Polygons of climatic threat conditions. Project ATN/OC-10064-RG In the case of flood risk, its attribute table records were generalized (summarized) based on the field that describes the risk of flooding and then a new field was created for the evaluation of each category of floods, using the values set in Table 3. For the case of altitude, its records were generalized (summarized) based on the field that describes these soils and a new field was created for the values of each of its categories, using the values set forth in Table 4.

Vulnerability map Proceed to join the layers of susceptibility, resilience and exposure. In the attribute table of this union a field was added, where the processes established in the formula of vulnerability will be applied. For example, multiply the exposure field value by the susceptibility field value and divide it by the resilience field value. To simplify the vulnerability map, to be used in the latest risk measurement process, the table is cut down, leaving only the last field where the formula for calculating vulnerability was developed. Agro-Climatic Risk Map The next step was to link the vulnerability map with the threat map and to multiply the fields of implicit valuation in each of these 2 components, presenting these results on a map with the estimated resulting risk.

The table of the attributes of each one of the variables listed above was simplified so as to show only those essential elements, or those fields related to both the description of each parameter as well as its value, and thereby simplify the process of “union” described next. Finally, we proceeded to the union of these three layers and the resulting attribute table of this union, a new field of total exposure evaluation, is created, which will be obtained through the sum of partial valuation fields of the three variables (risk of flooding, texture, altitude levels) for each record or polygon. To simplify the Exposure map for subsequent processes, reduce the table leaving only the field of exposure evaluation (sum). The map will become the constant exposure for some time, due to the low temporal fluctuation of its variables. Susceptibility For the evaluation of the susceptibility, we work with the union of the corresponding crop layer and the layer of homogeneous climatic conditions. In the attribute table of the weather, a new field properly summarized [generalized] was introduced, which will give the values of crop susceptibility to these climatic conditions. The valuation tables of the susceptibility are found in the tables: 7, 8, 9 and 10. We should repeat the same procedure for each crop, thus the component of susceptibility will be solved. Capacity for Recovery / Resilience To obtain a resilience map, it was necessary to rely also on the crop layer, adding information about the presence or absence of irrigation or drainage canals. Records are generalized based on the field that describes the existence

Figure 74. SIG structure for Agro-Climatic Risk calculation. Project ATN/OC-10064-RG The agricultural climate risk level obtained can be represented by its absolute values or by risk intervals. We recommend assigning different shades of red to the different levels of risk. RISK LEVEL

VALUE

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High

5

168

0

0

Moderately High

4

230

0

0

Medium

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255

70

70

Moderately Low

2

255

127

127

Low

1

255

190

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Table 24. Agriculture climate risk assessment. Project ATN/OC-10064-RG

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4.7 AGRO-CLIMATIC RISK IN THE ANDEAN COUNTRIES The agricultural climate risk assessment in the Andean countries is carried out with the application of the developed methodology that allows the integration of basic variables for risk calculation in each country. The methodology used generates the model for a first risk approximation, which, although is an estimate, it gives a tool to support decision making in the agricultural sector. Each participating country in the project made adjustments to some of the proposed variables in order to obtain results tailored to local realities and therefore stating that this methodology gives us the base guidelines to obtain a first approximation of agriculture climate risk and it should have adequations and adjustments as required. On next page are the agricultural climate risk maps created for the 6 countries.

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Figure 75. Agriculture climate Risk Map of Sesame crop. Estate of Portuguesa, Venezuela 2008. Project ATN/OC-10064-RG

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Figure 76. Agriculture climate Risk Map of rice crop. Tolima Valley, Colombia 2008. Project ATN/OC-10064-RG

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Figure 77. Agriculture climate Risk Map of rice crop. Costa de Ecuador 2008. Project ATN/OC-10064-RG

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Figure 78. Agriculture climate Risk Map of potato crop. Mantaro Valley, Peru 2008. Project ATN/OC-10064-RG

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Figure 79. Agriculture climate Risk Map of potato crop. Plateau of Bolivia 2008. Project ATN/OC-10064-RG

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Figure 80. Agriculture climate Risk Map of citrics crop. Valparaíso region, Chile 2008. Project ATN/OC-10064-RG

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CHAPTER V Implementation of local systems of climate information


CHAPTER V

Abigail Alvarado a.alvarado@ciifen-int.org Alexandra Rivadeneira a.rivadeneira@ciifen-int.org

5.1 CONCEPTUAL AND METHODOLOGICAL ELEMENTS The confusion generated by the users for climate information creates distrust, even more in a sector so vulnerable to the climate as is farming in South America and the considerable climatic dependence for irrigation. The producers of climate information mistakenly assume that the information they provide will be absolutely crucial in making decisions by users, in this case farmers. Seen from the perspective of the users, the picture is different. The decision-making process is anthropologically determined by a set of information in which the climate is a part of, but it is not the only component. It needs to consider the atmosphere, the attitude and finally a totally unpredictable element that depends on the culture, perception and psychological profile of the users. Although the decision maker is always going to look for more and more detailed weather information, the role of this information will be shared with other elements not related to climate. What is relevant in this analysis is that something that cannot happen is that the decision-making process does not use climate information at all because it does not have it, or it does not understand it, or because it is confusing or simply because he does not trust the information. In this context, from the standpoint of the mental processes involved, it is much more valuable to have modest information in resolution, but clear and accessible, so that it is used in the process. The challenge is to ensure that the farmer will give climate information its space in his decision making-process. To achieve this would represent a basic pillar in integral management of climate information. The premise of optimum weather information management comes from the fact that instead of having few informed people (usually scientists) with forecasts and good-quality climatic information, we should have informed people with acceptable weather information. This would mean more people making decisions based on the modest but acceptable weather information provided, but delivered in such a way that it is fully used1.

The climate information dissemination systems are designed to successfully close the cycle of information management. This envolves the design of strategies for sustainability and consolidation over time. The strategy used to strengthen the climate information system includes the following lines of action: 1) Strengthen the final format of climate information products. 2) Articulate the means to disseminate the information. 3) Empower users by introducing and involving them in the system, and 4) Establish alliances with potential actors/beneficiaries system multipliers to strengthen it. Figure 81 shows the cycle of information management. The conversion of the products to a simpler language and userfriendly and specific to each media format allows information to be distributed in many forms, and makes it more likely to be assimilated. These media can range from television, newspapers, radio, internet, and even text messages via cell phone or HF radio. The way in which users in a country see the weather is not in a computer chart or in the output of a model; users see the climate as what they experience: rain, drought, frost, wind, etc. If this perception is later associated with a name, for example: El Ni単o, La Ni単a, a physical pattern is generated in the imagination of the user, which is experienced by a label. This is internalized and remains in the minds of the users. Now, after a while when referring to El Ni単o or La Ni単a, for the user, there are only images associated with floods or droughts, death and destruction; the rest of the text that is used to supplement the information is simply transparent to them: it does not exist, it is not assimilated, it is just the mental image of what was internalized, and they will act accordingly with it if the message is repetitive or convincing2. The disseminated information is effective when it is understandable; it is received without distortions and generates a response in the recipient, for this, there must be a network of key users to maximize its distribution.

The implementation of local systems for the dissemination of climate information is intended to deliver this information to the farming community through various sources (print, radio, magazines, newsletters, television, sms text messages, email, among others) through the consolidation of user networks, strategic alliances, training workshops and capacity building to promote the system and especially the products. The climate services used on this project (database, climate, statistical and numerical forecasts, agroclimatic risk maps) for each country, plus the existing products at each NWS should ensure timely, fast, reliable and over time sustainable distribution, in which the information is not distorted and it serves to support decision making.

