Spatial Interpolation of Seasonal Precipitations Using Rain Gauge Data and Convection‐Permitting Regional Climate Model Simulations in a Complex Topographical Region DOI Creative Commons
Valentin Dura, Guillaume Évin, Anne‐Catherine Favre

et al.

International Journal of Climatology, Journal Year: 2024, Volume and Issue: 44(16), P. 5745 - 5760

Published: Oct. 27, 2024

ABSTRACT In mountainous areas, accurately estimating the long‐term climatology of seasonal precipitations is challenging due to lack high‐altitude rain gauges and complexity topography. This study addresses these challenges by interpolating precipitation data from 3189 across France over 1982–2018 period, using geographical coordinates, altitude. this study, an additional predictor provided simulations a Convection‐Permitting Regional Climate Model (CP‐RCM). The are averaged obtain climatology, which helps capture relationship between topography precipitation. Geostatistical machine learning models evaluated within cross‐validation framework determine most appropriate approach generate reference fields. Results indicate that best model uses interpolate ratio observations CP‐RCM simulations. method successfully reproduces both mean variance observed data, slightly outperforms geostatistical model. Moreover, incorporating outputs as explanatory variable significantly improves interpolation accuracy altitude extrapolation, especially when gauge density low. These results imply commonly used altitude‐precipitation may be insufficient derive simulations, increasingly available worldwide, present opportunity for improving interpolation, in sparse complex topographical regions.

Language: Английский

Spatial Interpolation of Seasonal Precipitations Using Rain Gauge Data and Convection‐Permitting Regional Climate Model Simulations in a Complex Topographical Region DOI Creative Commons
Valentin Dura, Guillaume Évin, Anne‐Catherine Favre

et al.

International Journal of Climatology, Journal Year: 2024, Volume and Issue: 44(16), P. 5745 - 5760

Published: Oct. 27, 2024

ABSTRACT In mountainous areas, accurately estimating the long‐term climatology of seasonal precipitations is challenging due to lack high‐altitude rain gauges and complexity topography. This study addresses these challenges by interpolating precipitation data from 3189 across France over 1982–2018 period, using geographical coordinates, altitude. this study, an additional predictor provided simulations a Convection‐Permitting Regional Climate Model (CP‐RCM). The are averaged obtain climatology, which helps capture relationship between topography precipitation. Geostatistical machine learning models evaluated within cross‐validation framework determine most appropriate approach generate reference fields. Results indicate that best model uses interpolate ratio observations CP‐RCM simulations. method successfully reproduces both mean variance observed data, slightly outperforms geostatistical model. Moreover, incorporating outputs as explanatory variable significantly improves interpolation accuracy altitude extrapolation, especially when gauge density low. These results imply commonly used altitude‐precipitation may be insufficient derive simulations, increasingly available worldwide, present opportunity for improving interpolation, in sparse complex topographical regions.

Language: Английский

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