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  • 标题:Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data
  • 本地全文:下载
  • 作者:Ménard, Cécile B. ; Essery, Richard ; Barr, Alan
  • 期刊名称:Earth System Science Data Discussions
  • 电子版ISSN:1866-3591
  • 出版年度:2019
  • 卷号:11
  • 期号:2
  • 页码:865-880
  • DOI:10.5194/essd-11-865-2019
  • 语种:English
  • 出版社:Copernicus Publications
  • 摘要:This paper describes in situ meteorological forcing and evaluation data, andbias-corrected reanalysis forcing data, for cold regions' modelling at 10sites. The long-term datasets (one maritime, one arctic, three boreal, andfive mid-latitude alpine) are the reference sites chosen for evaluatingmodels participating in the Earth System Model-Snow Model IntercomparisonProject. Periods covered by the in situ data vary between 7 and 20 years of hourly meteorological data, with evaluation data (snow depth, snowwater equivalent, albedo, soil temperature, and surface temperature)available at varying temporal intervals. Thirty-year (1980–2010) time serieshave been extracted from a global gridded surface meteorology dataset(Global Soil Wetness Project Phase 3) for the grid cells containing thereference sites, interpolated to 1h time steps and bias-corrected.Although the correction was applied to all sites, it was most important formountain sites hundreds of metres higher than the grid elevations and forwhich uncorrected air temperatures were too high and snowfall amounts toolow. The discussion considers the importance of data sharing to theidentification of errors and how the publication of these datasetscontributes to good practice, consistency, and reproducibility ingeosciences. The Supplement provides information on instrumentation,an estimate of the percentages of missing values, and gap-filling methods ateach site. It is hoped that these datasets will be used as benchmarks forfuture model development and that their ease of use and availability willhelp model developers quantify model uncertainties and reduce model errors.The data are published in the repository PANGAEA and are available athttps://doi.pangaea.de/10.1594/PANGAEA.897575.
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