Sample Chapter - Weather: A Concise Introduction, Hakim & Patoux (2018)

GREGORY HAKIM AND JÉRÔME PATOUX

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Brief Table of Contents Preface Introduction CHAPTER 1

Weather Variables

1

CHAPTER 2 Spatial Representations of Weather Data

17

CHAPTER 3 Our Atmosphere: Origin, Composition, and Structure

43

CHAPTER 4 Heat and Energy Transfer

51

CHAPTER 5 Water

75

CHAPTER 6 Cloud Formation

90

CHAPTER 7 Precipitation 117 CHAPTER 8 Wind

126

CHAPTER 9 Global Wind Systems

145

CHAPTER 10 Air Masses, Fronts, and Midlatitude Cyclones

167

CHAPTER 11 Thunderstorms and Tornadoes

188

CHAPTER 12 Tropical Cyclones

201

CHAPTER 13 Weather Forecasting

213

CHAPTER 14 Air Pollution

227

CHAPTER 15 Climate Change and Weather

234

Glossary References Credits Index

Spatial Representations of Weather Data

Opens with chapters on weather maps and monitoring to get students thinking about real-life weather prediction straight away

20

Figure 2.1. Example of surface station map on February 20, 2014, at 18:00 UTC. To facilitate reading, temperature is indicated in red, dew point temperature in blue, and sea-level pressure in black, in addition to following the weather station model layout described in Box 2.1.

station toward the direction the wind is coming from, while wind speed is indicated with barbs and pennants.

An example of a surface weather map showing measurements at land and buoy stations on February 20, 2014, is shown in Figure 2.1 (only temperature, dew point temperature, pressure, and wind are shown, for clarity). This is our first look at a weather event that will follow us through the rest of this book: an extratropical cyclone generated east of the Rocky Mountains and sweeping through the eastern half of the United States between February 19 and 22, 2014.

Extratropical cyclones are also called midlatitude storms, or frontal cyclones, as cyclones form fronts, but fronts can also form cyclones.

At this stage of its development, the storm (let’s call it “our February 2014 cyclone”) appears in our weather map as a counterclockwise circulation occurring over the eastern half of the United States. By following the direction of the wind barbs, we can get a sense of its size and locate its center somewhere along the border between Iowa and Missouri (see Appendix 2.2

for geographical reference). By contrast, the western United States is relatively quiet with low wind speeds and no organized circulation.

Uses a single case study of a 2.2 midlatitude Surface Maps cyclone that swept through the eastern half of the USA in February As evident in Figure 2.1, it is quite difficult to appreci2014 as a common thread throughout ate the spatial distribution of the different variables at theThat book, allowing students to studythethe a glance. is why we analyze and complement in different surfacestorm maps with additional contexts elements.

2.2.1 Isotherms and Temperature Maps We first color the temperature field using an intuitive color scale, typically going from blue (low temperatures) to red (high temperatures), as shown in Figure 2.2. We further contour the temperature observations with contour lines at regular intervals. These contours are called isotherms, from “iso,” meaning

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Example of a surface temperature map on February 20, 2014 at 18:00 UTC.

“equal,” and “therm”, meaning “heat.” Isotherms are lines of constant temperature. Indeed, if we were to walk along an isotherm with a thermometer, the temperature would remain constant. If we decided key terms are from the isotherm, the temperature would to depart introduced in either increase or decrease.

Coloring and contouring the temperature map allows boldface and defined us to quickly identify the air masses, where the temperin a glossary at the ature is relatively uniform. In Figure 2.2, for example, a back of the book warm air mass occupies the southeastern sector of the United States, while a cold air mass occupies the northwestern half. In between these air masses are areas of strong temperature change, where the temperature increases from cold to warm over relatively short distances. These regions of sharp temperature change are called temperature fronts – see Section 2.2.2.

The rate of change of a variable over a certain distance is called a gradient – a notion we will use often in the rest of this book. In Figure 2.2, for example,

we can see that the temperature gradient is relatively small in the cold and warm air masses, i.e., if we drove across an air mass with a thermometer, for example from Austin, Texas, to Atlanta, Georgia, a 1500 km drive, the temperature would not vary much. Frontal regions, however, are, by definition, regions of strong temperature gradient, i.e., if we drove across a temperature front, our thermometer would record a strong temperature increase or decrease over a short distance. Going from Kansas to Arkansas, for example, the temperature increases by 30 °F over only 200 miles.

2.2.2 Temperature Fronts Weather in the midlatitudes is largely the result of air masses in motion. In fact, by matching the air masses visible in Figure 2.2 with the corresponding wind directions in Figure 2.1, you can see that the

Heat and Energy Transfer

Box 4.1.

