Tuesday, 14 January 2020
Hall B1 (Boston Convention and Exhibition Center)
Season rainfall has an important impact on human survival and society development. But nowadays, the forecast skill of dynamic model outputs for rainfall usually preform not well. We aim to improve the seasonal forecast skill of station-scale early-summer rainfall in South China (SC) through statistical downscaling theme and BMA method in this study. The observational rainfall data from China’s 160-station monthly rainfall dataset and reanalysis data from ERA-Interim dataset provided by NCEP is used to establish the statistical downscaling models. In this progress, we obtained four models preforming well. And three real-time seasonal forecast systems, i.e., NCEP, NCC, and EC, are chose to be combined with the statistical downscaling models. The results indicate that the Statistical downscaling theme can improve the forecast skill of station-scale early-summer rainfall in SC and the forecast skill of SC average rainfall can be enhanced through forecasting the station rainfall in SC. In addition, BMA method is used to merge multiple downscaled forecasts to further improve the forecast skill of station-scale early-summer rainfall in SC.
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