A Statistical Model to Predict the Extratropical Transition of Tropical Cyclones

Details

• Journal Title:
  Weather and Forecasting
• Personal Author:
  Bieli, Melanie ; Sobel, Adam H. ; Camargo, Suzana J. ; Tippett, Michael K.
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research) ; CPO (Climate Program Office)
• Description:
  This paper introduces a logistic regression model for the extratropical transition (ET) of tropical cyclones in the North Atlantic and the western North Pacific, using elastic net regularization to select predictors and estimate coefficients. Predictors are chosen from the 1979–2017 best track and reanalysis datasets, and verification is done against the tropical/extratropical labels in the best track data. In an independent test set, the model skillfully predicts ET at lead times up to 2 days, with latitude and sea surface temperature as its most important predictors. At a lead time of 24 h, it predicts ET with a Matthews correlation coefficient of 0.4 in the North Atlantic, and 0.6 in the western North Pacific. It identifies 80% of storms undergoing ET in the North Atlantic and 92% of those in the western North Pacific. In total, 90% of transition time errors are less than 24 h. Select examples of the model’s performance on individual storms illustrate its strengths and weaknesses. Two versions of the model are presented: an “operational model” that may provide baseline guidance for operational forecasts and a “hazard model” that can be integrated into statistical TC risk models. As instantaneous diagnostics for tropical/extratropical status, both models’ zero lead time predictions perform about as well as the widely used cyclone phase space (CPS) in the western North Pacific and better than the CPS in the North Atlantic, and predict the timings of the transitions better than CPS in both basins.
• Source:
  Weather and Forecasting, 35(2), 451-466. https://journals.ametsoc.org/view/journals/wefo/35/2/waf-d-19-0045.1.xml
• DOI:
  https://doi.org/10.1175/WAF-D-19-0045.1
• Document Type:
  Journal Article
• Funding:
  Grant no. NA16OAR4310079
• Rights Information:
  Other
• Compliance:
  Submitted
• Download URL:
  https://repository.library.noaa.gov/view/noaa/28742/noaa_28742_DS1.pdf
• File Type:
  [PDF - 2.00 MB]
• Collection(s):
  Office of Oceanic and Atmospheric Research (OAR)
• Main Document Checksum:
  urn:sha256:71bdb3a3e3e53f284d15078b56a54c696c7946fa5c4302f71f5e8c3cf5864e16