Application Of S2S Multi-model Data In Marine Continental Region And Extended-term Precipitation Forecasting In My Country

Extended-range prediction of precipitation lies between weather forecast and climate prediction,in which initial conditions as well as boundary conditions are extremely important,and it is an urgent problem that necessarily needs to be solved in recent years.In this study,an equally weighted multi-model ensemble(MME)was constructed to evaluate the extended-range prediction of precipitation over the Maritime Continent(MC)during boreal summer using the historical reforecast data from five Sub-seasonal to Seasonal(S2S)models,including the China Meteorological Administration(CMA),the European Centre for Medium-Range Weather Forecasts(ECMWF),Environment and Climate Change Canada(ECCC),the National Centers for Environmental Prediction(NCEP),and the Met Office(UKMO).The relationship between MME in this region and the two types of intraseasonal oscillation(ISO)is preliminarily explored,and then this method extended to the extended-range prediction of precipitation over the China during boreal summer.And based on this,a non-filtering method and correlation analysis are used to select the 10-60 day low-frequency component of predictors for extended precipitation,prediction experiment of the summer precipitation over the middle and lower reaches of the Yangtze River are studied by developing a dynamic spatial-temporal projection model(DSTPM)based on S2 S model prediction.This paper has got several conclusions below:(1)MME can significantly improve the prediction skill of extended-range precipitation in the MC.Both the Temporal Correlation Coefficient(TCC)skill and the Anomaly Correlation Coefficient(ACC)skill reached 0.6 in lead week 1,rapidly dropped the following week,did not exceed 0.2 in lead week 3,and then lost their significance.According to the spatial distribution of the TCC skill,it shows higher prediction skill near the Equator than in the north at 10°N,and the skill over ocean is more significant than that over land after lead week 3.ACC skills exhibit significantly inter-annual variations,and the time average of the ACC is relatively higher than the spatial average of the TCC.The prediction ability of the MME improves significantly as the total number of model members increases.The prediction performance of the MME depends not only on the diversity of models but also on the number of model members.Moreover,the MME is particularly sensitive to the intensity and phase of Boreal Summer Intraseasonal Oscillation 1(BSISO1)with the highest skills appearing at initial phases 1 and 5.(2)The performances of models in China are different,the performance of MME is relatively better than that of individual models,its TCC skill decreases slowly as the lead time increases,it is significant over the most of the regions of China in lead pentad 3.As shown in the MME,significant-skill regions are concentrated near the Qinghai Province,Gansu Province and Guizhou Province although all individual models lost their prediction ability in lead pentad 4.The ACC skill is similar to the TCC's,the ACC skill of the MME is still positive for most cases.In addition,the time average of ACC is close to the spatial average of TCC,which is significantly different from the MC.(3)The different predictors are used,the prediction performance of DSTPM is different,and the prediction skill of selecting outgoing long-wave radiation(OLR)as a predictor is the highest.The skill of the ensemble of predictors significantly decreases with lead pentad increasing.Compared with the direct prediction of the dynamic model,the DSTPM effectively improves the prediction skills on the south of the middle reaches of the Yangtze River.Besides,this study also found that the DSTPM has a good ability to simulate the average precipitation in the first two pentads,and insufficient ability to simulate extreme precipitation.