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A Methodology for Determining River Forecasting Skill using Monthly Cumulative Distribution Functions of Mean Daily Flow
Noreen O. Schwein, NOAA/NWS, Kansas City, MO
The National Weather Service (NWS) has provided river forecasts for navigation and flood warning since the mid 1800s. The NWS mission includes the protection of life and property and enhancement of the national economy. In so doing, the NWS strives to deliver quality forecasts with increasing accuracy. One method established to improve forecasts is post-analysis or verification of forecast data compared to observed data. The typical method used for determining the accuracy of river forecasts has been to pair observed and forecast values in order to calculate statistics such as root mean-squared error (RMSE) and mean absolute error. Such calculations are certainly useful in comparing magnitudes of error, with the least amount of error showing the greatest amount of accuracy. However, river conditions vary greatly with the weather and many other hydrologic and geologic variables, resulting in a wide range of flows. Those conditions that are common, or in line with climatology, might be considered "easier" to forecast as opposed to more rare events. Therefore, a measure that takes the rarity of the event into account is needed.
The purpose of this paper is to develop a verification skill score and test its usefulness with more traditional methods of verification. This score, called the Linear Error in Probability Space (LEPS)-based skill score, will be determined using monthly cumulative distribution functions (CDF), or frequency curves, of mean daily flow for six separate river forecast points. The six points will have some similarities and differences in basin size, climate regime, and bed slope of the river. Results using the LEPS-based skill score will be compared to the RMSE to provide the forecaster or agency with additional information regarding the quality of the forecast.
Session 1, forecast evaluation
Monday, 14 January 2002, 9:30 AM-3:30 PM
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