J. L. García-Franco, Chia-ying Lee, S. Camargo, M. Tippett, Daehyun Kim, A. Molod, Y. Lim
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引用次数: 1
Abstract
This study evaluates the representation of tropical cyclone precipitation (TCP) in reforecasts from the Subseasonal-to-Seasonal (S2S) Project. The global distribution of precipitation in S2S models shows relevant biases in the multi-model mean ensemble which are characterized by wet biases in total precipitation (P) and TCP, except for the Atlantic. The TCP biases can contribute more than 50% of total P biases in basins such as the Southern Indian Ocean and South Pacific. The magnitude and spatial pattern of these biases exhibit little variation with lead time. The origins of TCP biases can be attributed to biases in the frequency of tropical cyclone occurrence (TCF). The S2S models simulate too few TCs in the Atlantic and Western North Pacific and too many TCs in the Southern Hemisphere and Eastern North Pacific. At the storm-scale, the average peak precipitation near the storm center is lower in the models than observations due to a too high proportion of weak TCs. However, this bias is offset in some models by higher than observed precipitation rates at larger radii (300-500 km). An analysis of the mean TCP for each TC at each grid-point reveals an overestimation of TCP rates, particularly in the near-equatorial Indian and Western Pacific Oceans. These findings suggest that the simulation of TC occurrence and the storm-scale precipitation require better representation in order to reduce TCP biases and enhance the subseasonal prediction skill of mean and extreme total P.
期刊介绍:
Weather and Forecasting (WAF) (ISSN: 0882-8156; eISSN: 1520-0434) publishes research that is relevant to operational forecasting. This includes papers on significant weather events, forecasting techniques, forecast verification, model parameterizations, data assimilation, model ensembles, statistical postprocessing techniques, the transfer of research results to the forecasting community, and the societal use and value of forecasts. The scope of WAF includes research relevant to forecast lead times ranging from short-term “nowcasts” through seasonal time scales out to approximately two years.