Assimilation of Himawari-8 Imager Radiance Data with the WRF-3DVAR system for the prediction of Typhoon Soulder
1
Citation
13
Reference
10
Related Paper
Citation Trend
Abstract:
Abstract. Himawari-8 is a new generation geostationary meteorological satellite launched by Japan Meteorological Agency (JMA). It carries the Advanced Himawari imager (AHI) onboard, which can continuously monitor high-impact weather events with high frequency space and time. The assimilation of AHI was implemented with the framework of the mesoscale numerical model WRF and its three-dimensional variational assimilation system (3DVAR) for the analysis and prediction of typhoon Soudelor in the Pacific Typhoon season in 2015. The effective assimilation of AHI Imager data in tropical cyclone with rapid intensify development has been realized. The results show that after assimilating the AHI imager data under clear sky conditions, the typhoon position in the background field in the model is effectively corrected compared with the control experiment without AHI data. It is found that assimilation of AHI imager data is able to improve the analyses of the water vapor and wind in typhoon inner-core region. The analyses and forecast of the typhoon minimum sea level pressure, the maximum near-surface wind speed, and the typhoon track are further improved．Keywords:
Typhoon
Abstract. Himawari-8 is a next-generation geostationary meteorological satellite launched by the Japan Meteorological Agency. It carries the Advanced Himawari Imager (AHI) on board, which can continuously monitor high-impact weather events with high frequency in space and time. The assimilation of AHI radiance data was implemented with the three-dimensional variational data assimilation system (3DVAR) of the Weather Research and Forecasting Model for the analysis and prediction of Typhoon Soudelor (2015) in the Pacific typhoon season. The effective assimilation of AHI radiance data in improving the forecast of the tropical cyclone during its rapid intensification has been realized. The results show that, after assimilating the AHI radiance data under clear-sky conditions, the typhoon position in the background field of the model was effectively corrected compared with the control experiment without AHI radiance data assimilation. It is found that the assimilation of AHI radiance data is able to improve the analyses of the water vapor and wind in a typhoon's inner-core region. The analyses and forecasts of the minimum sea level pressure, the maximum surface wind, and the track of the typhoon are further improved.
Typhoon
Cite
Citations (12)
Typhoon
Meteorological satellite
Cite
Citations (0)
The Running-In-Place (RIP) method is implemented in the framework of the Local Ensemble Transform Kalman Filter (LETKF) coupled with the Weather Research and Forecasting (WRF) model. RIP aims at accelerating the spin-up of the regional LETKF system when the WRF ensemble is initialised from a global analysis, which is obtained at a coarser resolution and lacks features related to the underlying mesoscale evolution. The RIP method is further proposed as an outer-loop scheme to improve the nonlinear evolution of the ensemble when the characteristics of the error statistics change rapidly owing to strong nonlinear dynamics. The impact of using RIP as an outer-loop for the WRF-LETKF system is evaluated for typhoon assimilation and prediction with Typhoon Sinlaku (2008) as a case study. For forecasts beyond one day, the typhoon track prediction is significantly improved after RIP is applied, especially during the spin-up period of the LETKF assimilation when Sinlaku is developing rapidly from a severe tropical storm to a typhoon. The impact of the dropsondes is significantly increased by RIP at early assimilation cycles. Results suggest that these improvements are because of the positive impact on the environmental condition of the typhoon. Results also suggest that using the RIP scheme adaptively allows RIP to be used as an outer-loop for the WRF-LETKF with further improvements.
Typhoon
Spin-up
Cite
Citations (18)
The control experiment and two assimilation experiments of WRF regional mesoscale model prediction are verified for typhoon Haitang (0505). Based on the WRF-3DVAR system, the FY-2C infrared and water vapor cloud motion winds are assimilated into WRF model. The assimilation data of one assimilation test are the cloud motion winds before quality control and those of the other are cloud motion winds after quality control. The effect of the cloud motion winds on the prediction of rain and wind field is analyzed through comparing the three tests. Results show that the assimilation of the quality-controled cloud motion winds into the initial fields has positive impact on prediction of the area and intensity of rain, and also indicate that different grades of Ts score are improved by far than the other tests. The assimilation makes improvement in the forecast of wind. The same approach as that for Haitang is taken to simulate other two typhoon cases and the Ts scores of the forecast rainfall are given. And the results for the two typhoon cases show similar conclusions. Therefore, assimilating cloud motion winds with reasonable control and selection can well improve the descriptive possibility of wind field in WRF model, supply the mesoscale information in the initialization field to improve the simulation result of precipitation and wind field and have positive impact on the forecasting ability of WRF model finally.
Typhoon
Initialization
Cite
Citations (0)
China’s new generation of polar-orbiting meteorological satellite FY-3A was successfully launched on May 26,2008,carrying microwave sounding devices which had similar performance to ATOVS of NOAA series.In order to study the application of microwave sounding data in numerical prediction of typhoons and to improve typhoon forecasting,we assimilated data directly for numerical forecasting of the track and intensity of the 2009 typhoon Morakot(0908)based on the WRF-3DVar system.Results showed that the initial fields of the numerical model due to direct assimilation of FY-3A microwave sounding data was improved much more than that due to assimilation of conventional observations alone,and the improvement was especially significant over the ocean,which is always without conventional observations.The model initial fields were more reasonable in reflecting the initial situation of typhoon circulation as well as temperature and humidity conditions,and typhoon central position at sea was also adjusted.Through direct 3DVar assimilation of FY-3A microwave data,the regional mesoscale model improves the forecasting of typhoon track.Therefore,the FY-3A microwave data could efficiently improve the numerical prediction of typhoons.
