swxg: A Python Library for Generalized Multivariate, Multisite, Copula-Based Stochastic Weather Generation

Alexander B. Thames; Antonia Hadjimichael; Julianne D. Quinn

doi:10.5334/jors.651

swxg: A Python Library for Generalized Multivariate, Multisite, Copula-Based Stochastic Weather Generation

Journal of Open Research Software

Volume 14 (2026): Issue 1

By: Alexander B. Thames , Antonia Hadjimichael and Julianne D. Quinn

Open Access

|Jun 2026

Abstract

swxg is a Python library for generalized multivariate, multisite, copula-based stochastic weather generation. It addresses the challenge of generating realistic, spatially and temporally correlated precipitation and temperature data for water resources planning and climate vulnerability assessments. The library uses semiparametric fitting for precipitation via Gaussian mixture hidden Markov models, followed by conditional copula-based temperature generation. swxg includes comprehensive validation tools to assess goodness of fit and ensure generated weather is statistically indistinguishable from observations. The software is openly available on GitHub under an MIT license and is designed for flexible integration into exploratory modeling frameworks, making it suitable for diverse hydroclimatic applications requiring synthetic weather data across multiple sites.

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Articles in this issue

DOI: https://doi.org/10.5334/jors.651 | Journal eISSN: 2049-9647

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Language: English

Page range: 47 - 47

Submitted on: Dec 1, 2025

Accepted on: May 26, 2026

Published on: Jun 19, 2026

Published by: Ubiquity Press

In partnership with: Paradigm Publishing Services

Keywords:

stochastic weather generator,

Python,

copula,

© 2026 Alexander B. Thames, Antonia Hadjimichael, Julianne D. Quinn, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 14 (2026): Issue 1