A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Aerosol data assimilation using data from Fengyun-4A, a next-generation geostationary meteorological satellite
Abstract The Fengyun-4A (FY-4A) meteorological satellite, a next-generation geostationary meteorological satellite, was launched on December 11, 2016. For instance, the Advanced Geosynchronous Radiation Imager (AGRI) aboard FY-4A (AGRI/FY-4A) takes full-disk images at a 15-min interval in 14 spectral bands with the 0.5–4-km resolution. Here we developed data assimilation system based on the Gridpoint Statistical Interpolation (GSI) system in which the Aerosol Optical Depth (AOD) derived from FY-4A data were successfully assimilated for the first time. The capability to assimilate FY-4A Aerosol optical depth (AOD) with an hourly cycling configuration was then evaluated by a dust storm over East Asia during 12–14 May 2019. The analyses initialized Weather Research and Forecasting-Chemistry (WRF-Chem) model forecasts. The system is tested with FY-4 AOD, Himawari-8 AOD in experiments and then the results are compared to the Aerosol Robotic Network (AERONET) AOD observations, which serving as the independent observations. The results indicated that assimilating FY-4 AOD substantially showed much better agreement with observations than those from the control. Furthermore, the Bias and RMSE generally reduced about 20% with forecast range. This study indicates that the aerosol data assimilation using data from FY-4A can be used to improve the performance of forecast model.
Highlights It is the first attempt to assimilate FY-4A AOD with a rapid-update DA system. It was investigated for the dust storm occurred over East Asia on 12–14 May 2019. General positive impacts were achieved from assimilating high-frequency data.
Aerosol data assimilation using data from Fengyun-4A, a next-generation geostationary meteorological satellite
Abstract The Fengyun-4A (FY-4A) meteorological satellite, a next-generation geostationary meteorological satellite, was launched on December 11, 2016. For instance, the Advanced Geosynchronous Radiation Imager (AGRI) aboard FY-4A (AGRI/FY-4A) takes full-disk images at a 15-min interval in 14 spectral bands with the 0.5–4-km resolution. Here we developed data assimilation system based on the Gridpoint Statistical Interpolation (GSI) system in which the Aerosol Optical Depth (AOD) derived from FY-4A data were successfully assimilated for the first time. The capability to assimilate FY-4A Aerosol optical depth (AOD) with an hourly cycling configuration was then evaluated by a dust storm over East Asia during 12–14 May 2019. The analyses initialized Weather Research and Forecasting-Chemistry (WRF-Chem) model forecasts. The system is tested with FY-4 AOD, Himawari-8 AOD in experiments and then the results are compared to the Aerosol Robotic Network (AERONET) AOD observations, which serving as the independent observations. The results indicated that assimilating FY-4 AOD substantially showed much better agreement with observations than those from the control. Furthermore, the Bias and RMSE generally reduced about 20% with forecast range. This study indicates that the aerosol data assimilation using data from FY-4A can be used to improve the performance of forecast model.
Highlights It is the first attempt to assimilate FY-4A AOD with a rapid-update DA system. It was investigated for the dust storm occurred over East Asia on 12–14 May 2019. General positive impacts were achieved from assimilating high-frequency data.
Aerosol data assimilation using data from Fengyun-4A, a next-generation geostationary meteorological satellite
Xia, Xiaoli (author) / Min, Jinzhong (author) / Shen, Feifei (author) / Wang, Yuanbing (author) / Xu, Dongmei (author) / Yang, Chun (author) / Zhang, Peng (author)
Atmospheric Environment ; 237
2020-06-08
Article (Journal)
Electronic Resource
English
Precise orbit determination of the Fengyun-3C satellite using onboard GPS and BDS observations
| Online Contents | 2017