Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data
International audience Abstract. This paper describes in situ meteorological forcing and evaluation data, and bias-corrected reanalysis forcing data, for cold regions' modelling at 10 sites. The long-term datasets (one maritime, one arctic, three boreal, and five mid-latitude alpine) are the re...
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Online Access: | https://meteofrance.hal.science/meteo-03657924 https://meteofrance.hal.science/meteo-03657924/document https://meteofrance.hal.science/meteo-03657924/file/essd-11-865-2019.pdf https://doi.org/10.5194/essd-11-865-2019 |
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ftmeteofrance:oai:HAL:meteo-03657924v1 2024-05-19T07:27:49+00:00 Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data Ménard, Cécile Essery, Richard Barr, Alan Bartlett, Paul Derry, Jeff Dumont, Marie Fierz, Charles Kim, Hyungjun Kontu, Anna Lejeune, Yves Marks, Danny Niwano, Masashi Raleigh, Mark Wang, Libo Wever, Nander ANR-16-CE01-0006,EBONI,Dépot, devenir et impact des impuretés absorbantes dans le manteau neigeux(2016) 2019 https://meteofrance.hal.science/meteo-03657924 https://meteofrance.hal.science/meteo-03657924/document https://meteofrance.hal.science/meteo-03657924/file/essd-11-865-2019.pdf https://doi.org/10.5194/essd-11-865-2019 en eng HAL CCSD Copernicus Publications info:eu-repo/semantics/altIdentifier/doi/10.5194/essd-11-865-2019 meteo-03657924 https://meteofrance.hal.science/meteo-03657924 https://meteofrance.hal.science/meteo-03657924/document https://meteofrance.hal.science/meteo-03657924/file/essd-11-865-2019.pdf doi:10.5194/essd-11-865-2019 http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess ISSN: 1866-3508 Earth System Science Data https://meteofrance.hal.science/meteo-03657924 Earth System Science Data, 2019, 11 (2), pp.865-880. ⟨10.5194/essd-11-865-2019⟩ [SDU.STU]Sciences of the Universe [physics]/Earth Sciences info:eu-repo/semantics/article Journal articles 2019 ftmeteofrance https://doi.org/10.5194/essd-11-865-2019 2024-04-25T00:50:14Z International audience Abstract. This paper describes in situ meteorological forcing and evaluation data, and bias-corrected reanalysis forcing data, for cold regions' modelling at 10 sites. The long-term datasets (one maritime, one arctic, three boreal, and five mid-latitude alpine) are the reference sites chosen for evaluating models participating in the Earth System Model-Snow Model Intercomparison Project. Periods covered by the in situ data vary between 7 and 20 years of hourly meteorological data, with evaluation data (snow depth, snow water equivalent, albedo, soil temperature, and surface temperature) available at varying temporal intervals. Thirty-year (1980–2010) time series have been extracted from a global gridded surface meteorology dataset (Global Soil Wetness Project Phase 3) for the grid cells containing the reference sites, interpolated to 1 h time steps and bias-corrected. Although the correction was applied to all sites, it was most important for mountain sites hundreds of metres higher than the grid elevations and for which uncorrected air temperatures were too high and snowfall amounts too low. The discussion considers the importance of data sharing to the identification of errors and how the publication of these datasets contributes to good practice, consistency, and reproducibility in geosciences. The Supplement provides information on instrumentation, an estimate of the percentages of missing values, and gap-filling methods at each site. It is hoped that these datasets will be used as benchmarks for future model development and that their ease of use and availability will help model developers quantify model uncertainties and reduce model errors. The data are published in the repository PANGAEA and are available at https://doi.pangaea.de/10.1594/PANGAEA.897575. Article in Journal/Newspaper albedo Arctic Météo-France: HAL Earth System Science Data 11 2 865 880 |
institution |
Open Polar |
collection |
Météo-France: HAL |
op_collection_id |
ftmeteofrance |
language |
English |
topic |
[SDU.STU]Sciences of the Universe [physics]/Earth Sciences |
spellingShingle |
