Subseasonal prediction of tropical cyclone precipitation

Jorge L. García-Franco a Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY
b Escuela Nacional de Ciencias de la Tierra, Universidad Nacional Autónoma de México, Mexico City

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Chia-Ying Lee a Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY

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Michael K. Tippett c Department of Applied Physics and Applied Mathematics, Columbia University, NY

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Suzana J. Camargo a Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY

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Daehyun Kim d School of Earth and Environmental Sciencies, Seoul National University, Korea
e Department of Atmospheric Sciences, University of Washington, Seattle, Washington

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Andrea Molod f Global Modeling and Assimilation Office, Goddard Space Flight Center, NASA, Greenbelt, MD, USA

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Young-Kwon Lim f Global Modeling and Assimilation Office, Goddard Space Flight Center, NASA, Greenbelt, MD, USA
g University of Maryland, Baltimore County, Baltimore, MD, USA

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Abstract

The accurate prediction of tropical cyclone precipitation (TCP) at an extended-range could be crucial to mitigate the impacts of TC-related flooding. This study examines probabilistic predictions of weekly-accumulated TCP and total precipitation using 11 subseasonal forecast systems. Raw, uncalibrated, categorical forecasts of basin-wide TCP are only skillful in the ECMWF model and only up to 15 days in advance and except in the northern Indian Ocean and the South Pacific. Calibration, through linear regression, improves forecasts and makes several forecast systems (GEOS, UKMO) skillful up to 15 days in advance but only in some basins. In most models and basins, such as the GEOS model in the Atlantic basin, the bias in the forecast probability of TC occurrence is the main factor driving biases in TCP and decreasing forecast skill. At the regional-scale, calibrated ECMWF forecasts are skillful beyond 15 day leads and globally. The poor prediction of TCP in raw forecasts is shown to affect total precipitation prediction skill. Therefore, biases in the TC occurrence probability forecast is the leading cause of low skill of TCP and may play a role in the skill of total precipitation.

© 2025 American Meteorological Society. This is an Author Accepted Manuscript distributed under the terms of the default AMS reuse license. For information regarding reuse and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jorge L. García-Franco, jorgegf@ldeo.columbia.edu

Abstract

The accurate prediction of tropical cyclone precipitation (TCP) at an extended-range could be crucial to mitigate the impacts of TC-related flooding. This study examines probabilistic predictions of weekly-accumulated TCP and total precipitation using 11 subseasonal forecast systems. Raw, uncalibrated, categorical forecasts of basin-wide TCP are only skillful in the ECMWF model and only up to 15 days in advance and except in the northern Indian Ocean and the South Pacific. Calibration, through linear regression, improves forecasts and makes several forecast systems (GEOS, UKMO) skillful up to 15 days in advance but only in some basins. In most models and basins, such as the GEOS model in the Atlantic basin, the bias in the forecast probability of TC occurrence is the main factor driving biases in TCP and decreasing forecast skill. At the regional-scale, calibrated ECMWF forecasts are skillful beyond 15 day leads and globally. The poor prediction of TCP in raw forecasts is shown to affect total precipitation prediction skill. Therefore, biases in the TC occurrence probability forecast is the leading cause of low skill of TCP and may play a role in the skill of total precipitation.

© 2025 American Meteorological Society. This is an Author Accepted Manuscript distributed under the terms of the default AMS reuse license. For information regarding reuse and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jorge L. García-Franco, jorgegf@ldeo.columbia.edu
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