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Evaluation of monthly precipitation forecasting skill of the National Multi‐model Ensemble in the summer season
Author(s) -
Wang Hui
Publication year - 2013
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.9957
Subject(s) - precipitation , environmental science , climatology , watershed , meteorology , quantitative precipitation forecast , streamflow , forecast skill , atmospheric research , water resources , climate change , drainage basin , computer science , geography , geology , ecology , oceanography , cartography , machine learning , biology
As a test bed, the National Multi‐model Ensemble (NMME) comprises seven climate models from different sources, including the National Oceanic and Atmospheric Administration, the National Aeronautics and Space Administration, the National Center for Atmospheric Research and the International Research Institute for Climate and Society. It provides 89 ensemble members of precipitation forecasts at different lead times. Precipitation forecasting from climate models has been applied to provide streamflow forecasts, and its utility in water resource system operation has been demonstrated in the literature. In this study, 1‐month‐ahead precipitation forecasts from NMME are evaluated for 945 grid points of 1°‐by‐1° resolution over the continental USA using mean square error and rank probability score. The temporal and spatial variabilities of the forecasting skill over different months of the summer season are discussed. The relation between forecasting uncertainty and observed precipitation is investigated. Such analyses have implications for monthly operational forecasts and water resource management at the watershed scale. Copyright © 2013 John Wiley & Sons, Ltd.