Tropical Cyclone Surface Winds From Aircraft With a Neural Network

Details

• Journal Title:
  Journal of Geophysical Research: Machine Learning and Computation
• Personal Author:
  DesRosiers, Alexander J. ; Bell, Michael M. ; DeHart, Jennifer C. ; Vigh, Jonathan L. ; Rozoff, Christopher M. ; Hendricks, Eric A.
• NOAA Program & Office:
  NWS (National Weather Service) ; OAR (Oceanic and Atmospheric Research) ; CIRA (Cooperative Institute for Research in the Atmosphere)
• Description:
  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.
• Source:
  Journal of Geophysical Research: Machine Learning and Computation, 2(2)
• DOI:
  https://doi.org/10.1029/2025jh000584
• ISSN:
  2993-5210 ; 2993-5210
• Format:
  PDF
• Publisher:
  American Geophysical Union (AGU)
• Document Type:
  Journal Article
• Funding:
  Grant no. NA22OAR4590521 ; Grant no. NA18NWS4680058
• License:
  https://creativecommons.org/licenses/by-nc/4.0/
• Rights Information:
  CC BY-NC
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:6a4330be19b5e793328b8b9f9a57a1a195151c20f81d50d96216f477548c6eebd6e8dd337aefea51e8677a619449286b19c28ec13c07dc07726821c3fb23c354
• Download URL:
  https://repository.library.noaa.gov/view/noaa/71107/noaa_71107_DS1.pdf
• File Type:
  [PDF - 4.48 MB ]