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The use of bivariate copulas for bias correction of reanalysis air temperature data
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-c...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6505955/ https://www.ncbi.nlm.nih.gov/pubmed/31067243 http://dx.doi.org/10.1371/journal.pone.0216059 |
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author | Alidoost, Fakhereh Stein, Alfred Su, Zhongbo |
author_facet | Alidoost, Fakhereh Stein, Alfred Su, Zhongbo |
author_sort | Alidoost, Fakhereh |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6505955 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-65059552019-05-23 The use of bivariate copulas for bias correction of reanalysis air temperature data Alidoost, Fakhereh Stein, Alfred Su, Zhongbo PLoS One Research Article 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. Public Library of Science 2019-05-08 /pmc/articles/PMC6505955/ /pubmed/31067243 http://dx.doi.org/10.1371/journal.pone.0216059 Text en © 2019 Alidoost et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Alidoost, Fakhereh Stein, Alfred Su, Zhongbo The use of bivariate copulas for bias correction of reanalysis air temperature data |
title | The use of bivariate copulas for bias correction of reanalysis air temperature data |
title_full | The use of bivariate copulas for bias correction of reanalysis air temperature data |
title_fullStr | The use of bivariate copulas for bias correction of reanalysis air temperature data |
title_full_unstemmed | The use of bivariate copulas for bias correction of reanalysis air temperature data |
title_short | The use of bivariate copulas for bias correction of reanalysis air temperature data |
title_sort | use of bivariate copulas for bias correction of reanalysis air temperature data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6505955/ https://www.ncbi.nlm.nih.gov/pubmed/31067243 http://dx.doi.org/10.1371/journal.pone.0216059 |
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