Seamless Prediction in China: A Review

Seamless prediction is a weather-climate integrated prediction covering multiple time scales that include days,weeks,months,seasons,years,and decades.Seamless prediction can provide different industries with information such as weather conditions and climate variations from the next few days to years,which have important impacts on economic and social development and important reference value for short-,medium-and long-term decision-making and planning of the country.Therefore,seamless prediction has received widespread attention from the international scientific community recently.As Chinese scientists have also carried out relevant research,this paper reviews the research in China on developments and applications of seamless prediction methods and prediction systems in recent years.Among them,the main progress of seamless prediction methods studies is reviewed from four aspects:short-and medium-range weather forecasting,subseasonal-to-seasonal,seasonal-to-interannual,and decadal climate prediction.In terms of development and application of seamless prediction systems,the main achievements made by meteorological operational departments,scientific institutes,and universities in China in recent years are reviewed.Finally,some of the issues in seamless prediction that need further study are discussed.

Quantitative Precipitation Forecasting Using Multi-Model Blending with Supplemental Grid Points:Experiments and Prospects in China

Quantitative Precipitation Forecast(QPF)is a challenging issue in seamless prediction.QPF faces the following difficulties:(i)single rather than multiple model products are still used;(ii)most QPF methods require long-term training samples not easily available,and(iii)local features are insufficiently reflected.In this work,a multi-model blending(MMB)algorithm with supplemental grid points(SGPs)is experimented to overcome these shortcomings.The MMB algorithm includes three steps:(1)single-model bias-correction,(2)dynamic weight MMB,and(3)light-precipitation elimination.In step 1,quantile mapping(QM)is used and SGPs are configured to expand the sample size.The SGPs are chosen based on similarity of topography,spatial distance,and climatic characteristics of local precipitation.In step 2,the dynamic weight MMB uses the idea of ensemble forecasting:a precipitation process can be forecast if more than 40% of the models predict such a case;moreover,threat score(TS)is used to update the weights of ensemble members.Finally,in step 3,the number of false alarms of light precipitation is reduced,thus alleviating unreasonable expansion of the precipitation area caused by the blending of multiple models.Verification results show that using the MMB algorithm has effectively improved the TS and bias score(BS)for blended 6-h QPF.The rate of increase in TS for heavy rainfall(25-mm threshold)reaches 20%-40%;in particular,the improvement has reached 47.6% for forecast lead time of 24 h,compared with the ECMWF model.Meanwhile,the BS is closer to 1,which is better than any single-model forecast.In sum,the QPF using MMB with SGPs shows great potential to further improve the present operational QPF in China.