Work

Statistical package for computing precision covariance matrices via modified Cholesky decomposition

Year: 2025

Type: software-paper

Abstract: We introduce a statistical package designed to compute precision covariance matrices using modified Cholesky decomposition, tailored for Atmospheric General Circulation Models (AGCMs). By incorporating predefined localization radius structures, the package achieves sparse precision matrix estimation, enhancing computational efficiency and scalability for large-scale atmospheric models. Leveraging ensemble modeling, the method ensures robustness and accuracy, with direct applications in data assimilation tasks. The package also features seamless integration with prominent climate datasets, such as ERA5 and MPAS, enabling streamlined workflows for climate modeling and prediction.

Source: SoftwareX

DOI: 10.1016/j.softx.2025.102125

Authors: Elías D. Niño-Ruiz, Dylan Samuel Cantillo Arrieta, Giuliano Raffaele Frieri Quiroz, Nicolas Quintero Quintero

Institution: Universidad del Norte

Language: English

Topic: Meteorological Phenomena and Simulations

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $1,200