The information can be distributed to different user groups, whether these are authorities, representatives of associations or unions, rescue teams (fire department, Red Cross), disaster management agencies, private sector, researchers and students of higher educational institutions, community representatives, among others. Due to differences in the distinctions of user groups, for purposes of a more standardized management at the regional level, they are classified into three main groups or categories and focused on the ultimate goal of this regional initiative: the agricultural sector.

1. Martinez, Rodney, 2006. Information management and climate prediction services to reduce impacts on agriculture in South America. Campinas, 8-15.

2. Martinez, Rodney, 2006. Information management and climate prediction services to reduce impacts on agriculture in South America. Campinas, 8-15.

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This structure was applied to the six Andean countries, emphasizing in a greater or lower level its component categories according to the socio-cultural and political intervention in each project region.

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• Identify the actors To identify the actors, several activities were carried out: LIST At this early stage we must work together to review any information gathered and then, through brainstorming, have a list of all the persons or institutions that can meet the following characteristics:

Figure 81. General structure graph Climate Information System

5.2 IDENTIFICATION AND MAPPING OF KEY ACTORS The mapping of actors is a technique used to identify all persons and entities that may be important to build a distribution network of weather information. This technique ensures that mapped users clearly know beforehand to who they have to define specific strategies to help them ensure the flow of information so that the actions taken are coordinated3.

• Are being or could be affected by the problem. • Could be affected by the proposed solution to the problem presented by the group. • They are not being directly affected, but may have an interest in the proposal. • They have information, experience or resources necessary to contribute to the goals of the project. • They have a national or local reach, for example associations of farmers, rescue groups, private companies of agricultural products, among others. • They are accepted by the community, e.g. community radio stations or radio fans, community leaders. • They are necessary for the implementation of project activities. • They feel entitled to be involved. • They are necessary for project sustainability. Thus, we obtain a preliminary list of stakeholder groups that should be mapped:

To perform a basic stakeholder mapping, you must perform the following steps: define the issues, identify stakeholders and map the actors. • Define the topic At this stage we specify which are the persons, groups or organizations on whom we should work according to the topic. They become important players to the work that is going to be performed. In this case, the climate services generated are focused on agro-climatic risk management in three of the four areas [4] that includes it: Risk Analysis, Risk Reduction and Management of Adverse Events.

Communitary Leaders

Communication Media

Private/ Productive Sector

State Agencies and Authorities

Universities

International Agencies

Figure 83. Preliminary group of key actors FOCUS The next step is to have each one of the groups identified and to obtain their contact information.

Private Sector

Risk Analysis

Agripac Corp. Group Risk Reduction

Management of Adverse Events

Figure 82. Risk Management, figure by Omar Dario Cardona, Adapted by CIIFEN, 2009 3. The methodology outlined is based on the document: Tools to Support Participatory Urban Decision Making Process: Satakeholders Analysis. Urban Governance Toolkit. UN-HABITAT program, 2001. 4. It was not considered the fourth area, recovery whose components are rehabilitation and reconstruction.

Agripac S.A. Public Relations Cynthia Baratau, Agripac En Directo magazine Publisher Address: Córdova 623 and Padre Solano. Phone (593-4)2313327 E-mail: cbaratau@agripac.com.ec

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tegories which in turn will have more subcategories. In this case three major groups were established: Authorities, Media and Productive Sector, as outlined below:

Figure 85. Mapping categories and subcategories of Actors

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• National Government • Ministries • Regional Intendences • Sub secretarials • Government ministry • Township • Mayors • Red Cross • Firemen Deps.

Each country updated the contact information available in the National Weather Service, classified it in the prescribed format and then fed the database with new key users according to available and selected climate products following the intervention area within the country. The monitoring of each group is important to create an alliance and show the commitment, scope and applicability of the project. After completing this step, we should contact each one of them; this represents field work to validate and complement the previously formed database. Figure 79 shows two steps to carry this out: through meetings and their subsequent monitoring. The meeting with the mapped actors constitutes the first advance, for which printed and digital material should be brought along to briefly report the purpose of the visit, and to form alliances, emphasizing that the benefit is mutual.

1

(identification of actors)

2

MEETING FOR ACTOR MAPPING

MONITORING KEY ACTORS

• Sending of letters of intention to formalize alliances. • Sending of weather information created by CIIFEN. • Coordination of dates and places for workshops

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• Cell Phones • Television • Radio National Newspapers • Local Newspapers • Internet

Productive Sector • Associations • Fraternities • Private Bussiness Production Chambers • Corporations • Universities • Communities

5.3 STRATEGIC ALLIANCES There are several things to consider when setting a strategic alliance or commitment to cooperation through a letter of intent or commitment letter: • These alliances are not contracts. • The letters of commitment or intent formalize the partnership of cooperation between the agency and the NWS (in this case). • Letters of commitment should have equal obligations of both parties (win-win). In Annex III there is the inventory of strategic alliances in the region. It is recommended to manage these letters or agreements during the mapping of key players because at this stage contact or dialogue is direct. Moreover, achieving a strategic alliance and formalizing by means of a letter of intent is a process that is usually not achieved in the short term. It is advisable to carry out these partnerships with entities that correspond to any of the three proposed groups; however, there are some exceptions, such as remote locations or populations that do not receive all the radio frequencies and therefore, have very specific services of certain frequencies that cover only that area. These local radio stations, in the case of broadcasting information to very vulnerable populations, become strategic partners when issuing an early warning or broadcasting climate information that the people need. Continuing with the same example, in these cases local radio stations tend to relay information for a limited time from other frequencies through phone or HF radio. In the case of signing an agreement with a radio station that has this type of retransmission mechanism through a signal relay to a local radio or amateur radio, the impact of providing climate services has a wider scope.

CONTACT MAP

• Coordination of the Meeting. • Creation of material for the presentation. • Information exchange through contact. • Establishment of verbal commitment. • Give information to those present.

3

Communication Media

Figure 86. Activities for the stakeholder mapping and monitoring

For the National Weather Service, this represents the responsibility to comply with sending information continuously and as stated in the agreement, meeting deadlines, format, length, and even ensure that it is in an easy-to-understand language. The commitment letters provide a mechanism to ensure dissemination of information; in the case of climate, to a certain group of end users. Each entity, whether it is governmental, private or non-profit, is committed to publicizing

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Establishing links with national/local institutions is vital for disseminating climate information. These are formal mechanisms with operational and powerful infrastructure, especially in small towns. Local authorities are the first group that should be approached and clearly and timely shown the focus of the action to be undertaken, the expected products and especially the benefits which that location will have once the action is implemented. A concise brochure and contact information is sufficient during the first approach. From then on, regular contact and good communication are important.

STRATEGIC ALLIANCES

• Formal commitments with those organisms whose reach is all intervention area of the project in the country.

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5.4 STRATEGIC ALLIANCES WITH LOCAL AUTHORITIES

MONITORING KEY ACTORS

• Sending of letters of intention to formalize alliances. • Sending of weather information • Coordination of dates and places for workshops

4

H A

Figure 87. Monitoring of key players

the products through their normal mechanisms for distribution, which may include: • E-Newsletters • Printed newsletters • Specialized magazines • Daily and weekly newspapers with national or local level reach (electronic or printed format) • Radio Programs • Television Programs • Mobile, text messaging • Website • Others You can define a set of desirable characteristics in information products and services (WCMC 1998, CADRC 2004). Information products must5: • Be aimed at specific audiences and have a purpose. • Based themselves on scientific principles and high quality data. • Be easy, as well as fast, to understand. The user-product interaction is facilitated by two features: a high level of representation of objects and an intuitive interface (CADRC 2004). The user interface should be graphical in nature. Overall, the product must be easily operable, so that users can learn to use independently. However, a support system should always be available. • Be accompanied by a full survey of the sources of information and intellectual property. • Be relevant and in time for decision-making needs. • Be circulated through recognized channels. • Be available at minimal costs in time, money and administration. • Have affinities with domestic and international references.

