当前位置： 首页»科研成果» 2013 Evaluating the predictive skill of post-processed NCEP GFS ensemble precipitation forecasts in China's Huai river basin 段青云、叶爱中、缪驰远

 

Y. Liu¹, Q. Duan^2,*, L. Zhao³, A. Ye², Y. Tao⁴, C. Miao², X. Mu⁴, J. C. Schaake⁵

 

¹ Department of Atmospheric Science, Chengdu University of Information Technology, Chengdu, China;

² State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science Beijing Normal University, Beijing, China;

³ Public Meteorological Service Center and National Meteorological Center, Chinese Meteorological Administration, Beijing, China;

⁴ College of Mathematical Science, Beijing Normal University, Beijing, China;

⁵ Retired, National Weather Service/NOAA, Silver Spring, MD, USA.

 

Abstract

The National Center for Environmental Predictions (NCEP) has produced an ensemble meteorological reforecast product by using a fixed version of Global Forecast System (GFS) ensemble prediction system since 1 January 1979. The 15-member ensemble product, with a global coverage at a 2.5° × 2.5° spatial resolution and a 14-day lead time, has been used successfully by the River Forecast Centers of the National Weather Service (NWS) to produce basin scale precipitation and temperature ensemble forecasts in the US for several years now. This study evaluates the predictive skill of post-processed ensemble forecasts based on GFS precipitation reforecast in China's Huai river basin. The evaluation is carried out in 15 sub-areas of the Huai river basin and covers the 1/1/1981–31/12/2003 period. The Ensemble Pre-Processing system version 3 (EPP3), developed at NWS, is used to develop joint probability distributions between forecasted ensemble mean precipitation and corresponding observations and to generate individual ensemble members that preserve space–time correlation of the observed precipitation data. Several statistical verification measures are used to quantify the goodness of fit between post-processed (i.e. EPP3 processed) ensemble mean and observation and to assess the ensemble spread. Results indicate that the post-processed forecasts have meaningful predictive skill for the first few days for ensemble daily precipitation forecasts. Predictive skill of ensemble forecasts of cumulative precipitation for lead times up to 14 days are significant. The forecast skill is highly dependent on seasonality, with relatively lower skills seen for wet summer season, when convective storm patterns dominate, as compared with other seasons. The predictive skill of the post-processed ensemble precipitation is much better than the raw forecasts and the climatological ensemble forecasts. The results from this study suggest that the NCEP's GFS reforecasts can be a valuable resource for places other than the US.

 

KEY WORDS: ensemble precipitation forecasting; hydrologic ensemble prediction; Huai river basin; ensemble verification; GFS reforecast product

 

PUBLISHED BY: HYDROLOGICAL PROCESSES, 2013, 27 (1): 57-74.

 

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