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ABSTRACT:Research and development of new verification strategies and reassessment of traditional forecast verification methods has received a great deal of attention from the scientific community in the last decade. This scientific effort has arisen from the need to respond to changes encompassing several aspects of the verification process, such as the evolution of forecasting systems, or the desire for more meaningful verification approaches that address specific forecast user requirements. Verification techniques that account for the spatial structure and the presence of features in forecast fields, and which are designed specifically for high-resolution forecasts have been developed. The advent of ensemble forecasts has motivated the re-evaluation of some of the traditional scores and the development of new verification methods for probability forecasts. The expected climatological increase of extreme events and their potential socio-economical impacts have revitalized research studies addressing the challenges concerning extreme event verification. Verification issues encountered in the operational forecasting environment have been widely discussed, verification needs for different user communities have been identified, and models to assess the forecast value for specific users have been proposed. Proper verification practice and correct interpretation of verification statistics has been extensively promoted with recent publications and books, tutorials and workshops, and the development of open-source software and verification tools. This paper addresses some of the current issues in forecast verification, reviews some of the most recently developed verification techniques, and provides recommendations for future research.

Precipitation forecasts from mesoscale numerical weather prediction (NWP)

Even as operational numerical weather prediction models become more accurate, improvements in rain forecasts are hard to come by. O f all the weather elements for which forecasts are provided to the public, rainfall is perhaps of the greatest interest. While most people simply want to know whether they will need an umbrella that day, there is growing demand from industry, agriculture, government, and many other sectors for more detailed rainfall predictions. Unfortunately, rainfall is certainly among the most difficult weather elements to predict correctly. Rainfall has greater spatial and temporal variability than most other meteorological quantities of interest. Many processes can lead to rain, including large-scale ascent of moist air, convection caused by heating of moist air near the surface, con-AMERJCAN METEOROLOGICAL SOCIETY vergence of moist air in a baroclinic zone, and orographic lifting. These processes must all be represented in numerical weather prediction (NWP) models, whose output forms the basis for most rainfall forecasts. It is of great interest to assess how well we can meet the need for timely and accurate rainfall forecasts using operational NWP models.In the mid-1990s, the Working Group on Numerical Experimentation (WGNE), established under the World Meteorological Organisation's World Climate Research Programme (WCRP) and Commission for Atmospheric Sciences (CAS), turned its attention to quantitative precipitation forecasts (QPFs). Since accurate prediction of rainfall depends critically on the accurate prediction of atmospheric motion and moisture content, it is reasonable to expect that a good forecast of rainfall over a large domain indicates a good forecast overall. Indeed, many operational centers use QPF skill as a critical measure of model health. Accumulated precipitation can be verified (albeit imperfectly, given its highly variable nature) using rain gauge networks. Knowledge of a model's QPF behavior not only helps model developers but also users of the QPFs to understand the reliability of the model output.At the 10th annual WGNE meeting it was recommended that QPFs from several operational NWP models be evaluated in different areas of the globe

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