Tropical Cyclone Surface Winds From Aircraft With a Neural Network
Abstract Estimates of the surface wind field in a tropical cyclone (TC) are required in real time by operational forecast centers to warn the public about potential impacts to life and property. In‐situ aircraft data must be adjusted from flight level to surface using wind reductions (WRs) since the...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
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| Online Access: | https://doi.org/10.1029/2025JH000584 |
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| author | Alexander J. DesRosiers Michael M. Bell Jennifer C. DeHart Jonathan L. Vigh Christopher M. Rozoff Eric A. Hendricks |
| author_facet | Alexander J. DesRosiers Michael M. Bell Jennifer C. DeHart Jonathan L. Vigh Christopher M. Rozoff Eric A. Hendricks |
| author_sort | Alexander J. DesRosiers |
| collection | DOAJ |
| description | Abstract Estimates of the surface wind field in a tropical cyclone (TC) are required in real time by operational forecast centers to warn the public about potential impacts to life and property. In‐situ aircraft data must be adjusted from flight level to surface using wind reductions (WRs) since the aircraft cannot fly too low due to safety concerns. Current operational WRs do not capture all the variability in the TC surface wind field. In this study, an observational data set of Stepped Frequency Microwave Radiometer (SFMR) surface wind speeds that are collocated with flight‐level predictors is used to analyze the variability of WRs with respect to aircraft altitude and TC storm motion and intensity. The Surface Winds from Aircraft with a Neural Network (SWANN) model is trained on the observations with a custom loss function that prioritizes accurate prediction of relatively rare high‐wind observations and minimization of variance in the WRs. The model is capable of learning physical relationships that are consistent with theoretical understanding of the TC boundary layer. Radar‐derived wind fields at flight level and independent dropwindsonde in‐situ surface wind measurements are used to validate the SWANN model and show improvement over the current operational procedure. A test case shows that SWANN can produce a realistic asymmetric surface wind field from a radar‐derived flight‐level wind field which has a maximum wind speed similar to the operational intensity, suggesting promise for the method to lead to improved real‐time TC intensity estimation and prediction in the future. |
| format | Article |
| id | doaj-art-99ee5e4c557543838a9c02793839b51e |
| institution | Matheson Library |
| issn | 2993-5210 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-99ee5e4c557543838a9c02793839b51e2025-06-25T14:02:49ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102025-06-0122n/an/a10.1029/2025JH000584Tropical Cyclone Surface Winds From Aircraft With a Neural NetworkAlexander J. DesRosiers0Michael M. Bell1Jennifer C. DeHart2Jonathan L. Vigh3Christopher M. Rozoff4Eric A. Hendricks5Department of Atmospheric Science Colorado State University Fort Collins CO USADepartment of Atmospheric Science Colorado State University Fort Collins CO USADepartment of Atmospheric Science Colorado State University Fort Collins CO USAU.S. National Science Foundation National Center for Atmospheric Research Boulder CO USAU.S. National Science Foundation National Center for Atmospheric Research Boulder CO USAU.S. National Science Foundation National Center for Atmospheric Research Boulder CO USAAbstract Estimates of the surface wind field in a tropical cyclone (TC) are required in real time by operational forecast centers to warn the public about potential impacts to life and property. In‐situ aircraft data must be adjusted from flight level to surface using wind reductions (WRs) since the aircraft cannot fly too low due to safety concerns. Current operational WRs do not capture all the variability in the TC surface wind field. In this study, an observational data set of Stepped Frequency Microwave Radiometer (SFMR) surface wind speeds that are collocated with flight‐level predictors is used to analyze the variability of WRs with respect to aircraft altitude and TC storm motion and intensity. The Surface Winds from Aircraft with a Neural Network (SWANN) model is trained on the observations with a custom loss function that prioritizes accurate prediction of relatively rare high‐wind observations and minimization of variance in the WRs. The model is capable of learning physical relationships that are consistent with theoretical understanding of the TC boundary layer. Radar‐derived wind fields at flight level and independent dropwindsonde in‐situ surface wind measurements are used to validate the SWANN model and show improvement over the current operational procedure. A test case shows that SWANN can produce a realistic asymmetric surface wind field from a radar‐derived flight‐level wind field which has a maximum wind speed similar to the operational intensity, suggesting promise for the method to lead to improved real‐time TC intensity estimation and prediction in the future.https://doi.org/10.1029/2025JH000584tropical cyclonemachine learningneural network |
| spellingShingle | Alexander J. DesRosiers Michael M. Bell Jennifer C. DeHart Jonathan L. Vigh Christopher M. Rozoff Eric A. Hendricks Tropical Cyclone Surface Winds From Aircraft With a Neural Network Journal of Geophysical Research: Machine Learning and Computation tropical cyclone machine learning neural network |
| title | Tropical Cyclone Surface Winds From Aircraft With a Neural Network |
| title_full | Tropical Cyclone Surface Winds From Aircraft With a Neural Network |
| title_fullStr | Tropical Cyclone Surface Winds From Aircraft With a Neural Network |
| title_full_unstemmed | Tropical Cyclone Surface Winds From Aircraft With a Neural Network |
| title_short | Tropical Cyclone Surface Winds From Aircraft With a Neural Network |
| title_sort | tropical cyclone surface winds from aircraft with a neural network |
| topic | tropical cyclone machine learning neural network |
| url | https://doi.org/10.1029/2025JH000584 |
| work_keys_str_mv | AT alexanderjdesrosiers tropicalcyclonesurfacewindsfromaircraftwithaneuralnetwork AT michaelmbell tropicalcyclonesurfacewindsfromaircraftwithaneuralnetwork AT jennifercdehart tropicalcyclonesurfacewindsfromaircraftwithaneuralnetwork AT jonathanlvigh tropicalcyclonesurfacewindsfromaircraftwithaneuralnetwork AT christophermrozoff tropicalcyclonesurfacewindsfromaircraftwithaneuralnetwork AT ericahendricks tropicalcyclonesurfacewindsfromaircraftwithaneuralnetwork |