pyTCR: A tropical cyclone rainfall model for python

Le, Phong V. V.; Persad, Geeta; Perez, Gabriel; Nduka, Ifeanyichukwu C.; Mobley, William; Coon, Ethan T.

doi:10.21105/joss.08074

pyTCR: A tropical cyclone rainfall model for python

Journal Article · Wed Jun 18 00:00:00 EDT 2025 · Journal of Open Source Software

DOI:https://doi.org/10.21105/joss.08074· OSTI ID:2573339

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Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Univ. of Texas, Austin, TX (United States)
Oklahoma State Univ., Stillwater, OK (United States)

pyTCR is a climatology software package developed in the Python programming language. It integrates the capabilities of several legacy physical models and increases computational efficiency to allow rapid estimation of tropical cyclone (TC) rainfall consistent with the large-scale environment. Specifically, pyTCR implements a horizontally distributed and vertically integrated model [Zhu et al., 2013] for simulating rainfall driven by TCs. Along storm tracks, rainfall is estimated by computing the cross-boundary-layer, upward water vapor transport caused by different mechanisms including frictional convergence, vortex stretching, large-scale baroclinic effect (i.e., wind shear), topographic forcing, and radiative cooling [Lu et al., 2018]. The package provides essential functionalities for modeling and interpreting spatio-temporal TC rainfall data. pyTCR requires a limited number of model input parameters, making it a convenient and useful tool for analyzing rainfall mechanisms driven by TCs. To sample rare (most intense) rainfall events that are often of great societal interest, pyTCR adapts and leverages outputs from a statistical-dynamical TC downscaling model [Lin et al., 2023] capable of rapidly generating a large number of synthetic TCs given a certain climate. As a result, pyTCR significantly reduces computational effort and improves the efficiency in capturing extreme TC rainfall events at the tail of the distributions from limited datasets. Furthermore, the TC downscaling model is forced entirely by large-scale environmental conditions from reanalysis data or coupled General Circulation Models (GCMs), simplifying the projection of TC-induced rainfall and wind speed under future climate using pyTCR. Finally, pyTCR can be coupled with hydrological and wind models to assess risks associated with independent and compound events (e.g., storm surges and freshwater flooding).

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2573339

Journal Information:: Journal of Open Source Software, Journal Name: Journal of Open Source Software Journal Issue: 110 Vol. 10; ISSN 2475-9066

Publisher:: Open Source Initiative - NumFOCUSCopyright Statement

Country of Publication:: United States

Language:: English

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