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原文传递 A Methodology for Determining River Forecasting Skill using Monthly Cumulative Distribution Functions of Mean Daily Flow

题名：	A Methodology for Determining River Forecasting Skill using Monthly Cumulative Distribution Functions of Mean Daily Flow
作者：	Noreen Schwein National Weather Service, Central Region Headquarters Hydrologic Services Division
关键词：	distribution；forecast；skill；function；daily；mean；sting；method；flow；ermi
摘要：	Introduction...The National Weather Service (NWS) has provided river forecasts for navigation a*.d flood warning since the mid 1800s (National Oceanic and Atmospheric Administration (NOAA), 1994). 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 completing a post-analysis or verification of the forecast data compared to observed data. The typical method used for determining the accuracy of a river forecast 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 from one event to another at one forecast point, but they cannot easily be compared from one location to another. For example, one river may have variations in flow from thousands to tens of thousands while another varies from hundreds to thousands. An RMSE of 1000 cfs may be a “good” forecast for the former river, where an RMSE of 1000 cfs for the latter would not be. There is a need to have a measure that is normalized from one river to the next, so that the agency can make fair assessments and prioritize identified needs. Statistics involving error magnitude also do not take the rarity of the event into account. 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 compared to more rare events. The Linear Error in Probability Space (LEPS)-based skill score (Wilks, 1995) could take this difficulty into account and add valuable information to verification. Additionally, the LEPS-based skill score is not dependent on the scale of the variable and could be used as a normalized score.
报告类型：	科技报告