5. Suárez-Mayorga A.M. (ed.). 2007. Administrator’s Guide for information on biodiversity. Biodiversity Information System of Colombia-SiB-Research Institute Alexander von Humboldt Biological Resources, D.C. Bogota, Colombia, 74 p.

Having agreements with local or national entities such as municipalities or governors is very important when organizing advocacy and awareness activities such as workshops, press conferences or even preparing press releases. Having a cooperation agreement or letter of intent ensures commitment from the local authority to support such initiatives, and in turn confirms the seriousness of the partner on the quality of the type of information being broadcast to the target population of the town. Having the support of state institutions strengthens the interest level of the population to participate in everything that is carried out, because it is backed by the local authority. There are also other national bodies established to channel policies for the improvement of the various development sectors. In the case of agriculture, national agricultural associations, or associations of producers are key allies. Its members are farmers, community leaders or technicians, and are constantly receiving training on related issues, and undertake activities in each locality to enhance their products and services. These networks are normally easy to reach, and therefore possess the valuable contact information and knowledge and credibility of its partners. Creating a partnership with this type of national institutions guarantees the mapping of players and also serves as a mean to reach a larger number of beneficiaries. The invitation to the events is done through them in places that people always gather and during the times that they know there will be a high attendance. Thus, the response from attendees is always positive and their participation greater because they are familiar with the place, the people who summon them and on a date which does not overly or interfere with their daily activities. Thus, twenty strategic alliances were achieved with local authorities in the region, described in the table on next page.

5.5 THE STRATEGIC ALLIANCES WITH THE PRIVATE SECTOR The private sector can become a great ally when creating a cooperation agreement, as it has the resources and infrastructure needed to support various initiatives. However, it should be kept in mind in this particular category that the actions taken with them should alter their normal activities as little as possible. Before any approach, it is important to identify the resour INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN

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• Ministerio del Ambiente • Municipalidad Distrital de Acolla • Municipalidad Distrital de Tunan Marca • Universidad Nacional de Huancavilca • Municipalidad Provincial de Jauja • Dirección Regional de Agricultura de Junín

PERÚ

In this case, during the project implementation, the NMS committed to:

• Asociación de Agricultores de Quilotoa • Comité de Paltas • Municipalidad de Quilotoa • Secretaría Ministerial de Agricultura de la Región de Valparaíso • Facultad de Agronomía de la Pontificia Universidad Católica de Valparaíso • Fundación de Comunicaciones, Capacitación y Cultura El Agro (FUDCA)

CHILE

BOLIVIA

• Prefectura del Departamento de La Paz

VENEZUELA

• Empresa Agroisleña • Asociación de Productores de Semilla Certificada de los Llanos Occidentales (APROSELLO) • Asociación de Productores del Estado de Portuguesa (ASOPORTUGUESA)

ECUADOR

1. Provide periodic climate risk maps, reports and forecasts described in a easy-to-understand language. 2. Provide technical assistance to the working group for dissemination of company information with the farmers. 3. To issue at least one training workshop with company personnel on the interpretation of the technical information generated. 4. Grant credit relating to the company’s products disseminated through this cooperation. The next step was to coordinate with the Department of Public Relations and Editorial on format and length of articles to be published. In this case it was easier for the company to add an article containing the following basic requirements: • Minimum length: 1 page. • Maximum length: 1 sheet. • Color maps. • Up to 3 full-color maps per page.

• Coorporación de Desarrollo Regional de El Oro (CODELORO) • Corporación Nacional de Agricultores y Sectores Afines (CONASA) • Municipalidad de Babahoyo • Consejo de Desarrollo del Pueblo Montubio de la Costa (CODEPMOC)

ces of that company, its communication strategy and especially if it performs social actions. Thus, the first communication will have the following elements:

Subsequently early drafts of the article to be sent, in coordination with the MTF, were done. These products are understood as information resources designed for a specific audience and defined purpose. They are the result of the compilation and presentation of analyzed or interpreted information (Villegas, Franco 2003).

• Clear concept of what is to be achieved • Benefits to be given to the target audience • Possible mechanism to execute the action. Letter of intent. • Benefits for private enterprise. Recognition in terms of corporate image and social responsibility by supporting the work.

Once the newsletter was ready, it was set as a template for subsequent editions. When the project was completed, this alliance passed it on to the National Weather Service, for it to maintain this operational mechanism with their products beyond the project life. There were also meetings with both institutions to establish a closer link and during the first four months, a close monitoring of the facility was done.

Two examples are given of successful strategic alliances with the private sector.

The newsletter is sent via email, additionally attaching as a separate files each logo and image contained in the article in the best possible resolution.

5.5.1 Journals Specializing in Agriculture Upon completion of the mapping of stakeholders, we identified an Industrial Group6 and an Editorial Group7 as potential allies, becauSPECIALIZED se they fulfilled certain characteristics: MAGAZINE

• Large Private Companies, easily recognized by the agricultural sector, their management and support. • Publish magazines focused on the topic. • Independently perform training campaigning on agricultural issues every year. • They have a high acceptance from population.

The following table describes very generally certain characteristics of both journals:

SCOPE

DISTRIBUTION MEDIA

METHOD OF ACQUISITION

CIRCU LATION

Free Magazine

5.000

3,00 USD

10.000

Agroindustrial Group AGRIPAC

National

Bimonthly

Agripac Centers of distribution of agricultural supplies. (128 total)

Editorial UMINASA

National

Monthly

Supermarkets

The first approach was to coordinate a meeting by appointment with each institution. It showed the scope of the project, the scheme

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6. www.agripac.com.ec 7. www.elagro.com.ec

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• Recurrence of transmission: limited. Only if there is probability of adverse event in the action area (which is defined by the NMS). • Mechanism of Sustainability: Letter of intent signed between all stakeholders to ensure the commitment from all parties. • Message Type: According to the parameters set by the telephone and relay company. Warning messages of a major climate threat 11. Once this point was reached, we proceeded to establish the mechanism for sending text messages and its format. As the repeater company is the one sending text messages, it is this company that should receive the message and the database attached to the cell to send later. To reach a format, several preliminary tests between the agencies involved were carried out to reach a consensus. An example of SMS message is shown below:

The main problem they faced was related to costs. In Ecuador, all cellular companies work with a single company, called Message Plus10. it is responsible for transmitting text messages, it is a relay company), independent of all participants, for sending text messages. This is a very strong argument for not having the direct power to grant a free service of written messages, as the expenditure can not be assumed solely by the cell phone company, but by the relay company which is responsible for sending the text messages. To overcome this obstacle, meetings were coordinated with both the cellular company and the relay/repeater firm, leading as a concrete proposal and limited in scope with the following characteristics: • Action Area and scope: limited. Coastal Region, 5 provinces (Esmeraldas, Manabi, Los Rios, Guayas and El Oro). • Number of Members: limited. Up to 1,000 users in the provinces agreed upon.

Figure 89 shows the established mechanism of transmission. At the time if INAMHI predicts a likely occurrence of any adverse event for the agricultural sector or the wider community, for example heavy rains, it will send this message via email to the repeater company, which must confirm its receipt. Then INMAHI should call the designated person to reconfirm receiving the email and find out the sending status. MPlus immediately sends that text message to the approved mobile database.

