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
文摘边界层参数化方案的准确性会影响模式对近地面变量和大气低层热动力结构的模拟,对雷暴等强对流天气的预报非常重要,但边界层方案内在的不确定性使得单一预报具有局限性。为了提高对流尺度数值模式中边界层方案的预报效果,基于WRF(The Weather Research and Forecasting Model)模式,应用随机参数扰动(SPP)方法对Mellor-Yamada-Nakanishi-Niino(MYNN)边界层方案中重要的3个不确定参数进行扰动,探究了该方法对北京地区一次雷暴过程模拟的影响。同时考虑了对流尺度集合预报系统的特点,调整随机参数扰动方法的3个参量(去相关时间尺度、空间尺度和格点标准差)探究了对流尺度中对MYNN方案参数进行扰动的最优设置。结果显示:随机扰动MYNN边界层方案参数(SPPM)方法可以有效提高近地面变量和700 hPa以下低层变量的离散度,同时提高了短时强降水位置和强度的预报技巧。3个参量的试验说明,去相关时间尺度增大到12 h集合离散度有明显提高;格点标准差增大到0.20,预报技巧也略有提高;去相关空间尺度维持在默认值700 km较好,尺度过小(150 km)预报技巧明显降低。上述结果表明,在对流尺度中SPPM方法可以有效表达边界层参数化方案的不确定性,提高集合预报系统的预报技巧。
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.
