Spatial Interpolation of Seasonal Precipitations Using Rain Gauge Data and Convection-Permitting Regional Climate Model Simulations in a Complex Topographical Region
IF 3.5 3区 地球科学Q2 METEOROLOGY & ATMOSPHERIC SCIENCES
Valentin Dura, Guillaume Evin, Anne-Catherine Favre, David Penot
{"title":"Spatial Interpolation of Seasonal Precipitations Using Rain Gauge Data and Convection-Permitting Regional Climate Model Simulations in a Complex Topographical Region","authors":"Valentin Dura, Guillaume Evin, Anne-Catherine Favre, David Penot","doi":"10.1002/joc.8662","DOIUrl":null,"url":null,"abstract":"<p>In mountainous areas, accurately estimating the long-term climatology of seasonal precipitations is challenging due to the lack of high-altitude rain gauges and the complexity of the topography. This study addresses these challenges by interpolating seasonal precipitation data from 3189 rain gauges across France over the 1982–2018 period, using geographical coordinates, and altitude. In this study, an additional predictor is provided from simulations of a Convection-Permitting Regional Climate Model (CP-RCM). The simulations are averaged to obtain seasonal precipitation climatology, which helps capture the relationship between topography and long-term seasonal precipitation. Geostatistical and machine learning models are evaluated within a cross-validation framework to determine the most appropriate approach to generate seasonal precipitation reference fields. Results indicate that the best model uses a machine learning approach to interpolate the ratio between long-term seasonal precipitation from observations and CP-RCM simulations. This method successfully reproduces both the mean and variance of observed data, and slightly outperforms the best geostatistical model. Moreover, incorporating the CP-RCM outputs as an explanatory variable significantly improves interpolation accuracy and altitude extrapolation, especially when the rain gauge density is low. These results imply that the commonly used altitude-precipitation relationship may be insufficient to derive seasonal precipitation fields. The CP-RCM simulations, increasingly available worldwide, present an opportunity for improving precipitation interpolation, especially in sparse and complex topographical regions.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 16","pages":"5745-5760"},"PeriodicalIF":3.5000,"publicationDate":"2024-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joc.8662","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Climatology","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/joc.8662","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
In mountainous areas, accurately estimating the long-term climatology of seasonal precipitations is challenging due to the lack of high-altitude rain gauges and the complexity of the topography. This study addresses these challenges by interpolating seasonal precipitation data from 3189 rain gauges across France over the 1982–2018 period, using geographical coordinates, and altitude. In this study, an additional predictor is provided from simulations of a Convection-Permitting Regional Climate Model (CP-RCM). The simulations are averaged to obtain seasonal precipitation climatology, which helps capture the relationship between topography and long-term seasonal precipitation. Geostatistical and machine learning models are evaluated within a cross-validation framework to determine the most appropriate approach to generate seasonal precipitation reference fields. Results indicate that the best model uses a machine learning approach to interpolate the ratio between long-term seasonal precipitation from observations and CP-RCM simulations. This method successfully reproduces both the mean and variance of observed data, and slightly outperforms the best geostatistical model. Moreover, incorporating the CP-RCM outputs as an explanatory variable significantly improves interpolation accuracy and altitude extrapolation, especially when the rain gauge density is low. These results imply that the commonly used altitude-precipitation relationship may be insufficient to derive seasonal precipitation fields. The CP-RCM simulations, increasingly available worldwide, present an opportunity for improving precipitation interpolation, especially in sparse and complex topographical regions.
期刊介绍:
The International Journal of Climatology aims to span the well established but rapidly growing field of climatology, through the publication of research papers, short communications, major reviews of progress and reviews of new books and reports in the area of climate science. The Journal’s main role is to stimulate and report research in climatology, from the expansive fields of the atmospheric, biophysical, engineering and social sciences. Coverage includes: Climate system science; Local to global scale climate observations and modelling; Seasonal to interannual climate prediction; Climatic variability and climate change; Synoptic, dynamic and urban climatology, hydroclimatology, human bioclimatology, ecoclimatology, dendroclimatology, palaeoclimatology, marine climatology and atmosphere-ocean interactions; Application of climatological knowledge to environmental assessment and management and economic production; Climate and society interactions