Multivariate copula quantile mapping for bias correction of reanalysis air temperature data

F. Alidoost*, A. Stein, B. Su, Ali Sharifi

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

3 Citations (Scopus)
25 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)299-315
Number of pages17
JournalJournal of spatial science
Volume66
Issue number2
Early online date6 May 2019
DOIs
Publication statusPublished - 4 May 2021

Keywords

  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-HYBRID
  • UT-Hybrid-D
  • Bias correction
  • conditional
  • copula
  • data scarce
  • mean temperature

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