Advanced topics are separated from the main text. This makes the book appealing to non-science students while retaining potential for advanced study

56

Radiation Laws

In the second half of the nineteenth century, a series of experimental and theoretical findings revolutionized our understanding of radiation and were critical for the advancement of physics. Around 1860, Gustav Kirchhoff introduced the concept of a “black body” – a body that, simply put, absorbs Figure 4.1.1. Gustav all radiation – and stated Kirchoff. that, if we know how much this theoretical black body emits at each wavelength at a given temperature, then we know how much any body emits, in proportion to how much it absorbs. Thus, for example, if a body absorbs half as much as a black body, it also emits half as much as a black body. Kirchhoff’s law is better known in the form,“good absorbers are good emitters; poor absorbers are poor emitters.”

A black body at a given temperature, however, does not emit the same amount of radiation at all wavelengths. The exact emission function would later be determined by Max Planck – see below. In the meantime, a series of scientists laid the Figure 4.1.2. Jožef ground for him by estabStefan. lishing several important relationships. At the end of the nineteenth century, Jožef Stefan (1835–1893) and Ludwig Boltzmann (1844–1906) determined that the total emission of radiation (E) by a black body at a given temperature T is proportional to the fourth power of temperature: E ∝ T4

We refer to this relationship as the Stefan– Boltzmann law. It tells us that, even with a small increase in temperature (expressed in kelvin), the total amount of energy emitted by an object (in W/m2) increases with the fourth power of temperature. For examFigure 4.1.3. Ludwig ple, if the temperature is Boltzmann. multiplied by 2, the total amount of energy is not simply multiplied by 2, but by 24, which is equal to 16.

This relationship has many applications in physics. In certain situations, we prefer to use the inverse relationship by which the temperature of an object changes as the fourth root of the radiative energy it emits: T ∝ 4√E Thus, if we can somehow measure or estimate the total amount of energy emitted by an object, such as with a satellite in space, we can estimate the object’s temperature.

In 1893, Wilhelm Wien (1864–1928) derived another important relationship which tells us that the wavelength of peak emission (λm) of a black body is inversely proportional to the temperature T of that black body: λm ≈ 2897/T where λm is expressed in micrometers and T in kelvin. In this relationship of inverse proportionality, known as Wien’s displacement law, when T increases, λm decreases, which corresponds to a shorter wavelength, and therefore a higher frequency. The sun, for example, with a temperature of 6000 K, emits mostly around a wavelength of 2897/6000 ≈ 0.5 µm (i.e., in the visible part of the electromagnetic spectrum). By contrast,

In-depth coverage of the forecasting process, focusing on modern methods215 such as ensemble forecasting

Weather Forecasting

as enunciated by Isaac Newton, the laws of thermodynamics, and mass conservation, as we have established throughout this book. These laws can be cast in mathematical form and used to calculate the future state of the fluid body, given its current state. This can be illustrated using Newton’s second law of motion, starting with the familiar form

(13.1)

F = ma

Where F is the sum of all forces applied to an air parcel (bold indicates vectors), a is the acceleration (change in velocity) experienced by the parcel, and m is its mass. We can rewrite it as a=

F m

(a)

(13.2)

Now recall that an acceleration is a change in velocity ∆v occurring over an interval of time ∆t: a=

∆v F = ∆t m

(13.3)

Thus, we can also write that the change in velocity ∆v =

∆t F m

(b)

(13.4)

Since the change is equal to the difference between the future and the current state, ∆v = vfuture – vcurrent

(13.5)

we obtain that vfuture = vcurrent +

∆t F m

(13.6)

In other words, if we know all that is on the right side (the current velocity of the air parcel, its mass, the sum of all forces applied to it, and an interval of time of interest, ∆t), then we know the future velocity of the air parcel. We can repeat a similar operation with the other conservation laws.

Thus, if we divide the atmosphere into individual air parcels and choose a time interval ∆t of 10 minutes, for example, we could, in theory, calculate the future properties of all the air parcels 10 minutes from now. And if we repeated the calculation by 10 minute intervals, we could extend our forecast to 1 hour, 6 hours, or any number of days. In order to make this calculation, or to use other approaches that

(c)

Figure 13.1. Three examples of grids at increasingly higher resolution. (a) The whole globe covered with a 10° grid. (b) The USA with a 1° grid. (c) Washington State with a 0.1° grid.

do not involve following air parcels, we evaluate the evolution of individual variables (such as velocity, temperature, humidity) at points on a grid that covers a region of interest (Figure 13.1). Coding these equations into a computer at each point of our grid and letting the application run provides a deterministic forecast: a single forecast, using a single estimate of the current state of the atmosphere. As we will see, we cannot stop there, because we have not yet measured

Chapter summaries to review the material 226 learned

Weather Forecasting

These forecasts usually associate large-scale patterns with changes in climatological (historical average) probabilities for what normally happens at the particular time of year. A persistent upper-level ridge, for example, is often indicative of a greater chance of clear skies, and weaker winds. The presence of an upper-level trough, however, may suggest a greater chance of clouds and precipitation. Forecasters might mention that chances for precipitation are higher or lower, without explicitly detailing the features that will produce the precipitation. Figure 13.8 shows such an example of a 6–10-day outlook and an 8–14-day outlook issued by the NOAA Climate Prediction Center, and corresponding to our February cyclone.

