Air temperature data retrieved from global atmospheric models may show a systematic bias with respect to measurements from weather stations. This is a common concern in local climate studies. The current study presents two methods based upon copulas and Conditional Probability (CP) to predict bias-corrected mean air temperature in a data-scarce environment: CP-I offers a single conditional probability as a predictor, CP-II is a pixel-wise version of CP-I and offers spatially varying predictors. The methods were applied on daily air temperature in the Qazvin Plain, Iran that were collected at 24 weather stations and 150 ECMWF ERA-interim grid cells from a single month (June) over 11 years. We compared the methods with the commonly applied conditional expectation and conditional median methods.
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The weather stations data are provided by the SAJ Consulting firm in Iran."]}]},"dansRights":{"displayName":"Rights Metadata","name":"dansRights","fields":[{"typeName":"dansRightsHolder","multiple":true,"typeClass":"primitive","value":["Unknown"]},{"typeName":"dansPersonalDataPresent","multiple":false,"typeClass":"controlledVocabulary","value":"Unknown"}]},"dansRelationMetadata":{"displayName":"Relation Metadata","name":"dansRelationMetadata","fields":[{"typeName":"dansAudience","multiple":true,"typeClass":"primitive","value":["https://www.narcis.nl/classification/D15000"],"expandedvalue":[{"@id":"https://www.narcis.nl/classification/D15000","termName":[{"lang":"nl","value":"Aardwetenschappen"},{"lang":"en","value":"Earth sciences"}],"vocabularyUri":"https://www.narcis.nl/classification/"}]}]},"dansDataVaultMetadata":{"displayName":"Data Vault 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