Typhoon
Cite
Citations (4)
<p>Lacking of high-resolution observations over oceans is one of the major problems for the numerical simulation of the tropical cyclones (TC), especially for the tropical cyclone inner-core structure&#8217;s simulation. Satellite observations plays an important role in improving the forecast skills of numerical weather prediction (NWP) systems. Many studies have suggested that the assimilation of satellite radiance data can substantially improve the numerical weather forecast skills for global model. However, the performance of satellite radiance data assimilation in limited-area modeling systems is still controversial.</p><p>This study attempts to investigate the impact of assimilation of the Advanced Technology Microwave Sounder (ATMS) satellite radiances data and its role to improve the model initial condition and forecast of typhoon LEKIMA(2019) using a regional mesoscale model. In this study, detailed analysis of the data impact will be presented, also the results from different data assimilation methods and different data usage schemes will be discussed.</p>
Typhoon
Weather prediction
Numerical models
Cite
Citations (1)
The impact of assimilating radiances from the Advanced Microwave Sounding Unit-A(AMSU-A) on the track prediction of Typhoon Megi(2010) was studied using the Weather Research and Forecasting(WRF) model and a hybrid ensemble threedimensional variational(En3DVAR) data assimilation(DA) system.The influences of tuning the length scale and variance scale factors related to the static background error covariance(BEC) on the track forecast of the typhoon were studied.The results show that,in typhoon radiance data assimilation,a moderate length scale factor improves the prediction of the typhoon track.The assimilation of AMSU-A radiances using 3DVAR had a slight positive impact on track forecasts,even when the static BEC was carefully tuned to optimize its performance.When the hybrid DA was employed,the track forecast was significantly improved,especially for the sharp northward turn after crossing the Philippines,with the flow-dependent ensemble covariance.The flow-dependent BEC can be estimated by the hybrid DA and was capable of adjusting the position of the typhoon systematically.The impacts of the typhoon-specific BEC derived from ensemble forecasts were revealed by comparing the analysis increments and forecasts generated by the hybrid DA and 3DVAR.Additionally,for 24 h forecasts,the hybrid DA experiment with use of the full flow-dependent background error substantially outperformed 3DVAR in terms of the horizontal winds and temperature in the lower and mid-troposphere and for moisture at all levels.
Typhoon
Cite
Citations (0)
Abstract The fifth-generation Pennsylvania State University–National Center for Atmospheric Research nonhydrostatic Mesoscale Model is employed to evaluate the impact of the Geostationary Meteorological Satellite-5 water vapor and infrared atmospheric motion vectors (AMVs), incorporated with the four-dimensional variational (4DVAR) data assimilation technique, on tropical cyclone (TC) track predictions. Twenty-two cases from eight different TCs over the western North Pacific in 2002 have been examined. The 4DVAR assimilation of these satellite-derived wind observations leads to appreciable improvements in the track forecasts, with average reductions in track error of ∼5% at 12 h, 12% at 24 h, 10% at 36 h, and 7% at 48 h. Preliminary results suggest that the improvement depends on the quantity of the AMV data available for assimilation.
Atmospheric research
Cite
Citations (14)
Abstract. Himawari-8 is a new generation geostationary meteorological satellite launched by Japan Meteorological Agency (JMA). It carries the Advanced Himawari imager (AHI) onboard, which can continuously monitor high-impact weather events with high frequency space and time. The assimilation of AHI was implemented with the framework of the mesoscale numerical model WRF and its three-dimensional variational assimilation system (3DVAR) for the analysis and prediction of typhoon Soudelor in the Pacific Typhoon season in 2015. The effective assimilation of AHI Imager data in tropical cyclone with rapid intensify development has been realized. The results show that after assimilating the AHI imager data under clear sky conditions, the typhoon position in the background field in the model is effectively corrected compared with the control experiment without AHI data. It is found that assimilation of AHI imager data is able to improve the analyses of the water vapor and wind in typhoon inner-core region. The analyses and forecast of the typhoon minimum sea level pressure, the maximum near-surface wind speed, and the typhoon track are further improved．
Typhoon
Cite
Citations (1)
The Fengyun-4A (FY-4A) geostationary satellite carries the Lightning Mapping Imager that measures total lightning rate of convective systems from space at high spatial and temporal resolutions. In this study, the performance of FY-4A lightning data assimilation (LDA) on the forecast of non-typhoon oceanic mesoscale convective systems (MCSs) is investigated by using an LDA method implemented in the Weather Research and Forecasting-Four Dimensional Data Assimilation (WRF-FDDA). With the LDA scheme, three-dimensional graupel mixing ratio fields retrieved from the FY-4A lightning data and the corresponding latent heating rates are assimilated into the Weather Research and Forecasting model via nudging terms. Two oceanic MCS cases over the South China Sea were selected to perform the study. The subjective evaluation results demonstrate that most of the oceanic convective cells missed by the control experiments are recovered in the analysis period by assimilating FY-4A lightning data, due to the promoted updrafts by latent-heat nudging, the more accurate and faster simulations of the cold pools, and the associated gust-fronts at the observed lightning locations. The cold pools and gust-fronts generated during the analysis period helped to maintain the development of the MCSs, and reduced the morphology and displacement errors of the simulations in the short-term forecast periods. The quantitative evaluation indicates that the most effective periods of the LDA for simulation enhancement were at the analysis time and the nowcasting (0–2 h forecast) periods.
Nowcasting
Typhoon
Lightning
Mesoscale convective system
Rainband
Cite
Citations (5)