These longer-term forecasts are becoming more accurate and valuable as computer power increases and computer models improve our ability to perform ensemble forecasting.

13.6.2 Seasonal Outlook Similarly, forecasters can use the departures from climatology based on long-term indicators such as teleconnections to issue a seasonal outlook several months in advance. The main source of predictive skill at these timescales is associated with ENSO. For example, increasing sea surface temperatures in the Central Pacific Ocean, announcing the return of an El Niño event, can be the basis for predicting higher chances of rain and flooding for California in late winter (i.e., higher chances than average).

Here again, forecasters are not trying to forecast the weather on a specific day three months in advance using the techniques described for short-term forecasting. They are mainly using statistical techniques to determine if weather variables will be average (climatology), higher than climatology, or lower than climatology (i.e, the seasonal-average conditions over a long period of time, typically 30 years).

These seasonal outlooks can be valuable for farming, energy planning, recreational activities, and risk preparedness, for example. Again, with increases in computer power, numerical models are now being used, coupled to ocean prediction models, to make seasonal forecasts.

Summary The atmosphere is a fluid that obeys the laws of physics, and the mathematical form of these laws can be encoded for computers to solve, yielding a future prediction given the current state of the atmosphere. This involves calculating the evolution of individual variables (such as velocity, temperature, humidity) at certain points on a grid that covers a region of interest. Weather observations provide the starting point for the forecast.

Our ability to forecast future weather is limited because: f the observations used as initial conditions cannot be known exactly; f certain small-scale processes are not resolved by the model grid and must be parameterized, which translates into uncertainty and model errors; and f small errors in the initial conditions grow in time to become large errors, ultimately completely contaminating the forecast.

The limit of weather forecasting skill is thought to be about two weeks, even if we know the initial state almost perfectly. Ensemble forecasts, where many forecasts are generated from slightly different initial conditions, and by different forecast models, provide the tool by which we measure the loss of skill in the forecast. Typically, small differences among the different forecasts grow quickly, so by 5–10 days we cannot usually speak with confidence on details in the forecast, but often there are broader patterns of agreement for which we do have forecast confidence.

Persistent features allow weather forecasters to issue medium-range forecasts out to two weeks.

In spite of the chaotic nature of the atmosphere, forecasting its future state is possible on longer timescales because there exist forcings that affect, or constrain, the dynamics of the atmosphere and therefore the statistics of weather events (e.g., global warming and ENSO). These forcings allow forecasters to issue a seasonal outlook several months in advance and to make forecasts of climate for several decades. Again, individual weather features cannot be predicted on these timescales, but we can often make useful predictions about how the weather, on average, will depart from historical averages (climatology).

“In these days of dumbed-down science curricula, it’s heartening to see this serious, lucid, and engaging textbook for non-majors: concise, yet containing a wealth of interesting material, well explained and beautifully illustrated.”

Professor John M. Wallace, University of Washington “This is a wide-ranging and beautifully illustrated book that would be an ideal text to accompany a university introductory course on Weather and Climate Science. The book is also accessible to those with a more general interest in the weather, who could skip the text boxes which explain the scientific aspects in more detail.”

Dr. Pete Inness, University of Reading “Weather: A Concise Introduction is aptly named and provides exactly that. This text presents the fundamental concepts of meteorology... an excellent starter for those students just entering the field as well as a welcoming overview for students taking a general education course.”

Tim Canty, PhD., University of Maryland From a world-renowned team at the Department of Atmospheric Sciences at the University of Washington, Seattle, Weather: A Concise Introduction is an accessible and beautifully illustrated text covering the foundations of meteorology in a concise, clear, and engaging manner. Designed to provide students with a strong foundation in the physical, dynamical, and chemical processes taking place in the atmosphere, this introductory textbook will appeal to students with a wide range of mathematical and scientific backgrounds.

Featuring a single case study of a mid-latitude cyclone, which is woven throughout the text, students are able to: • Return to a familiar case over and over again to understand different concepts, such as how upper-level features interact with the surface cyclone, creating circulations that produce characteristic patterns of cloud and precipitation.

• Investigate the physical processes (such as matter, energy, and motion) that underlie the atmosphere at work, one step at a time. By doing so, students will be able to explain the weather phenomena they experience around the year and throughout the world.

• Build a systematic picture of weather systems by observing and synthetizing the information available, and to gain an appreciation of the fundamental aspects of weather forecasting.

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