TEMPEST - A modular Python-based thermophysical model for rough, irregularly-shaped airless bodies
Abstract
Comparing observed and predicted surface temperatures on airless planetary bodies is crucial for interpreting their surface processes, constraining activity, and deriving their surface properties. We present a new tool in this endeavour: TEMPEST (Thermophysical Evolution Model for Planetary Environment Surface Temperatures). It is a modular, open-source Python model for diurnal thermal evolution on irregular bodies. TEMPEST can reproduce non-Lambertian multiple scattering using arbitrary Bidirectional Reflectance Distribution Functions (BRDFs) and notably incorporates surface roughness on sub-facet scales through representative sphere-section geometry. The model includes an explicit time-stepping one-dimensional periodic conduction solver influenced by Spencer's thermal model Thermprojrs, and a semi-implicit Crank-Nicolson solver, and can be paired with other thermal solvers. Precomputed look-up tables enable fast application of scattering laws (Lambertian, Lommel-Seeliger, Hapke, or input empirical BRDF data), allowing the user to assess their effects in permanently shadowed regions or partially shadowed terrains. TEMPEST is parallelised, running on personal computers in minutes for thousands of facets and scaling to high-performance servers for models exceeding 100,000 facets. Validation was performed against Thermprojrs in one dimension, against Sorli's Binary Thermophysical Model in three dimensions, and with empirical thermal data from the Lunar Reconnaissance Orbiter, showing good agreement in each case. Application to the high-resolution shape-model of 25143 Itokawa shows the importance of self-shadowing and radiative mutual heating in controlling local temperatures. TEMPEST has already been used to analyse data from the Lucy Mission, Galileo, and ground-based observations, and is adaptable to upcoming datasets from targets of missions such as Comet Interceptor and Europa Clipper.
Versions
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BibTeX
@misc{lyster2026tempest,
title = {{TEMPEST - A modular Python-based thermophysical model for rough, irregularly-shaped airless bodies}},
author = {D. G. Lyster and C. J. A. Howett},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.40406},
eprint = {2610.40406},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.40406},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - TEMPEST - A modular Python-based thermophysical model for rough, irregularly-shaped airless bodies
AU - D. G. Lyster
AU - C. J. A. Howett
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.40406
UR - https://arxiv.org/abs/2610.40406
AB - Comparing observed and predicted surface temperatures on airless planetary bodies is crucial for interpreting their surface processes, constraining activity, and deriving their surface properties. We present a new tool in this endeavour: TEMPEST (Thermophysical Evolution Model for Planetary Environment Surface Temperatures). It is a modular, open-source Python model for diurnal thermal evolution on irregular bodies. TEMPEST can reproduce non-Lambertian multiple scattering using arbitrary Bidirectional Reflectance Distribution Functions (BRDFs) and notably incorporates surface roughness on sub-facet scales through representative sphere-section geometry. The model includes an explicit time-stepping one-dimensional periodic conduction solver influenced by Spencer's thermal model Thermprojrs, and a semi-implicit Crank-Nicolson solver, and can be paired with other thermal solvers. Precomputed look-up tables enable fast application of scattering laws (Lambertian, Lommel-Seeliger, Hapke, or input empirical BRDF data), allowing the user to assess their effects in permanently shadowed regions or partially shadowed terrains. TEMPEST is parallelised, running on personal computers in minutes for thousands of facets and scaling to high-performance servers for models exceeding 100,000 facets. Validation was performed against Thermprojrs in one dimension, against Sorli's Binary Thermophysical Model in three dimensions, and with empirical thermal data from the Lunar Reconnaissance Orbiter, showing good agreement in each case. Application to the high-resolution shape-model of 25143 Itokawa shows the importance of self-shadowing and radiative mutual heating in controlling local temperatures. TEMPEST has already been used to analyse data from the Lucy Mission, Galileo, and ground-based observations, and is adaptable to upcoming datasets from targets of missions such as Comet Interceptor and Europa Clipper.
ER -