Global Verification of Tropical Cyclone Genesis Forecasts from Five Global Numerical Models

Details

• Journal Title:
  Weather and Forecasting
• Personal Author:
  Halperin, Daniel J. ; Hart, Robert E.
• NOAA Program & Office:
  NWS (National Weather Service) ; OAR (Oceanic and Atmospheric Research) ; WPO (Weather Program Office)
• Description:
  Prior studies by the authors have documented the verification statistics of disturbance-based tropical cyclone (TC) genesis forecasts over the North Atlantic (AL) and eastern North Pacific (EP) basins, which led to the development of real-time probabilistic TC genesis guidance based on multiple logistic regression [the TC Logistic Guidance for Genesis (TCLOGG)]. This study provides a substantial update to that prior work by analyzing a more recent period (2017–22) with one additional model [Navy Global Environmental Model (NAVGEM)], expanding the forecast period temporally to 7 days, and expanding the study domain spatially to include all basins [except the central North Pacific (CP) basin, where the sample size of TC genesis events was too small to generate meaningful statistics]. TC genesis forecasts from five global models are verified against the NHC’s and JTWC’s best tracks. Verification statistics exhibit nontrivial interannual and model-to-model variability rendering it unfeasible to attempt to define repeatable performance rankings among the models. Nevertheless, results indicate that the ECMWF model exhibits the largest mean success ratio (SR) overall, while the UKMO and GFS models exhibit the greatest probability of detection (POD). All models exhibit a clear trade-off between SR and POD, yielding mean critical success index values < 0.35 for any individual model and basin. The ECMWF and UKMO models exhibit the greatest critical success index (CSI) values globally. This study provides additional evidence that some best track TC genesis events can be detected at least 1 week in advance, but maximum lead times are inconsistent. The resulting dataset of verified forecasts will serve as an updated training dataset for enhanced and updated TCLOGG products.
• Keywords:
  Cyclones Numerical Weather Forecasting Numerical Weather Forecasting Forecast Skill Forecast Verification Model Evaluation Numerical Weather Prediction Tropical Cyclones
• Source:
  Wea. Forecasting, 40, 1125–1145
• DOI:
  https://doi.org/10.1175/WAF-D-24-0130.1
• Format:
  PDF
• Document Type:
  Journal Article
• Funding:
  Grant no. NA18NWS4680066 ; Grant no. NA19OAR4590134/5
• Rights Information:
  Other
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:31cf46beb82034e457c9eedcbe6a2c9356773f91bafe0a7fca14329a9702ee25c93feaec2c9b87ec525c320790f5469cc1eb8f86dbbbea007baf34f75c4d4cca
• File Type: