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WEPS Climate Data (Cligen and Windgen)

This document provides an overview of the Stochastic Processes used in WEPS (Wind Erosion Prediction System) for climate data generation. By employing stochastic simulation techniques like Normal and Weibull distributions, we recreate weather data that statistically mimics historical records. Rather than depending on actual data, WEPS employs statistically probable weather conditions, which is more efficient for computer storage. This approach allows for the generation of reliable climate estimates over a period of 15 years, using data from quality-controlled sources to inform estimates of wind speed, precipitation, and other factors critical to soil erosion modeling.

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WEPS Climate Data (Cligen and Windgen)

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  1. WEPS Climate Data(Cligen and Windgen)

  2. Climate Data • Climate Generation by Stochastic Process A stochastic process is one involving a randomly determined sequence of observations each of which is considered as a sample of one element from a probability distribution. By stochastically simulating a process, such as weather, one can recreate a series of weather data that statistically mimic the historic record.

  3. Consider a Normal Distribution where the Y-axis represents the probability of some measurement (height of people or wind speed) on the X-Axis.

  4. If the probabilities are cumulated, the distribution looks like the graph above. To stochastically simulate a measurement, one only needs to randomly select a probability and read the corresponding measurement (i.e., wind speed) from the X-Axis.

  5. If random numbers are repeatedly selected and measurements determined, eventually the original historic wind speed distribution will be recreated. Wind speeds typically follow a Weibull Distribution.

  6. Climate Data • Climate Generation by Stochastic Process • WEPS does not use actual data. • It uses statistically probable weather conditions. • This is more appropriate than using measured data (saves computer space) . • WEPS and WEPP use this type of data generation. • The model runs for 15 years to recreate the historical weather distribution and get a stable erosion estimate. • Two steps are used: Statistics are calculated from historic data, then the data is stochastically generated.

  7. Climate Data • WINDGEN Data • WINDGEN was developed specifically for WEPS. • The data comes from a quality-controlled hourly wind data sets. • There are 1304 stations in the 48 state data set. • Stations with less than 5 years of data were excluded to get a good representation of historical winds.

  8. Wind (Windgen) Station Data • Winds>Threshold - 8.0 m/s or 17.9 miles/hr, Units are in hours for the month. • Energy - kJ/m2/Month, stations over 1000 have fair risk of erosion. • Monthly Percent - Percent of the yearly wind energy. • Preponderance - Same as for WEQ • PWED - Prevailing Wind Erosion Direction (16 points of the compass).

  9. Climate Data • Climate Generation by Stochastic Process • Like Windgen, Cligen is a stochastic weather generator which produces daily estimates of precipitation, temperature, dewpoint, wind, and solar radiation for a single geographic point, using monthly parameters (means, SD's, skewness, etc.) derived from the historic measurements.

  10. Climate Data • Temp, Rainfall, Radiation, Dew point are used. • Wind, speed and direction are not used) • CLIGEN

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