Articles | Volume 30, issue 18
https://doi.org/10.5194/hess-30-5791-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-5791-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Sep 2026
Research article |
| 15 Sep 2026
| 15 Sep 2026
Spatial pattern regression for meteorological fields interpolation
Vihotogbé Houssou
CORRESPONDING AUTHOR
Department of Mathematics and Industrial Engineering, Polytechnique Montréal, 2500 chemin de Polytechnique, Montréal, H3T 1J4, Québec, Canada
GERAD – Groupe d'Études et de Recherche en Analyse des Décisions, 2920 Chemin de la Tour, Montréal, H3T 1N8, Québec, Canada
IVADO – Institute for Data Valorization, 950 Av. Beaumont, Montréal, H3N 1V5, Québec, Canada
Julie Carreau
Department of Mathematics and Industrial Engineering, Polytechnique Montréal, 2500 chemin de Polytechnique, Montréal, H3T 1J4, Québec, Canada
GERAD – Groupe d'Études et de Recherche en Analyse des Décisions, 2920 Chemin de la Tour, Montréal, H3T 1N8, Québec, Canada
Mila – Quebec Artificial Intelligence Institute, 6666 Saint-Urbain, Montréal, H2S 3H1, Québec, Canada
IVADO – Institute for Data Valorization, 950 Av. Beaumont, Montréal, H3N 1V5, Québec, Canada
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Short summary
Spatial Pattern Regression (SPR) is a new way to reconstruct daily weather fields in regions with few measurement stations. Our approach combines information from past high-resolution simulations with available observations to produce more accurate maps of precipitations and temperature. Tests on both synthetic and real data show clear improvements over common methods, especially when stations are sparse, helping support better hydrological and climate studies.
Spatial Pattern Regression (SPR) is a new way to reconstruct daily weather fields in regions...