TELEPHONE SERVICE USER

CELLULAR TELEPHONE COMPANY RELAY COMPANY

TEXT MESSAGES SERVICE

Figure 88. General scheme of the mechanism of telephony and text messages

8. www.movistar.com.ec 9. Users MOVISTAR mapped in 2008 in the provinces of Esmeraldas, Manabi, Los Rios, Guayas and El Oro These recipients represent farmers’ associations, private sector, agencies and / or bodies Rescue local and sectoral authorities, community leaders and officials in NGO’S,

IO and public institutions whose activities are aimed at risk management and disaster prevention. 10. www.mplus.ec 11. No Alarm messages will be issued as only Authority local, regional or national level can create alarm messages.

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Figure 89. Messaging mechanism

2) SEND MESSAGE VIA E-MAIL 1) WRITE TEXT MESSAGE

3) RECEPTION/ CONFIRMATION OF WARNING MESSAGE

5.6 THE STRATEGIC ALLIANCES WITH THE MEDIA We worked intensively with the media in the region, stressing much on the issues of sustainability and taking care in how to develop different products for each medium in ways that are as understandable and attractive as possible. A working group from each country coordinated the proper management at this crucial stage of the project, and also the research and the gathering of information to transform these newsletters or radio messages into “communicable” climate information. Also included was the background information compiled by a panel of experts in agriculture from each country, that through surveys and interviews obtained valuable information about traditional jargon and other cultural elements to establish communication with the target audience.

4) SEND TEXT MESSAGE TO MOVISTAR USERS

There was also a very good approximation in Bolivia with Pachaqamasa rural radio, which has interpreters and translates newsletters from the Meteorological Service of Bolivia, SENAMHI to Aymara, the language widely used in the native rural population of Bolivia.

We first analyzed and systematized information previously obtained on the traditional knowledge of the villagers so that they can understand the collective imagination in every sector of intervention. The results are processed in the Annex IV. After this, and having identified the actors in the media, appointments were coordinated to create partnerships for dissemination of climate information. In this stage, there was an interaction within this group of players to receive their feedback regarding the development of formats. Because of the experience with the private sector, there was a basis for articles in newspapers and electronic magazines, but with radio it was necessary to work on the product type, then on its frequency and delivery method. At this point it is worth emphasizing the importance of having identified radio broadcast networks, and the progress that some have acquired to use the Internet as alternative media. In the case of Chile, for example, we managed to implement an audio narration through the radio that broadcasts Foundation of Training, Communication and Culture, an organization with which, through a cooperative agreement, broadcasts over the radio the interpretation of agro-climatic risk map for Chile’s Region V. This big step and innovative mechanism for disseminating information and, in this case, the interpretation of maps, also continues to remain operational even after the project’s completion. The audios can be downloaded from FUCOA’s website: http://www. fucoa.gob.cl/radio/radio.php Photo 1. Radio Pachaqamasa, El Alto, Bolivia.

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C It is no the same to write a newsletter for the radio, than to write a newsletter for the press, for the following reasons12:

Spoken Language

Written Language

It is more disarranged

It is more impersonal

It is more personal

It is more accurate

It is more expressive

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Figure 90. Comparative table between spoken and written language That is why, when broadcasting a message over the radio, it should give the main idea in the first sentence, however, an article for print media can depict the ideas and even use more complex vocabulary. At the regional level, agreements with several media were reached, including local newspapers, radio networks, online newspapers, weeklies and community foundations, listed in figure 91.

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• Radio Programas Perú RPP • Semanario Enfocando la Semana • Sin Pelos en la Pluma • Tierra Fecunda

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• Radio emisora Nexo AM y Libra FM • El Mercurio de Valparaíso • Municipalidad de Quilotoa • Empresa Periodística El Observador • Fundación de Comunicaciones, Capacitación y Cultura El Agro (FUDCA)

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5.7 TRAINING STRATEGIES As a complement, we developed educational materials for community leaders, advisors of authorities and rescue groups. The compilation and systematization of similar materials were the basis for developing all the parts. Without quality information there cannot be an effective participation13 and this is the reason for all the effort to improve climate products and services generated by the NMSs and CIIFEN, and to circulate it through the media. This was presented in workshops so that the people also knows where to find weather information and how to interpret it. In the case of Ecuador, the central concept of this material is to train potential trainers on the topic of prevention of risks to replicate basic concepts very clearly to the people in remote locations. The material developed is an educational basic guide for risk prevention, with emphasis over the information systems implemented during the project. The material was designed in such way that training activities are part of a comprehensive training program outlined to help an individual or group to learn14. For the development of all the phases of the material, we worked in close coordination with the NMS, partner agencies and project work team to define the general outline of the educational kit. For this, we first established the general content to know what kind of activities could be done according to each chapter and the way in which this instruction would be presented. This also contained the experiences gained during the field trip in the design phase of the mapping of stakeholders and building strategic alliances, since they had that background on local needs and knowledge gaps. After the completion of this stage, we obtained the general content of the guide divided into five modules: Module I: Introduction Use of Community Guide and training materials, how to use it. Module II: Climate and Climate Variability Work table, remembering the past

• El Diario S.A. • Editorial UMINASA • Coordinadora de Radio Popular y Educativa del Ecuador (CORAPE) • Movistar, Message Plus • Radio Naval - INOCAR y 32 Radiodifusoras • Diario La Hora de Quevedo

Module III: Risk Management and Agro-climatic Risk Mapping. Work Table, Development of Community Risk Map Module IV: Information and Prevention Module V: Early Warning System

Figure 91. List of agreements signed with media Thus, in general, it is concluded that 06 partnerships were established with the productive sector, 16 partnerships with government institutions and 11 with media in the Andean region (Bolivia, Chile, Colombia, Ecuador, Peru and Venezuela) to disseminate, through different channels, climate products that were generated through the project and also the products made by each NMS. 12. Graphic CIIFEN Adapted by the book “Training Manuals” Library University House Great, School of Communication, 2007.

To make the material user-friendly, the introduction was a module on how to handle the designed material, and even the logistical coordination that must be carried out to organize a workshop. Each chapter of the Guide has summarized and clear explanations through practical examples. Each chapter has supporting visual material to explain the central idea of that module. There are also primers for each chapter, promo 13. Gustavo Wilchez-Chaux, 2006. 14. Framework for strengthening the capacity of National Societies, Colombian Red Cross.

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ting group activities during the workshop. A cartoon was also created to conclude each workshop, which had a similar version to a printed booklet that was given along with the other printed materials. All this material is supplemented to provide the training workshop, and to have visual support, it is not imperative to have an In Focus projector for the workshop; it can even be arranged without a computer. This effort integrated the support from other cooperating institutions, such as the ProVention Consortium, and DIPECHO, via the V action plan. The items that make up this training kit are: • Community Preparation Guide: Guide notebook for the trainer; it contains the methodology to organize workshops, the members who must attend, place, time, development of each module, management and guidance during the work tables. • Activities Primers: Direct complementary elements; the guide, they give instructions on the activity to be developed, and also how to develop the activity. • ”El Temporal” newspaper: Newspaper that concisely summarizes the core concepts of each module in the guide. Element of consultation at any time during the workshop. • Cartoon: Animated story about disaster prevention. Additional element to reinforce concepts • Pamphlet: Brochure with additional tips on how to take care of our environment. • Lunar Calendar: Given at the workshops, wall calendar. • Pocket guide: List of provincial emergency telephones. Besides the items mentioned above, participants were given folders and pens and material required to develop the activities. Overall, the training workshops had an introductory phase before the development of the topics. The development and implementation of information systems was explained. Then an introduction was given on basic concepts to familiarize the participants on the methodology and use of workshop tools. During the workshops, participants applied the concepts through group activities, sharing experiences to synthesize them on a chart about a locally-impacting adverse climatic encounter (work table module II) and to develop a climate risk map of their sector, which identified risk zones, vulnerable areas, risk areas, and proposed possible temporary shelters and evacuation routes (work table module II). At the end of each workshop, each attendee was given a certificate, a community guide and printed visual support material, including primers for the work tables contained in a folder for 10 people. That is, each attendee received a community guide and 10 folders so that they can replicate the workshop in their locations. It is important to point out that it is not necessary to have projector or computer to implement the workshop.

