Based on North American Multi Model Ensemble(NMME)and monthly average soil moisture return data of five models in Xinjiang region during 1982-2010,using ERA-interim reanalysis soil moisture data for verification,the a...Based on North American Multi Model Ensemble(NMME)and monthly average soil moisture return data of five models in Xinjiang region during 1982-2010,using ERA-interim reanalysis soil moisture data for verification,the abilities of five models and simple ensemble mean to predict winter soil moisture in Xinjiang region were evaluated.The results showed that forecasting skill of CanCM4 in single model was the best,while forecasting skill of CESM1 was the lowest.With the increase of lead time,the forecasting skills of all models gradually decreased,containing NMME.But NMME gradually reflects the advantages in longer lead time,and its forecasting skills in Lead1-Season,Lead2-Season and Lead3-Season were higher than all single model,illustrating that multi model simple ensemble mean is more useful for predicting soil moisture in longer lead time,which is helpful for improving prediction skills.In spatial distribution,correlation of all models in winter in southwest Xinjiang(Tarim Basin)and northwest region was worse.CanCM3 and CanCM4 models had higher correlation in northern Xinjiang.Meanwhile,root mean square error was also smaller in corresponding region and larger in southwest region.Forecasting skills of GFDL-CM2p1 and CESM1 in eastern area of central Xinjiang were higher,and root mean square error in this area was also smaller.This paper also could lay the foundation for further studying prediction of soil drought.展开更多
Forecast skill (Anomaly Correlated Coefficient, ACC) is a quantity to show the forecast quality of the products of numerical weather forecasting models. Predicting forecast skill, which is the foundation of ensemble f...Forecast skill (Anomaly Correlated Coefficient, ACC) is a quantity to show the forecast quality of the products of numerical weather forecasting models. Predicting forecast skill, which is the foundation of ensemble forecasting, means submitting products to predict their forecast quality before they are used. Checking the reason is to understand the predictability for the real cases. This kind of forecasting service has been put into operational use by statistical methods previously at the National Meteorological Center (NMC), USA (now called the National Center for Environmental Prediction (NCEP)) and European Center for Medium-range Weather Forecast (ECMWF). However, this kind of service is far from satisfactory because only a single variable is used with the statistical method. In this paper, a new way based on the Grey Control Theory with multiple predictors to predict forecast skill of forecast products of the T42L9 of the NMC, China Meteorological Administration (CMA) is introduced. The results show: (1) The correlation coefficients between 'forecasted' and real forecast skill range from 0.56 to 0.7 at different seasons during the two-year period. (2) The grey forecasting model GM(1,8) forecasts successfully the high peaks, the increasing or decreasing tendency, and the turning points of the change of forecast skill of cases from 5 January 1990 to 29 February 1992.展开更多
文摘Based on North American Multi Model Ensemble(NMME)and monthly average soil moisture return data of five models in Xinjiang region during 1982-2010,using ERA-interim reanalysis soil moisture data for verification,the abilities of five models and simple ensemble mean to predict winter soil moisture in Xinjiang region were evaluated.The results showed that forecasting skill of CanCM4 in single model was the best,while forecasting skill of CESM1 was the lowest.With the increase of lead time,the forecasting skills of all models gradually decreased,containing NMME.But NMME gradually reflects the advantages in longer lead time,and its forecasting skills in Lead1-Season,Lead2-Season and Lead3-Season were higher than all single model,illustrating that multi model simple ensemble mean is more useful for predicting soil moisture in longer lead time,which is helpful for improving prediction skills.In spatial distribution,correlation of all models in winter in southwest Xinjiang(Tarim Basin)and northwest region was worse.CanCM3 and CanCM4 models had higher correlation in northern Xinjiang.Meanwhile,root mean square error was also smaller in corresponding region and larger in southwest region.Forecasting skills of GFDL-CM2p1 and CESM1 in eastern area of central Xinjiang were higher,and root mean square error in this area was also smaller.This paper also could lay the foundation for further studying prediction of soil drought.
文摘Forecast skill (Anomaly Correlated Coefficient, ACC) is a quantity to show the forecast quality of the products of numerical weather forecasting models. Predicting forecast skill, which is the foundation of ensemble forecasting, means submitting products to predict their forecast quality before they are used. Checking the reason is to understand the predictability for the real cases. This kind of forecasting service has been put into operational use by statistical methods previously at the National Meteorological Center (NMC), USA (now called the National Center for Environmental Prediction (NCEP)) and European Center for Medium-range Weather Forecast (ECMWF). However, this kind of service is far from satisfactory because only a single variable is used with the statistical method. In this paper, a new way based on the Grey Control Theory with multiple predictors to predict forecast skill of forecast products of the T42L9 of the NMC, China Meteorological Administration (CMA) is introduced. The results show: (1) The correlation coefficients between 'forecasted' and real forecast skill range from 0.56 to 0.7 at different seasons during the two-year period. (2) The grey forecasting model GM(1,8) forecasts successfully the high peaks, the increasing or decreasing tendency, and the turning points of the change of forecast skill of cases from 5 January 1990 to 29 February 1992.
