Using Tropical Cyclone Reconnaissance to Improve Forecasts of Postlandfall Tropical Cyclone Hazards

Details

• Journal Title:
  Bulletin of the American Meteorological Society
• Personal Author:
  Sippel, Jason A. ; Ditchek, Sarah D. ; Hazelton, Andrew ; Brown, Daniel ; Alaka, Ghassan J. ; Lamers, Alex
• NOAA Program & Office:
  NWS (National Weather Service) ; OAR (Oceanic and Atmospheric Research) ; AOML (Atlantic Oceanographic and Meteorological Laboratory) ; CIMAS (Cooperative Institute for Marine and Atmospheric Studies) ; HPC (Weather Prediction Center) ; NCEP (National Centers for Environmental Prediction) ; NHC (National Hurricane Center)
• Description:
  This study presents a first attempt to systematically evaluate the impact of tropical cyclone (TC) reconnaissance missions on postlandfall TC forecasts, including life-threatening hazards. Reconnaissance data demonstrably improve TC forecasts, which has encouraged a significant increase in reconnaissance flights over the past decade. Despite this increase, aircraft observations are primarily used to minimize prelandfall forecast errors, with little to no operational consideration of postlandfall forecasts. Thus, this effort represents a natural next step in optimizing the effectiveness of TC reconnaissance missions. Observing-system experiments conducted using NOAA's Hurricane Weather Research and Forecasting (HWRF) Model suggest that prelandfall reconnaissance benefits postlandfall forecasts. In particular, dropwindsondes released from missions into landfalling TCs substantially improve track forecasts through the postlandfall period. We specifically examine Hurricanes Harvey (2017) and Florence (2018), two TCs with long postlandfall tracks that were notable for catastrophic flooding. In both, dropwindsonde sampling substantially modifies the near-storm flow, leading subsequent to forecast improvements for both track and precipitation. Precipitation forecast skill improves the most in cycles with the largest track improvements. These results suggest that reconnaissance sampling can be further optimized to more holistically improve TC-hazard forecasts, both before and after landfall. This could be accomplished by using reconnaissance to the maximum extent through landfall, especially for cases with high-impact potential. In extreme cases like Harvey and Florence, aircraft might also be used postlandfall, perhaps with additional radiosonde observations. Further, new targeting strategies could also be implemented to target postlandfall impacts. Significance Statement In this study, we found that data collected from aircraft flying into a tropical cyclone (TC) before landfall help forecasts of the TC path and rainfall prior to as well as after landfall. That finding is important since postlandfall impacts, such as flooding, can be catastrophic. Our results suggest that we can further improve our forecasts and, thereby, our TC-hazard forecasts by extending and adjusting how and when we collect data, especially for high-impact TCs.
• Source:
  Bulletin of the American Meteorological Society, 107(7), E1599-E1612
• DOI:
  https://doi.org/10.1175/BAMS-D-25-0125.1
• ISSN:
  0003-0007 ; 1520-0477
• Format:
  PDF
• Publisher:
  American Meteorological Society
• Document Type:
  Journal Article
• Rights Information:
  Other
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:66a3f9302a31ca348f3f1b84034030822415a190318cb677dd4326a9074f70aa5706bea8a2af25c42b450c33f835343d58a100ea0615688163d0d635b244e94b
• Download URL:
  https://repository.library.noaa.gov/view/noaa/74036/noaa_74036_DS1.pdf
• File Type:
  [PDF - 2.11 MB ]