The GRAPES-TCM is used to make ensemble prediction experiments for typhoon track.Three kinds of ensemble schemes are designed for the experiments.A total of 109 experiments are made for the nine typhoons in 2011 and t...The GRAPES-TCM is used to make ensemble prediction experiments for typhoon track.Three kinds of ensemble schemes are designed for the experiments.A total of 109 experiments are made for the nine typhoons in 2011 and the integral time is 72 h.The experiment results are shown as follows.In the three ensemble schemes,on the whole,scheme 1 has the best track prediction.Its average absolute track error and overall deviations of typhoon moving speed and moving direction are all the smallest in the three schemes.For both scheme 1 and scheme 2,they are all smaller than those of their control predictions.Both of their ensemble predictions show superiority to their deterministic predictions.Overall,compared with the observations,the typhoon moving directions of the three schemes mainly skew to the right,and in the late integration they mainly tend to be relatively slow.In the three schemes,the track dispersion of scheme 1 is the largest and that of scheme 3 the smallest.In scheme 1 it is much larger than in schemes 2 and 3.The difference of dispersion between scheme 2 and scheme 3 is small.The track dispersions of the three schemes are all much smaller than their rational dispersions.Compared with the eight domestic and overseas operational numerical weather prediction(NWP) models,scheme 1 has better predictions than the other seven operational models except ECMWF NWP model.Scheme 1 has the value of operational application.展开更多
Hybrid data assimilation combines a conventional 3-D or 4-D variational system with background error covariance(BEC)generated from ensemble forecast systems.In order to achieve better BEC,three perturbation schemes,na...Hybrid data assimilation combines a conventional 3-D or 4-D variational system with background error covariance(BEC)generated from ensemble forecast systems.In order to achieve better BEC,three perturbation schemes,namely,the random combination of multiple physical paramterization schemes(referred to as MP),the MP plus stochastical perturbation on physical process tendencies(MP-SPPT),and the unified perturbation of stochastic physics with bias correction(UPSB,proposed by the authors of this paper in a previous work),were first used in a regional ensemble model,i.e.,the Global and Regional Assimilation and Prediction System-Regional Ensemble Prediction System(GRAPES-REPS),and the BECs thus obtained were compared for 7-day ensemble forecasts.The results show that UPSB,which is in fact an MP-SPPT but with the systematic model bias removed,has a better consistency,i.e.,the ratio between root-mean-square error(RMSE)and ensemble spread is much closer to 1,especially at low model levels,compared to the other two schemes.Moreover,the BEC derived from UPSB captured more reasonable distributions of forecast errors.Second,performance of a hybrid data assimilation system(the GRAPES-MESO hybrid En-3DVar)was evaluated by using the BECs from the three perturbation schemes for 7-day hybrid data assimilation forecasts,and thus disclosing the effect of the model bias correction(assuming that the random stocastical features are in general offset in the three perturbation schemes)on the hybrid system forecasts.A covariance weight of 0.8 was prescribed,and this value was determined through sensitivity experiments.The forecast results from the hybrid data assimilation system show that UPSB reduced the false correlation between distant points.The quality of analysis fields of the UPSB scheme shows visible improvement,i.e.,the analysis fields produced by UPSB have much smaller RMSEs than those of the other two schemes,at all vertical model levels.The quality of the hybrid data assimilation forecast fields was also improved by this scheme.Furthermore,the improvement was much greater in the early stage of the assimilation cycle than in the late stage.Generally,the quality of the hybrid data assimilation of GRAPES-MESO hybrid En-3DVar could be efficiently improved by the model bias correction in the UPSB scheme.展开更多
基金National Natural Science Foundation of China(41575108,41275067,41475082,41475059)Special Scientific Research Fund of Meteorological Public Welfare of China(GYHY201506007)
文摘The GRAPES-TCM is used to make ensemble prediction experiments for typhoon track.Three kinds of ensemble schemes are designed for the experiments.A total of 109 experiments are made for the nine typhoons in 2011 and the integral time is 72 h.The experiment results are shown as follows.In the three ensemble schemes,on the whole,scheme 1 has the best track prediction.Its average absolute track error and overall deviations of typhoon moving speed and moving direction are all the smallest in the three schemes.For both scheme 1 and scheme 2,they are all smaller than those of their control predictions.Both of their ensemble predictions show superiority to their deterministic predictions.Overall,compared with the observations,the typhoon moving directions of the three schemes mainly skew to the right,and in the late integration they mainly tend to be relatively slow.In the three schemes,the track dispersion of scheme 1 is the largest and that of scheme 3 the smallest.In scheme 1 it is much larger than in schemes 2 and 3.The difference of dispersion between scheme 2 and scheme 3 is small.The track dispersions of the three schemes are all much smaller than their rational dispersions.Compared with the eight domestic and overseas operational numerical weather prediction(NWP) models,scheme 1 has better predictions than the other seven operational models except ECMWF NWP model.Scheme 1 has the value of operational application.
基金Supported by the National Natural Science Foundation of China(41605082 and 91437113)Jiangsu Province Postgraduate Research and Innovation Program(KYCX17_0869)。
文摘Hybrid data assimilation combines a conventional 3-D or 4-D variational system with background error covariance(BEC)generated from ensemble forecast systems.In order to achieve better BEC,three perturbation schemes,namely,the random combination of multiple physical paramterization schemes(referred to as MP),the MP plus stochastical perturbation on physical process tendencies(MP-SPPT),and the unified perturbation of stochastic physics with bias correction(UPSB,proposed by the authors of this paper in a previous work),were first used in a regional ensemble model,i.e.,the Global and Regional Assimilation and Prediction System-Regional Ensemble Prediction System(GRAPES-REPS),and the BECs thus obtained were compared for 7-day ensemble forecasts.The results show that UPSB,which is in fact an MP-SPPT but with the systematic model bias removed,has a better consistency,i.e.,the ratio between root-mean-square error(RMSE)and ensemble spread is much closer to 1,especially at low model levels,compared to the other two schemes.Moreover,the BEC derived from UPSB captured more reasonable distributions of forecast errors.Second,performance of a hybrid data assimilation system(the GRAPES-MESO hybrid En-3DVar)was evaluated by using the BECs from the three perturbation schemes for 7-day hybrid data assimilation forecasts,and thus disclosing the effect of the model bias correction(assuming that the random stocastical features are in general offset in the three perturbation schemes)on the hybrid system forecasts.A covariance weight of 0.8 was prescribed,and this value was determined through sensitivity experiments.The forecast results from the hybrid data assimilation system show that UPSB reduced the false correlation between distant points.The quality of analysis fields of the UPSB scheme shows visible improvement,i.e.,the analysis fields produced by UPSB have much smaller RMSEs than those of the other two schemes,at all vertical model levels.The quality of the hybrid data assimilation forecast fields was also improved by this scheme.Furthermore,the improvement was much greater in the early stage of the assimilation cycle than in the late stage.Generally,the quality of the hybrid data assimilation of GRAPES-MESO hybrid En-3DVar could be efficiently improved by the model bias correction in the UPSB scheme.