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In the case of the other Andean countries, we worked in close coordination with each NMS to develop training materials, answering the needs of each intervention area, and according to the capabilities identified during the mapping. Special emphasis was given to the interpretation of agro-climatic risk maps and to each NMS newsletters, which were improved by the project team. The workshops ensured the presence of the media, local authorities and were attended by representatives of trade associations, representatives from disaster management agencies, rescue agencies, production houses, technicians, consultants from local authorities, community leaders, among others.

National Workshop on PROSUKO facilities. Community Pucarami, Bolivia.

Brunildo and Magola, characters of the serie “Let`s understand wheater to can live with him”

National Workshop. Aragua, Venezuela

National Workshop. Huancayo, Junin Department, Peru National Workshop. The Ligua, Valparaiso Region, Chile

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CHAPTER VI Capacity building in Western South America


CHAPTER VI 6.1 REGIONAL WORKSHOP TRAINING ON CLIMATE MODELING STATISTICS The Regional Training Workshop on Climate Modeling Statistics, took place on October 8-13, 2007 at the Bolivarian Military Aviation’s Weather Service installations in Maracay, Venezuela. The workshop gathered 18 people from the National Meteorological Services of Bolivia, Chile, Colombia, Ecuador, Peru and Venezuela. The workshop included the participation of two regional trainers: Ángel Muñoz (Venezuela) and Marco Paredes (Peru), who combined the theoretical and the practical phases of the course that was based on the implementation of the CPT tool (Climate Predictability Tool) developed by the IRI. The workshop had two components: instructional and applicability. The results of the workshop were: The seasonal forecast for the October to December 2007 quarter, with actual data for each country prepared by each participant, the monthly and bimonthly forecast for each country presented individually, the Quarterly regional seasonal forecast and preparation of a document discussed by the participants on methodological principles and recommendations for application and implementation of Climate Modeling Statistics in the region.

Participants of the Regional Workshop on Numerical Modeling I (Lima, 19 to 24 November 2007)

Training by technicians participating NMHSs in the use of CMM5 and CWRF Photo of the participants of the Regional Workshop on Numerical Modeling Statistics (Maracay, Venezuela, October 8-13, 2007)

6.2 REGIONAL TRAINING WORKSHOP ON NUMERICAL MODELING FOR CLIMATE PREDICTION The Regional Training Workshop on “Modeling Climate Statistics” was held on November 19-24, 2007 at the headquarters of the Meteorology and Hydrology Service of Peru (SENAMHI) in Lima. The workshop gathered 14 people from the NMHSs of Bolivia, Chile, Colombia, Ecuador and Peru. The lecture, exercises, methods and practice sessions were conducted by D. Angel G. Munoz Solorzano, professor at the University of Zulia (Venezuela) and Deputy Director of Scientific Modeling Center (CMC).

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6.3 REGIONAL TRAINING WORKSHOP FOR AGRO-CLIMATIC RISK MAPPING The International “Methodology for Agro-climatic Risk Mapping” Workshop took place on January 14-19, 2008 at the Santiago de Guayaquil Catholic University in Guayaquil, Ecuador. The workshop included six people from the National Meteorological Services of Bolivia, Chile, Colombia, Ecuador, Peru and Venezuela, eleven people representing government agencies and private institutions of Ecuador (INAMHI, INOCAR, SENPLADES, UCSG, MAGAP, MAA, Cedega). The workshop had the participation of: Angel Llerena, Harold Troya and Nadia Manobanda, who combined the theoretical and practical phases of the course that was based on the explanation of the methodology and risk mapping for the agricultural sector. The workshop had two components: instructional and applicability. In addition, with the participation of Juan Jose Nieto in the operation of the Surfer tool for weather forecast mapping. The results of the workshop were: development of an agro-climatic risk map, with real data prepared by groups of four participants and a group presentation by participants on the methodo-

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6.5 INTERNATIONAL TRAINING WORKSHOP ON CLIMATE DATA PROCESSING The workshop was organized by CIIFEN and the Venezuelan Aviation Weather Service (SEMETFAV) on October 6-7, 2008, in which the participants learned techniques for processing, filtering, quality control and time series standardization. They reviewed theoretical concepts as well as some tools (codes in matlab) for such purposes.

Photo of the participants of the Regional Workshop on Numerical Modeling of Weather and Climate II May 26-31, 2008

6.4 REGIONAL WORKSHOP ON NUMERICAL MODELING OF WEATHER AND CLIMATE II The Regional “Numerical Modeling of Weather and Climate” Workshop was held on May 26 -31, 2008 at Escuela Politécnica del Litoral (ESPOL) in Guayaquil, Ecuador. Workshop participants included five people from the National Meteorological Services of Bolivia, Chile, Ecuador, Peru and Venezuela as well as eighteen people representing government agencies in Ecuador (INAMHI, INOCAR, ESPOL, Institute of Fisheries). The workshop included the participation of Prof. Angel G. Muñoz S. (Center for Scientific Modeling, CMC, University of Zulia - Venezuela) as an instructor, who introduced the content of theory and practice sessions: the former focused on atmospheric-oceanographic phenomena and their involvement in the weather and climate forecast, as well as the related physical mathematical fundaments, global and regional models, downscaling and validation. In the practice sessions, attendees were able to compare in detail the differences between models and observations, and carry out their own executions in (C) MM5 and (C) WRF weather and climate models, fed with data from the NNRP, GFS and CAM model, from which CMC is in operational mode to make regional forecasts. Finally, it is worth noting that the workshop allowed to formally establish the Regional Modeling Group (MRG), which includes all the participants of the meeting and had the support of the National Meteorological Services.

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CHAPTER VII Performance indicators


CHAPTER VII BOLIVIA

asked, IDEAM being the largest source with 47%, followed by FEDEARROZ with 38%, 10% and Internet radio 5%.

In the Department of La Paz-Northern Altiplano, OruroCentral Altiplano, Potosi, Southern Highlands, Bolivia, surveys were conducted on the Agrarian Union, local municipal governments, small farmers, local NGOs, lender institutions, Ministry of Agriculture (SIBTA), Ministry of Lands, Ministry of Planning, as a baseline which gave the result that only 2% has access to climate information, while the remaining 98% does not have access to it. This information was selected as a baseline by the project team and SENAMHI to disseminate weather products mainly through newspapers and radio. Once the entire project management and national workshops were completed in the Department of La Paz, a new survey was conducted focusing on the people who did not receive information. The results showed that of the 98% that at the beginning of the project did not have access to climate information, 5% now had access to climate information. In addition, out of the total number of people surveyed, 74% use climate products for the agricultural management of their crops to varying degrees.

CHILE In Region V, Chile, initial surveys were carried out and concluded that 62% has access to climate information, while the remaining 38% does not have access to it. This information was taken as a baseline for the project team and DMC to disseminate weather products mainly through newspapers, radio and internet. Once the work and national workshops were completed in Region V, a new survey was conducted focusing on the people who did not receive information. The results showed that of the 38% that at the beginning of the project said they did not have access to climate information, 4% now had it. In addition, out of the total number of respondents, 67% find a high applicability of climate products for the agricultural management of their crops. A question regarding the major sources of climate information was also asked; the newspaper is the largest source with 25%, followed by email with 21% cell phones with 20%, 18% radio and internet with 16%.