[SDU.STU]Sciences of the Universe [physics]/Earth Sciences Ménard, Cécile Essery, Richard Barr, Alan Bartlett, Paul Derry, Jeff Dumont, Marie Fierz, Charles Kim, Hyungjun Kontu, Anna Lejeune, Yves Marks, Danny Niwano, Masashi Raleigh, Mark Wang, Libo Wever, Nander Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data |
topic_facet |
[SDU.STU]Sciences of the Universe [physics]/Earth Sciences |
description |
International audience Abstract. This paper describes in situ meteorological forcing and evaluation data, and bias-corrected reanalysis forcing data, for cold regions' modelling at 10 sites. The long-term datasets (one maritime, one arctic, three boreal, and five mid-latitude alpine) are the reference sites chosen for evaluating models participating in the Earth System Model-Snow Model Intercomparison Project. Periods covered by the in situ data vary between 7 and 20 years of hourly meteorological data, with evaluation data (snow depth, snow water equivalent, albedo, soil temperature, and surface temperature) available at varying temporal intervals. Thirty-year (1980–2010) time series have been extracted from a global gridded surface meteorology dataset (Global Soil Wetness Project Phase 3) for the grid cells containing the reference sites, interpolated to 1 h time steps and bias-corrected. Although the correction was applied to all sites, it was most important for mountain sites hundreds of metres higher than the grid elevations and for which uncorrected air temperatures were too high and snowfall amounts too low. The discussion considers the importance of data sharing to the identification of errors and how the publication of these datasets contributes to good practice, consistency, and reproducibility in geosciences. The Supplement provides information on instrumentation, an estimate of the percentages of missing values, and gap-filling methods at each site. It is hoped that these datasets will be used as benchmarks for future model development and that their ease of use and availability will help model developers quantify model uncertainties and reduce model errors. The data are published in the repository PANGAEA and are available at https://doi.pangaea.de/10.1594/PANGAEA.897575. |
author2 |
ANR-16-CE01-0006,EBONI,Dépot, devenir et impact des impuretés absorbantes dans le manteau neigeux(2016) |
format |
Article in Journal/Newspaper |
author |
Ménard, Cécile Essery, Richard Barr, Alan Bartlett, Paul Derry, Jeff Dumont, Marie Fierz, Charles Kim, Hyungjun Kontu, Anna Lejeune, Yves Marks, Danny Niwano, Masashi Raleigh, Mark Wang, Libo Wever, Nander |
author_facet |
Ménard, Cécile Essery, Richard Barr, Alan Bartlett, Paul Derry, Jeff Dumont, Marie Fierz, Charles Kim, Hyungjun Kontu, Anna Lejeune, Yves Marks, Danny Niwano, Masashi Raleigh, Mark Wang, Libo Wever, Nander |
author_sort |
Ménard, Cécile |
title |
Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data |
title_short |
Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data |
title_full |
Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data |
title_fullStr |
Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data |
title_full_unstemmed |
Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data |
title_sort |
meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data |
publisher |
HAL CCSD |
publishDate |
2019 |
url |
https://meteofrance.hal.science/meteo-03657924 https://meteofrance.hal.science/meteo-03657924/document https://meteofrance.hal.science/meteo-03657924/file/essd-11-865-2019.pdf https://doi.org/10.5194/essd-11-865-2019 |
genre |
albedo Arctic |
genre_facet |
albedo Arctic |
op_source |
ISSN: 1866-3508 Earth System Science Data https://meteofrance.hal.science/meteo-03657924 Earth System Science Data, 2019, 11 (2), pp.865-880. ⟨10.5194/essd-11-865-2019⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.5194/essd-11-865-2019 meteo-03657924 https://meteofrance.hal.science/meteo-03657924 https://meteofrance.hal.science/meteo-03657924/document https://meteofrance.hal.science/meteo-03657924/file/essd-11-865-2019.pdf doi:10.5194/essd-11-865-2019 |
op_rights |
http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.5194/essd-11-865-2019 |
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Earth System Science Data |
container_volume |
11 |
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2 |
container_start_page |
865 |
op_container_end_page |
880 |
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