Radar Data Assimilation with JEDI LETKF for Ensemble Forecasting of Hurricane Ida (2021) Using a HAFS-Like Configuration.
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| Title: | Radar Data Assimilation with JEDI LETKF for Ensemble Forecasting of Hurricane Ida (2021) Using a HAFS-Like Configuration. |
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| Authors: | Tong, Chong-Chi1 (AUTHOR), Liu, Chengsi1 (AUTHOR) cliu@ou.edu, Xue, Ming1,2 (AUTHOR) |
| Source: | Monthly Weather Review. May2026, Vol. 154 Issue 5, p1-17. 17p. |
| Subjects: | Data assimilation, Hurricane forecasting, Ensemble learning, Hurricanes, Tracking radar |
| Abstract: | This study evaluates the capability of the Local Ensemble Transform Kalman Filter within the Joint Effort for Data Assimilation Integration (JEDI) framework to directly assimilate radar observations for improving short-range ensemble forecasts of landfalling hurricanes. Experiments are performed using a model configuration that adopts the physics suite of the Hurricane Analysis and Forecast System (HAFS) for Hurricane Ida (2021). Four hourly cycling data assimilation (DA) experiments are conducted and compared against a control forecast without DA. The baseline DA experiment assimilates only conventional observations, while the other three additionally assimilate radar reflectivity from the Multi-Radar Multi-Sensor system, radial velocity from WSR-88D radars, or both. Results show that assimilating radial velocity substantially improves the inner-core wind structure, reduces the ensemble spread in track forecasts during the DA cycling, and enhances the accuracy of landfall location forecasts. Assimilating reflectivity improves rainfall forecasts, particularly for outer rainbands prior to landfall, while having relatively minor impact on intensity. Post-landfall rainfall prediction also benefits from radial velocity assimilation, mainly through improved track forecasts. Quantitative verification using equitable threat scores shows that radar DA improves forecasts of total rainfall accumulation. Radial velocity assimilation contributes most to moderate rainfall prediction, while reflectivity assimilation offers greater skill in forecasting extreme rainfall. These findings underscore the value of incorporating radar data into ensemble hurricane forecasting and provide practical guidance for future implementation of radar DA within the HAFS-JEDI framework for operational applications. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | This study evaluates the capability of the Local Ensemble Transform Kalman Filter within the Joint Effort for Data Assimilation Integration (JEDI) framework to directly assimilate radar observations for improving short-range ensemble forecasts of landfalling hurricanes. Experiments are performed using a model configuration that adopts the physics suite of the Hurricane Analysis and Forecast System (HAFS) for Hurricane Ida (2021). Four hourly cycling data assimilation (DA) experiments are conducted and compared against a control forecast without DA. The baseline DA experiment assimilates only conventional observations, while the other three additionally assimilate radar reflectivity from the Multi-Radar Multi-Sensor system, radial velocity from WSR-88D radars, or both. Results show that assimilating radial velocity substantially improves the inner-core wind structure, reduces the ensemble spread in track forecasts during the DA cycling, and enhances the accuracy of landfall location forecasts. Assimilating reflectivity improves rainfall forecasts, particularly for outer rainbands prior to landfall, while having relatively minor impact on intensity. Post-landfall rainfall prediction also benefits from radial velocity assimilation, mainly through improved track forecasts. Quantitative verification using equitable threat scores shows that radar DA improves forecasts of total rainfall accumulation. Radial velocity assimilation contributes most to moderate rainfall prediction, while reflectivity assimilation offers greater skill in forecasting extreme rainfall. These findings underscore the value of incorporating radar data into ensemble hurricane forecasting and provide practical guidance for future implementation of radar DA within the HAFS-JEDI framework for operational applications. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00270644 |
| DOI: | 10.1175/MWR-D-25-0139.1 |