COLOMBIA In the Department of Tolima and Sabana de Bogotá, Colombia, a baseline survey was undertaken on 26 flowerproducing and rice-producing companies in the Savannah Bogota and in central Tolima, respectively. This number constitutes 10% of all companies (260) in the region. The results showed that 60% has access to climate information while 40% does not have access to it. This information was taken as a baseline by the project team and IDEAM. At the end of the project and upon completion of the national workshops, a new survey was conducted focusing on the people who did not receive information. The results showed that of the 40% that at the beginning of the project said they did not have access to climate information, 33% now had it. In addition, out of the total number of respondents, 70% found climate products very useful and applicable for farm their crop management. A question regarding the main sources of climate information was also

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ECUADOR In the provinces of Manabi, Los Rios and Guayas, Ecuador, initial surveys were conducted which concluded that only 4% has access to climate information, while 96% does not have access to it. This information was taken as a baseline for the project team and INAMHI to disseminate weather products through newspapers, radio, internet, email and private sector journals. Once the project management team and national workshops in the 3 provinces were finished, a new survey was conducted focusing on the people who did not receive information. The results showed that of the 96% that at the beginning of the project said they did not have access to climate information, 94% now had access to it. In addition, out of the total number of people surveyed, 87% finds a high applicability of climate products for the agricultural management of their crops. A question regarding the major sources of climate information was also asked and the newspaper is largest source with 41%, followed by radio at 32%, magazines with 18% and 9% cell phones.

PERU In the cities of Cockaigne and Huancayo in the Mantaro Valley, Junín, Peru, initial surveys were conducted which concluded that only 6% has access to climate information while the remaining 94% does not. This information was taken as a baseline by the project team and SENAMHI to disseminate weather products through newspapers, radio, internet and email. Once the project team and national workshops in the two cities mentioned above were finished, a new survey was conducted focusing on the people who did not receive information. The results showed that of the 94% that at the beginning of the project said that they did not have access to climate information, 15.98% had access to it now. In addition, of the total number of those surveyed, 83% finds a high applicability of climate products in the agricultural management of their crops.

VENEZUELA In the towns of Turén, Acarigua and Guanare in Portuguesa State, Venezuela, the surveys conducted as a baseline for the project team concluded that 43.3% has access to climate information, while 56.8% does not. This information was taken as a baseline for the project team and SEMETAVIA in order to disseminate through internet, email and associations climate products to end users. Once the project management team and national workshops were finished in these three cities, a new survey was conducted focusing on people who did not receive information. The results showed that of the 56.8% that at the beginning of the project said that they did not have access to climate information; 45.55% now had access to it. Furthermore, of the total number of those surveyed, 85.67% finds a high applicability of climate products into the agricultural management of their crops.

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BIBLIOGRAPHIC REFERENCES Power, Scott, Plummer, N., Alford, P. 2007. Making Climate Model Forecast more useful. Australian Journal of agricultural Research. 58: 945-951. Power, Scott, Sadler, Brian, and Nicholls, Neville. 2005. The Influence of Climate Science on Water Management in Western Australia Lessons for Climate Scientists. BAMS-86-6-839. Santillán, G., 2005. Manual para la Prevención de Desastres y Respuesta a Emergencias. La experiencia de Apurímac y Ayacucho. ITDG. Randall E et al. A beginner’s guide to structural equation modeling pg. 38. Suárez-Mayorga A.M. (ed.). 2007. Guía del administrador de información sobre biodiversidad. Sistema de Información sobre Biodiversidad de Colombia -SiB-, Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, Bogotá D.C., Colombia, 74 pp. Sutherland, W., et al. 2009. One Hundred Questions of Importance to the Conservation of Global Biology Diversity. Society for Conservation Biology. 1523-1739. Tarbell, T.C., T.T. Warner and R.A. Anthes, 1981. The initialization of the divergent component of the horizontal wind in mesoscale numerical weather prediction models and its effect on initial precipitation rates. Mon. Wea. Rev., 109, 77-95. UNIDR, 2009. Terminología sobre Riesgo de Desastres. United Nations Organization, 2001. Tools to Support Participatory Urban Decision Making Process: Stakeholder Analysis. Urban Governance Toolkit of HABITAT program. United Nations, UNEP, 2007. Biodiversity and Climate Change. International day for Biological Diversity. 1-48. Villagrán De León, J., 2006. Vulnerability. A conceptual and Methodological Review. United Nations University Institute for Environment and Human Security. Series UNU – EHS No. 4. UNISDR, 2009. Terminología sobre Reducción de Riesgo de Desastres 2009 para los conceptos de Amenaza, vulnerabilidad y riesgo. Wilches-Chaux, G., 2007. Conceptos Básicos sobre Gestión de Riesgo y Seguridad Territorial. Wilches-Chaux, G. Brújula, Bastón y Lámpara para trasegar los caminos de la Educación Ambienal. Ministerio del Ambiente, Vivienda y Desarrollo Territorial, República de Colombia. Xiao, Q., W. Guo, and X. Zhou, 1996. Preliminary results from numerical experiments of a heavy rain process with PENN STATE/NCAR MM5, Advance in Atmospheric Sciences, 13(4), 539-547. William H. Press. Numerical recipes: the art of scientific computing pg. 349. Yucel, I., W. J. Shuttleworth, X. Gao, and S. Sorooshian, 2003. Short-Term Performance of MM5 with Cloud-Cover Assimilation from Satellite Observations, Monthly Weather Review, 131, 1797-1810.

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This represents an increase of new users in Bolivia by 4.9%, 1.52% in Chile, 13.2% in Colombia, 92.94% in Ecuador, in Peru 15.03% and 25.88% in Venezuela. The increase of users in the region is 25.58% and the population that finds climate information applicable is 77.78% in the Andean region.

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CHAPTER VIII Learned Lessons


CHAPTER VIII IMPLEMENTATION OF THE VIRTUAL CORE OF CLIMATE APPLICATIONS (VCCA) • The project component of the Core Development Virtual Climate Applications in the developmental stage passed through several obstacles that were part of the software development cycle; however, it was possible to identify three lessons: • The development of VCCA was a team effort, and although It was coordinated over distance, it had positive results. The join of Andean countries on this effort and the creation of an integrated database showed that it is possible to create regional products and make them available to the general public, despite distances and differences. • Creating high quality software applications and availability are feasible on Open Source tools, without detracting functionality in the least. These are a highly reliable alternatives to ensure the permanence of the application in time. • The rapprochement between the Web-based computer tools and the end user is usually possible, offering easy to use products that provide useful information for the activities of these users.

