The MM5, which is the PSU/NCAR mesoscale nonhydrostatic limited-area ,nodel, and its adjoining modeling system are used in this paper. Taking T106 analysis data as background field the authors generate an optimal init...The MM5, which is the PSU/NCAR mesoscale nonhydrostatic limited-area ,nodel, and its adjoining modeling system are used in this paper. Taking T106 analysis data as background field the authors generate an optimal initial condition of a typhoon by using two bogus data assimilation schemes, and conduct some numerical simulating experiments. The results ofno.9608 typhoon （Gloria） show that the optimal initial tield have some dramatic improvements, such as inaccurate position of typhoon center, weaker typhoon circulation and incomplete inner structure of the typhoon, which are caused by shortage of data over the sea. Some improvements have been made in the track forecast. Through several comparing experiments, the initialization optimized by BDA scheme is found to be more reasonable than GFDL scheme and its typhoon track forrecast is better.展开更多
The Tropical Cyclone(TC)track prediction using different NWP models and its verification is the critical task to provide prior knowledge about the model errors,which is beneficial for giving the model guidance-based r...The Tropical Cyclone(TC)track prediction using different NWP models and its verification is the critical task to provide prior knowledge about the model errors,which is beneficial for giving the model guidance-based real-time cyclone warning advisories.This study has attempted to verify the Global Forecast System(GFS)model forecasted tropical cyclone track and intensity over the North Indian Ocean(NIO)for the years 2019 and 2020.GFS is one of the operational models in the India Meteorological Department(IMD),which provides the medium-range weather forecast up to 10 days.The forecasted tracks from the GFS forecast are obtained using a vortex tracker developed by Geophysical Fluid Dynamics Laboratory(GFDL).A total of 13 tropical cyclones formed over the North Indian Ocean,eight during 2019 and five in 2020 have been considered in this study.The accuracy of the model predicted tracks and intensity is verified for five days forecasts(120 h)at 6-h intervals;the track errors are verified in terms of Direct Position Error(DPE),Along Track Error(ATE)and Cross-Track Error(CTE).The annual mean DPE over NIO during 2019(51–331 km)is lower than 2020(82–359 km),and the DPE is less than 150 km up to 66 h during 2019 and 48 h during 2020.The positive ATE(76–332 km)indicates the predicted track movement is faster than the observed track during both years.The positive CTE values for most forecast lead times suggest that the predicted track is towards the right side of the observed track during both years.The cyclone Intensity forecast for the maximum sustained wind speed(Max WS)and central mean sea level pressure(MSLP)are verified in terms of mean error(ME)and root mean square error(RMSE).The errors are lead time independent.However,most of the time model under-predicted the cyclone intensity during both years.Finally,there is a significant variance in track and intensity errors from the cyclone to cyclone and Bay of Bengal basin to the Arabian Sea basin.展开更多
基金National Key Fundamental Research Project of China (40175012)
文摘The MM5, which is the PSU/NCAR mesoscale nonhydrostatic limited-area ,nodel, and its adjoining modeling system are used in this paper. Taking T106 analysis data as background field the authors generate an optimal initial condition of a typhoon by using two bogus data assimilation schemes, and conduct some numerical simulating experiments. The results ofno.9608 typhoon （Gloria） show that the optimal initial tield have some dramatic improvements, such as inaccurate position of typhoon center, weaker typhoon circulation and incomplete inner structure of the typhoon, which are caused by shortage of data over the sea. Some improvements have been made in the track forecast. Through several comparing experiments, the initialization optimized by BDA scheme is found to be more reasonable than GFDL scheme and its typhoon track forrecast is better.
文摘The Tropical Cyclone(TC)track prediction using different NWP models and its verification is the critical task to provide prior knowledge about the model errors,which is beneficial for giving the model guidance-based real-time cyclone warning advisories.This study has attempted to verify the Global Forecast System(GFS)model forecasted tropical cyclone track and intensity over the North Indian Ocean(NIO)for the years 2019 and 2020.GFS is one of the operational models in the India Meteorological Department(IMD),which provides the medium-range weather forecast up to 10 days.The forecasted tracks from the GFS forecast are obtained using a vortex tracker developed by Geophysical Fluid Dynamics Laboratory(GFDL).A total of 13 tropical cyclones formed over the North Indian Ocean,eight during 2019 and five in 2020 have been considered in this study.The accuracy of the model predicted tracks and intensity is verified for five days forecasts(120 h)at 6-h intervals;the track errors are verified in terms of Direct Position Error(DPE),Along Track Error(ATE)and Cross-Track Error(CTE).The annual mean DPE over NIO during 2019(51–331 km)is lower than 2020(82–359 km),and the DPE is less than 150 km up to 66 h during 2019 and 48 h during 2020.The positive ATE(76–332 km)indicates the predicted track movement is faster than the observed track during both years.The positive CTE values for most forecast lead times suggest that the predicted track is towards the right side of the observed track during both years.The cyclone Intensity forecast for the maximum sustained wind speed(Max WS)and central mean sea level pressure(MSLP)are verified in terms of mean error(ME)and root mean square error(RMSE).The errors are lead time independent.However,most of the time model under-predicted the cyclone intensity during both years.Finally,there is a significant variance in track and intensity errors from the cyclone to cyclone and Bay of Bengal basin to the Arabian Sea basin.
