EASY

DANS - Data Archiving and Networked Services

 

Can we ask you a few questions about EASY? More information.

 

Search datasets

EASY offers sustainable archiving of research data and access to thousands of datasets.

Close Search help

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

Cite as:

Alidoost, F. (Faculty of Geo-Information and Earth Observation, (ITC) University of Twente) (): The use of bivariate copulas for bias correction of reanalysis air temperature data. DANS. https://doi.org/10.17026/dans-z4g-eh9r

2019-03-18 Alidoost, F. (Faculty of Geo-Information and Earth Observation, (ITC) University of Twente) 10.17026/dans-z4g-eh9r

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.