IMPLEMENTATION OF STATISTICAL MODELS FOR CLIMATE PREDICTION • Among the problems identified it can be mentioned that upon not having a standardized methodology, the different national contributions to regional prediction were not uniform and sometimes physically inconsistent, having results diametrically opposite in neighboring countries along their borders. Moreover, the statistical background on the methodology of the terciles, correlations and linear combinations seems to be unfamiliar to some of the users of the program. For this reason their climatic perspectives were sometimes based on subjective evaluations. The cause of this seems to be that the resources of some institutions are limited to daily tasks and they can allocate very little of their budgets to research and training. So far there have been few but valuable training opportunities on these concepts to the participants in these forums. CIIFEN has made great efforts to disseminate a set of executable programs that utilize user-friendly graphic interfaces to be used by meteorological services in weather forecast. • One of the biggest challenges was the standardization of knowledge related to the use of software available in the world; this activity was a gradual process in the field of statistical modeling that was done by itinerant experts and can be synthesized in 3 steps: • 1st Stage: Use of Exever (program developed by NOAA/ OGP), and promoted by CIIFEN in western South America in 2004 as a climate prediction tool, which sought linear correlations between two variables for a particular season, the predictor and predicting, through cross-correlations -1 and 0 lags (monthly time scale). This software was easy to use in areas where there weren’t many stations; however, it

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entailed more time when many stations worked because the runs were carried out station by station and variable by variable. • 2nd Stage: of CPT (program developed by IRI), and promoted by CIIFEN in western South America in 2005 as a climate prediction tool, through which was determined the maximization of the linear correlations between a set of predictor variables and a set of one variable that was located over a predicting area. This stage had a breakthrough with respect to numbers of processing stations. The use of predictors and access of data to be used as predictors via the IRI data library was an important starting point for the use of CPT. • 3rd Stage: Use of the CPT as a validation tool in weather forecasting; this stage sought to exploit the validation component that CPT has immersed after obtaining preliminary results. • Each stage was reinforced with permanent itinerant visits to each of the participating countries and training conducted within the Climatic Forums of western South America, which is approved by the World Meteorological Organization coordinated by CIIFEN. • The use of CPT achieved the standardization of methodologies for climate prediction, obtaining coherent and comprehensive (regional) results of precipitation and temperature variables. The delivery time of forecasts to the population was summarized and the search for shorter temporary horizons (semester and monthly scales) was begun. • The seasonal forecast for western South America, based on CPT as a common tool, is an operational product that is generated monthly and distributed to thousands of users in the region.

IMPLEMENTATION OF NUMERICAL MODELS FOR CLIMATE PREDICTION • The attention and motivation on the part of NMHSs to this activity was always very high. • An interesting aspect was that although the original objectives envisioned only the start of experiments in downscaling in retrospect and with only a single regional model, it was possible, working in coordination, to install and configure in the vast majority of countries two regional models (CMM5 and CWRF) and additionally, integrations of the models in experimental forecasting was begun. • In some NMHSs, the Internet connection was slow, so the decision to back up everything necessary on a portable hard drive was correct. • Although the experiments were completed, a pending assignment of each NMHS involved obtaining each model’s climatology. Indeed it is a task that, while not complicated, involves a long period of computing time to reach its end and that is related to the availability and computational ca-

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pabilities present in each Weather Service and associated with the Project. At present some countries are completing this assignment, but others have not continued it.

• The local team in each country should have worked longer on the project to build alliances, at least 12 months of hard work are needed.

• Two training workshops were conducted: One (November 2007) regarding installation, configuration and basic execution of the CMM5 model. The second (June 2008) focused on deeper aspects of the climatic configuration and implementation of both CMM5 and CWRF. It is considered that a single workshop definitely was not enough, so that having organized a second to reinforce ideas and methodologies was worthwhile. Conducting two training sessions a year of such topics is a recommended idea.

• The development of baseline and final project measuring surveys should have been carried out by a single group of consultants for a more detailed analysis of the impact of the results and focusing on the social, cultural and political reality of each country in such a manner that it is easy to compare it with other countries in the region.

• Having articulated training workshops, with specific tasks in each NMHS and the corresponding technical support contributed to the development of capacities for numerical modeling of the NMHS in the six countries.

IMPLEMENTATION OF AGRO-CLIMATIC RISK MAPS • The development of agriculture climate risk maps involves interdisciplinary development that brings together climatologists, geographers, agronomists, sociologists and computer programmers in a common discussion, it cannot be done unilaterally. • One of the key steps was the development of the agricultural risk conceptual model. Despite the stringency of the definitions it has to take into consideration the feasibility of obtaining the information, the scale and the accuracy of the information. In this sense, the quality of the final information is not a function of the complexity and number of variables involved but on the strength and availability of the variables to be used. • The development and validation testing involved was based on experience from experts and users. • For this type of implementation, it would be necessary two workshops, one for discussion and conceptual model validation and one for training in the design of the GIS.

• The work to create alliances to form the distribution network required more time, money and manpower in each country. In fact this only warranted a separate project due to its complexity and especially because the articulation of people, institutions and other organizations demands time, building of trust, face to face contact and lots of patience. • The ideal time to give workshops on prevention and preparedness in hydro-meteorological issues is before the rainy season. • During the development of training material, the observations made by of representatives of each group about the users maps are important to strengthen the structure of content. • After having a mapping of actors, it would be appropriate to identify some important characteristics of the actors, such as having influence over another group of actors, affinity with the topics covered, level of cooperation or if they are active in the field where they work, to name a few. With this you could construct a flowchart that represents the real inter-agency relationships. • It takes time to accept changes in practices and behavior of users due to the use of new technologies. • The reliability of some development sectors in climate forecasting is still very limited. • Few consecutive incorrect forecasts cause the people to distrust research centers.

• Support from the project team through itinerant missions in each country, was critical to its implementation. • Such tools require a specialized unit NMHSs counterpart, especially for its sustainability and continuous improvement.

IMPLEMENTATION OF LOCAL SYSTEMS OF CLIMATE INFORMATION DISSEMINATION. • The technical language of climate information is difficult to convert. A native language like Aymara requires further work. It should be linked with cultural elements of ancient knowledge of the climate. • Working steadily with services throughout the process helped to see the need for additional efforts to establish protocols for dissemination of products and services in consensus with everyone and not wait for someone to appoint a service technician to design it.

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CHAPTER IX Future Actions


CHAPTER IX IMPLEMENTATION OF THE VIRTUAL CORE OF CLIMATE APPLICATIONS (VCCA) Actions to be developed according to each application are outlined: Regional Climate Database • Update information by adding new records, according to the Data Access Protocol signed by the National Meteorological Services. • Application of statistical analysis techniques for data quality. • Addition of monthly data on precipitation, maximum and minimum temperature. Map Server • Update information by country, including forecast climate information and crop types.

perience and knowledge of the local experts of each country, can continue. In this sense, CMC and CIIFEN have continued joining forces, along with NMSs and universities in the Andean region (Universidad del Zulia, Universidad Católica de Bogotá, Universidad Mayor de San Andrés, Universidad de Chile), constituting what has been called the “Extraordinary Events Andean Observatory. The Center’s purpose is to articulate institutions, technicians and technological resources to provide scientific tools which, through climate forecast, assist in decision making, the creation of early warning systems and risk management within the framework of the Andean region.

IMPLEMENTATION OF AGRO-CLIMATIC RISK MAPS • To continue improving the methodology for the agriculture risk estimation. • Incorporate information from remote sensing related to water retention capacity of soil, vegetation index and other variables.

• Encourage the use of Open Source tools for the generation of SIG products.

• Migrate the current system entirely to Open Source.

Climate Modeling Products Viewer

• Incorporate the new GIS spatial analysis tools.

• Publication of forecast products for different areas and with different climate models.

• Disseminate the methodology to other countries.

Virtual Library

IMPLEMENTATION OF LOCAL SYSTEMS OF CLIMATE INFORMATION DISSEMINATION

• Addition of new publications for general consultation, depending on their availability..

• Replicate the experience with the companies of cell phones and repeater in Ecuador and other countries.

IMPLEMENTATION OF STATISTICAL MODELS FOR CLIMATE PREDICTION

• Expande the space in the journals with which there are cooperation agreements, to provide a space with information targeted specifically to the rural community.

• It is necessary to continue to coordinate activities for standardization of validation and verification criteria of seasonal forecasts. This is a process that will take time and the results obtained will serve to guide the efforts of climate forecasting in the short and medium term. • Have virtual conferences on a monthly basis among the participants, guided by regional experts or CIIFEN. • Share and expand this process to other regions of Latin America and the Caribbean.

