Multivariate copula quantile mapping for bias correction of reanalysis air temperature data. Issue 2 (4th May 2021)

• Record Type: Journal Article
• Authors: Alidoost, Fakhereh; Stein, Alfred; Su, Zhongbo; Sharifi, Ali
• Is Part Of: Journal of spatial science. Volume 66:Issue 2(2021)
• Page Start: 299
• Page End: 315
• Publication Date: 2021-05-04
• Subjects: Bias correction -- copula -- conditional -- mean temperature -- data scarce; Geographic information systems -- Periodicals; Cartography -- Periodicals; Surveying -- Periodicals; Geodesy -- Periodicals; Photogrammetry -- Periodicals; Cartography; Geodesy; Geographic information systems; Photogrammetry; Surveying; Periodicals; 526.05
• DOI: 10.1080/14498596.2019.1601138
• Languages: English
• ISSNs: 1449-8596

Abstract

ABSTRACT: Reanalysis data retrieved from the European Centre for Medium-range Weather Forecasts (ECMWF) are commonly used for hydrological studies. Their use requires bias correction, defined as the difference between reanalysis values and measurements. We propose three multivariate copula quantile mappings (MCQMs) to predict bias-corrected values at unvisited locations. We apply the methods to the Qazvin Plain, Iran, for daily air temperature retrieved from weather stations and the ECMWF archive. Results showed that MCQMs reduced bias by 46% as compared with classical quantile mapping. The study concludes that MCQMs are well able to represent the spatial and temporal variation of air temperature.