Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts
This study investigated the impact of the assimilation of satellite radiance observations in a three-dimensional variational data assimilation system (3DVAR) that could improve the tracking and intensity forecasts of the Tropical Storm Dianmu in 2021, which occurred over parts of southeast mainland...
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MDPI AG
2022-06-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/13/6/956 |
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| author | Thippawan Thodsan Falin Wu Kritanai Torsri Efren Martin Alban Cuestas Gongliu Yang |
| author_facet | Thippawan Thodsan Falin Wu Kritanai Torsri Efren Martin Alban Cuestas Gongliu Yang |
| author_sort | Thippawan Thodsan |
| collection | DOAJ |
| description | This study investigated the impact of the assimilation of satellite radiance observations in a three-dimensional variational data assimilation system (3DVAR) that could improve the tracking and intensity forecasts of the Tropical Storm Dianmu in 2021, which occurred over parts of southeast mainland Asia. The weather research and forecasting (WRF) model was used to conduct the assimilation experiments of the storm. Four sets of numerical experiments were performed using the WRF. In the first, the control experiment, only conventional data in Binary Universal Form for the Representation of Meteorological Data (PREPBUFR) observations from the National Centers for Environmental Prediction (NCEP) were assimilated. The second experiment (RDA1) was performed with PREPBUFR observations and satellite radiance data from the Advanced Microwave Unit-A (AMSU-A), and the Advanced Technology Microwave Sounder (ATMS). PREPBUFR observations and the High-resolution Infrared Radiation Sounder (HIRS-4) were used in the third experiment (RDA2). The fourth experiment (ALL-OBS) used the assimilation of PREPBUFR observations and all satellite radiance data (AMSU-A, ATMS, and HIRS-4). The community radiative transfer model was used on the forward operator for the satellite radiance assimilation, along with quality control and bias correction procedures, before assimilating the radiance data. To evaluate the impact of the assimilation experiments, a forecast starting on 00 UTC 23 September 2021, was produced for 72 h. The results showed that the ALL-OBS experiment improved the short-term forecast up to ~24 h lead time, as compared to the assimilation considering only PREPBUFR observations. When all observations were assimilated into the model, the storm’s landfall position, intensity, and structure were accurately predicted. In the deterministic forecast, the tracking errors of the ALL-OBS experiment was consistently less than 40 km within 24 h. The case study of Tropical Storm Dianmu exhibited the significant positive impact of all observations in the numerical model, which could improve updates for initial conditions and storm forecasting. |
| first_indexed | 2024-03-10T00:26:26Z |
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| id | doaj.art-300a66072450416bbe7fc4605089ca8a |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T00:26:26Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-300a66072450416bbe7fc4605089ca8a2023-11-23T15:33:21ZengMDPI AGAtmosphere2073-44332022-06-0113695610.3390/atmos13060956Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) ForecastsThippawan Thodsan0Falin Wu1Kritanai Torsri2Efren Martin Alban Cuestas3Gongliu Yang4SARS Laboratory, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, ChinaSARS Laboratory, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, ChinaHydro-Informatics Institute, Ministry of Higher Education, Science, Research and Innovation, Bangkok 10900, ThailandAsian Institute of Technical Sciences, Bangkok 10400, ThailandSARS Laboratory, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, ChinaThis study investigated the impact of the assimilation of satellite radiance observations in a three-dimensional variational data assimilation system (3DVAR) that could improve the tracking and intensity forecasts of the Tropical Storm Dianmu in 2021, which occurred over parts of southeast mainland Asia. The weather research and forecasting (WRF) model was used to conduct the assimilation experiments of the storm. Four sets of numerical experiments were performed using the WRF. In the first, the control experiment, only conventional data in Binary Universal Form for the Representation of Meteorological Data (PREPBUFR) observations from the National Centers for Environmental Prediction (NCEP) were assimilated. The second experiment (RDA1) was performed with PREPBUFR observations and satellite radiance data from the Advanced Microwave Unit-A (AMSU-A), and the Advanced Technology Microwave Sounder (ATMS). PREPBUFR observations and the High-resolution Infrared Radiation Sounder (HIRS-4) were used in the third experiment (RDA2). The fourth experiment (ALL-OBS) used the assimilation of PREPBUFR observations and all satellite radiance data (AMSU-A, ATMS, and HIRS-4). The community radiative transfer model was used on the forward operator for the satellite radiance assimilation, along with quality control and bias correction procedures, before assimilating the radiance data. To evaluate the impact of the assimilation experiments, a forecast starting on 00 UTC 23 September 2021, was produced for 72 h. The results showed that the ALL-OBS experiment improved the short-term forecast up to ~24 h lead time, as compared to the assimilation considering only PREPBUFR observations. When all observations were assimilated into the model, the storm’s landfall position, intensity, and structure were accurately predicted. In the deterministic forecast, the tracking errors of the ALL-OBS experiment was consistently less than 40 km within 24 h. The case study of Tropical Storm Dianmu exhibited the significant positive impact of all observations in the numerical model, which could improve updates for initial conditions and storm forecasting.https://www.mdpi.com/2073-4433/13/6/956variational data assimilationsatellite radiance datatropical stormweather research and forecasting (WRF) model |
| spellingShingle | Thippawan Thodsan Falin Wu Kritanai Torsri Efren Martin Alban Cuestas Gongliu Yang Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts Atmosphere variational data assimilation satellite radiance data tropical storm weather research and forecasting (WRF) model |
| title | Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts |
| title_full | Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts |
| title_fullStr | Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts |
| title_full_unstemmed | Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts |
| title_short | Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts |
| title_sort | satellite radiance data assimilation using the wrf 3dvar system for tropical storm dianmu 2021 forecasts |
| topic | variational data assimilation satellite radiance data tropical storm weather research and forecasting (WRF) model |
| url | https://www.mdpi.com/2073-4433/13/6/956 |
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