Advances in Avalanche Forecasting 2012

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LABINIX – a tool for regional avalanche danger assessment from meteorological data based on regression Martin Vojtek* Military Weather Centre, Slovak Air Force INTRODUCTION Computer-assisted models help the avalanche forecaster to gain confidence and reduce the risk of failure caused by the human factor. LABINIX is a simple software tool that calculates daily avalanche danger level from meteorological data. The name comes from Latin: LABIna = avalanche and NIX = snow. So far, the area of interest covers the High Tatra Mts. (~ 250 km2), and the measurements from 7 meteorological stations (Figure 1) are used as input. METHOD The method based on stepwise regression is described in detail in author‘s PhD thesis (Vojtek, 2010). Beside the basic input meteorological variables, also some nonlinear, mixed and cumulative variables (up to 7 days back) were defined. Another useful element – snow water equivalent (SWE) – is measured only once a week in Slovakia. Therefore, a method developed by Němec et al. (2003) is adopted in LABINIX to estimate the daily SWE values. The dataset was divided into 4 subsets, depending on the water content of avalanches fallen (dry, wet and moist/combined) and the rest consisting of non-avalanche days. The best set of avalanche-danger-level-related variables was selected by stepwise regression using both stepping direction approach in Splus (Mathsoft, 1999). The models are signed by G (general), D (dry), M (moist) or W (wet), followed by number of stations and number of variables used. Regression coefficients were calculated on the period 1994-2002 plus some days with estimated avalanche danger levels 3 and more (selected from historical records back to 1980). RESULTS The best model D7x44 predicts 74% of days correctly on testing dataset (2003-2008). The __________________________ * Corresponding author address:

Martin Vojtek Military Weather Centre, Slovak Air Force Na Slatinkách 7 962 31 Sliač, SLOVAKIA tel: +421 949 338 609 e-mail:

error is not greater than 1.5 for the rest of the days. If the best model is selected for each month, the accuracy ranges between 66% in May to 82 % in December, (refer to Table 1). The main drawbacks: 1. if a meteorological station moves, the regression coefficients must be recalculated; 2. if a meteorological element is missing, the avalanche danger level cannot be calculated until the value is estimated / completed later; 3. a l l models have problem wi t h underforecasting of the avalanche danger level 3 (considerable). On the other hand, the avalanche danger level can be forecasted for the next 2-3 days if data for the following days are inserted from a numerical weather prediction model (e.g. ALADIN). LABINIX can be thus useful for skitour planning or earlier warning for extreme avalanches. In order to use the LABINIX operationally, further recalculations were performed due to lack of some meteorological elements in realtime. At the moment, LABINIX is capable to load and decode 2 special meteorological messages / bulletins (INTER and INTER TATRY, available at 06:50 UTC), to check for missing data, and to calculate the avalanche danger level (using M7x60 designed for moist avalanche days). The tool offers a forecaster variety of charts, e.g. contributions of meteorological elements to the calculated avalanche danger level (Figure 2). The best avalanche-related elements are: snow depth, air temperature, relative humidity, snow water equivalent, SDRRH (introduced by Salway, 1979), the daily amplitude of air temperature, and new snow height. CONCLUSIONS There is a growing potential for numerical avalanche forecasting in Slovakia thank to increasing records of avalanche, meteorological, snow profiles and stability data. Automatic weather stations introduced in 2007/08 will be another useful input. LABINIX is going to be used in the following winter as experimental tool, and remains the subject of further development. If it proves to be helpful, it can be applied to other avalanche regions in Slovakia.

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