IMPLEMENTATION OF NUMERICAL MODELS FOR CLIMATE PREDICTION

• Increase the number of partners in private companies to disseminate information. • Develop and disseminate spots containing warnings and short messages over the radio and certain television spaces. • Replicate the most relevant experiences in the region to create pilot projects in other areas. • Maintain contact with the mapping of participants established through personal visits, email, local workshops, and videoconferences, among others.

It is the opinion of the National Meteorological Services (NMSs) that the organizational and structural initiative that currently exists for implementing regional climate models, configured with the settings chosen on the basis of the ex-

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CHAPTER X Elements of Sustainability


CHAPTER X IMPLEMENTATION OF THE VIRTUAL CORE OF CLIMATE APPLICATIONS (VCCA)

IMPLEMENTATION OF LOCAL SYSTEMS OF CLIMATE INFORMATION DISSEMINATION

• The NVAC, since its initiation, was designed as a sustainable system in time, at the stages of development, maintenance and updating. The following elements of the sustainability of the system have been defined:

• One of the positive factors identified has been the level of commitment reached by all NMHSs, derived from a coherent Operations Plan and focused on the institutions.

• Open Source Architecture Software: To ensure sustainability of the NVAC, from the planning stage, the use of software under Open Source license was determined. Under this philosophy, future license renewal through additional payments is avoided. Respecting this principle, each of the components of NVAC as well as the applications running on it, have been developed using Open Source tools, thus ensuring the retention and upgrading of applications on time. • Free Access: Access to the NVAC applications is free, any user with an Internet connection can access, view and obtain information.

• The mechanisms implemented to disseminate information through private enterprise and the media were given to the NMHSs to adopt them as their own and integrate them into the mandatory activities of the Service. This new window for the NMS is an opportunity to position the service as a good source of products and services developed specifically for the end user. • Having private enterprise as an ally ensures a more prolonged duration of an agreement since it is more stable than political positions. Having made agreements with this group gives greater strength to the sustainability of what has been implemented.

• Information Update: Authorized users of each NMS are able to update the weather information as it is generated; this way, products are guaranteed to have the latest information gathered by regional NMHSs.

IMPLEMENTATION OF STATISTICAL MODELS FOR CLIMATE PREDICTION • The regional project helped to consolidate the formation of an important group of techniques from the six countries of the region, with the capacity to expand the critical mass of people that successfully operate, understand and apply the CPT. • CIIFEN, as an international organization with strong ties to NMHSs, continues to promote improved seasonal forecasting and training and technical assistance opportunities.

IMPLEMENTATION OF NUMERICAL MODELS FOR CLIMATE PREDICTION • During project implementation, CIIFEN signed a cooperation agreement with the Center for Scientific Modeling of the University of Zulia. • This partnership has ensured the important technical support of CMC on numerical modeling, within the various lines of work that CIIFEN maintains with NMHSs in the region.

IMPLEMENTATION OF AGRO-CLIMATIC RISK MAPS • CIIFEN maintains a technical support to all NMHSs regarding risk maps to ensure sustainability. • Manuals produced and distributed to NMHSs, allow to work in the GIS and do subsequent alterations.

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BIBLIOGRAPHIC REFERENCES Aldrian, E., 2009. Regional Climate Information for Risk Management (needs). World Climate Conference 3, white paper Nº12. Andrade, A. (ed), 2007. Aplicación del Enfoque Ecosistémico en Lationoamérica. CEM-UICN. Bogotá, Colombia. 1-89. Anthes, R.A. and T.T. Warner, 1978. Development of hydrodynamic models suitable for air pollution and other mesometeorological studies. Mon. Wea. Rev., 106, 1045-1078. Brown, C. and Hansen, J.W., 2008. Agricultural Water Management and Climate Risk. Report to the Bill and Melinda Gates Foundation. IRI Tech. Rep. No. 08-01. International Research Institute for Climate and Society, Palisades, new York, USA. 19 pp. Cardona, O., 2005. Indicadores de Riesgo de Desastres y de Gestión de Riesgos. Banco Interamericano de Desarrollo, Departamento de Desarrollo Sostenible, División de Medio Ambiente. 1-53. Colle, B. A., and C. F. Mass, 2000. The 5-9 February 1996 flooding event over the Pacific Northwest: sensitivity studies and evaluation of the MM5 precipitation forecasts. Mon. Wea. Rev, 128, 593-617. Colle, B. A., C. F. Mass, and D. Ovens, 2001. Evaluation of the timing and strength of MM5 and Eta surface trough passages over the Eastern Pacific. Weather and Forecasting, Boston, MA, 16(5): 553-572. ESPOL, 2009. Aplicación de Técnicas SIG, Sensoramiento Remoto y análisis multitemporal para la detección de Impactos Antropogénicos en la cobertura de suelos y su proyección para el 2010. Caso de Estudio: Estuario de Santos (Brasil). Revista Tecnológica ESPOL. 1-7. Fernández, M., 1996. Ciudades en Riesgo, Declaración Ambiental, Riesgos Urbanos y Desastres. LA RED. Ferradas, P., 2006. Metodologías y Herramientas para la Capacitación en Gestión de Riesgo de Desastres. INDECI-Instituto Nacional de Defensa Civil, Soluciones Prácticas-ITDG. Geraldo, M., 2003. Hacia una Cultura de Prevención de Desastres, desde la Educación. Memoria del Seminario Taller Internacional sobre Prevención y Atención de Desastres en la Educación, Lima, Perú. Kaipi, K., et al. 2005. Gestión de Riesgo de Amenazas Naturales en Proyectos de Desarrollo. Serie de Informes de Buenas Prácticas del Departamento de Desarrollo Sostenible, Banco Interamericano de Desarrollo. 1-55. Marti Ezpeleta, A., 1993. Cálculo del Riesgo de Adversidades Climáticas para los Cultivos: Los Cereales de Verano en Montenegros. Dpto. de Geografía y Ordenación del Territorio, Universidad Zaragoza. Martínez, R., 2008. Users Liaison in RCOF Position Paper. International Expert Review Meeting on Regional Climate Outlook Forums, Arusha, United Republic of Tanzania. Martínez, R., 2009. Regional Climate Information for Risk Management (capacilities). World Climate Conference 3. Maskrey, A. (ed). 1998. Navegando Entre Brumas, La aplicación de los Sistemas de Información Geográfica al Análisis de Riesgo en América Latina. LA RED. 1-11. Martínez, Rodney, 2006. Gestión de la información y servicios de predicción climática para la reducción de impactos en el sector agrícola en Sudamérica. Campiñas, 8-15. Miao, J.-F., D. Chen, and K. Wyser, 2006. Modelling Subgrid Scale Dry Deposition Velocity of O3 over the Swedish West Coast with MM5-PX Model. Atmospheric Environment 40 (3): 415-429. Oficina de los Estados Unidos de Asistencia para Desastres en el Exterior, USAID, 2007. Evaluación de Daños y Análisis de Necesidades Nivel Toma de Decisiones. Michalakes, J.; Dudhia, J et al. 2004. The weather Research and forecast model: Software architecture and performance. 11th ECMWF workshop on the Use of High Performance Computing in Meteorology, Reading U.K. Pereira Fo., Augusto J. Crawford, Kenneth C. Stensrud, David J., 1999. Mesoscale precipitation fields. Part II: Hydrometeorologic modeling. Journal of Applied Meteorology, Boston, MA, 38(1): 102-125.

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Technical Guide of a Regional Climate Information System  

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