The use of bivariate copulas for bias correction of reanalysis air temperature data : University of Twente

  • Sarah Alidoost (Creator)



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.

Earth sciences
Date made available18 Mar 2019
PublisherDANS easy
Temporal coverageJun 2008 - Jun 2019
Date of data production2008 - 2019
Geographical coverageQazvin Plain, Iran
Geospatial point36.299885, 50.018246

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