The future climate dynamical downscaling method is that output of general circulation models( GCMs) is employed to provide initial conditions,lateral boundary conditions,sea surface temperatures,and initial land surfa...The future climate dynamical downscaling method is that output of general circulation models( GCMs) is employed to provide initial conditions,lateral boundary conditions,sea surface temperatures,and initial land surface conditions to regional climate models( RCMs). There are two methods of downscaling: offline coupling and online coupling. The two kinds of coupling methods are described in detail by coupling the Weather Research and Forecasting model( WRF) with the Institute of Atmospheric Physics of Chinese Academy of Sciences Atmospheric General Circulation Model Version 4. 0( IAP AGCM4. 0) in the study. And the extreme precipitation event over Beijing on July 212012 is simulated by using the two coupling methods. Results show that online coupling method is of great value in improving the model simulation. Furthermore,the data exchange frequency of online coupling has some effect on simulation result.展开更多
This paper presents results from a statistical validation of the hindcasts of surface wind by a high-reso-ution-mesoscale atmospheric numerical model Advanced Research WRF （ARW3.3）, which is set up to force the oper...This paper presents results from a statistical validation of the hindcasts of surface wind by a high-reso-ution-mesoscale atmospheric numerical model Advanced Research WRF （ARW3.3）, which is set up to force the operational coastal ocean forecast system at Indian Na- tional Centre for Ocean Information Services （INCOIS）. Evaluation is carried out based on comparisons of day-3 forecasts of surface wind with in situ and remote-sensing data. The results show that the model predicts the surface wind fields fairly accurately over the west coast of India, with high skill in predicting the surface wind during the pre-monsoon season. The model predicts the diurnal variability of the surface wind with reasonable accuracy. The model simulates the land-sea breeze cycle in the coastal region realistically, which is very clearly observed during the northeast monsoon and pre-monsoon season and is less prominent during the southwest monsoon season.展开更多
Data assimilation is the process by which measurements and model predictions are combined to obtain an accurate representation of the state of the modeled system. We implemented a data assimilation scheme called LETKF...Data assimilation is the process by which measurements and model predictions are combined to obtain an accurate representation of the state of the modeled system. We implemented a data assimilation scheme called LETKF （local ensemble transform Kalman filter） with FSUGSM （Florida State University Global Spectral Model） and made an experiment to evaluate the initial condition generated to numerical weather prediction to FSUGSM model. The LETKF analysis carries out independently at each grid point with the use of ＂local＂ observations. An ensemble of estimates in state space represents uncertainty. The FSUGSM is a multilevel （27 vertical levels） spectral primitive equation model, where the variables are expanded horizontally in a truncated series of spherical harmonic functions （at resolution T63） and a transform technique is applied to calculate the physical processes in real space The assimilation cycle runs on the period 01/01/2001 to 31/01/2001 at （00, 06, 12 and 18 GMT） for each day. We examined the atmospheric fields during the period and the OMF （observation-minus-forecast） and the OMA （observation-minus-analysis） statistics to verify the analysis quality comparing with forecasts and observations. The analyses present stability and show suitable to initiate the weather predictions.展开更多
基金Supported by the National Natural Science Foundation of China(No.61602477)China Postdoctoral Science Foundation(No.2016M601158)National Key Research and Development Program of China(No.2016YFB0200804)
文摘The future climate dynamical downscaling method is that output of general circulation models( GCMs) is employed to provide initial conditions,lateral boundary conditions,sea surface temperatures,and initial land surface conditions to regional climate models( RCMs). There are two methods of downscaling: offline coupling and online coupling. The two kinds of coupling methods are described in detail by coupling the Weather Research and Forecasting model( WRF) with the Institute of Atmospheric Physics of Chinese Academy of Sciences Atmospheric General Circulation Model Version 4. 0( IAP AGCM4. 0) in the study. And the extreme precipitation event over Beijing on July 212012 is simulated by using the two coupling methods. Results show that online coupling method is of great value in improving the model simulation. Furthermore,the data exchange frequency of online coupling has some effect on simulation result.
基金University Grants Commission (UGC) for funding to pursue this work
文摘This paper presents results from a statistical validation of the hindcasts of surface wind by a high-reso-ution-mesoscale atmospheric numerical model Advanced Research WRF （ARW3.3）, which is set up to force the operational coastal ocean forecast system at Indian Na- tional Centre for Ocean Information Services （INCOIS）. Evaluation is carried out based on comparisons of day-3 forecasts of surface wind with in situ and remote-sensing data. The results show that the model predicts the surface wind fields fairly accurately over the west coast of India, with high skill in predicting the surface wind during the pre-monsoon season. The model predicts the diurnal variability of the surface wind with reasonable accuracy. The model simulates the land-sea breeze cycle in the coastal region realistically, which is very clearly observed during the northeast monsoon and pre-monsoon season and is less prominent during the southwest monsoon season.
文摘Data assimilation is the process by which measurements and model predictions are combined to obtain an accurate representation of the state of the modeled system. We implemented a data assimilation scheme called LETKF （local ensemble transform Kalman filter） with FSUGSM （Florida State University Global Spectral Model） and made an experiment to evaluate the initial condition generated to numerical weather prediction to FSUGSM model. The LETKF analysis carries out independently at each grid point with the use of ＂local＂ observations. An ensemble of estimates in state space represents uncertainty. The FSUGSM is a multilevel （27 vertical levels） spectral primitive equation model, where the variables are expanded horizontally in a truncated series of spherical harmonic functions （at resolution T63） and a transform technique is applied to calculate the physical processes in real space The assimilation cycle runs on the period 01/01/2001 to 31/01/2001 at （00, 06, 12 and 18 GMT） for each day. We examined the atmospheric fields during the period and the OMF （observation-minus-forecast） and the OMA （observation-minus-analysis） statistics to verify the analysis quality comparing with forecasts and observations. The analyses present stability and show suitable to initiate the weather predictions.
