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

Zhongbo Su,Fakhereh Alidoost,Alfred Stein

doi:10.1371/journal.pone.0216059

Abstract

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. Leave-k-out cross-validation and correlation scores show that the new methods reduced the bias with 44–68% for the full data set and with 34–74% on a homogeneous subarea. We conclude that the two methods are able to locally improve ECMWF air temperatures in a data-scarce area.

Highlights

Assessment of the impact of climate change in agricultural areas is primarily based upon changes in weather data such as air temperature [1]
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 European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-interim grid cells from a single month (June) over 11 years

Summary

Introduction

Assessment of the impact of climate change in agricultural areas is primarily based upon changes in weather data such as air temperature [1]. The European Centre for Mediumrange Weather Forecasts (ECMWF) provides gridded ERA-interim reanalysis weather data that are being used increasingly [2]. They are prone to uncertainty because of the coarse resolution of models and variability of model parameters in space and time [3,4]. A copula is a joint distribution function, describing the dependence structure between two or more variables [6]. Copula-based methods have been developed to correct bias in dependent variables [8,9]. Copula-based methods are applied for deriving bias-corrected

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Conclusion