Articles | Volume 11, issue 2
https://doi.org/10.5194/essd-11-865-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-11-865-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
17 Jun 2019
Data description paper |
| 17 Jun 2019
| 17 Jun 2019
Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data
School of Geosciences, University of Edinburgh, Edinburgh, UK
Richard Essery
School of Geosciences, University of Edinburgh, Edinburgh, UK
Alan Barr
Global Institute for Water Security, University of Saskatchewan, Saskatoon, Canada
Climate Research Division, Environment and Climate Change Canada, Toronto, Canada
Paul Bartlett
Climate Research Division, Environment and Climate Change Canada, Toronto, Canada
Jeff Derry
Center for Snow and Avalanche Studies, Silverton, Colorado, USA
Marie Dumont
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Etudes de la Neige, Grenoble, France
Charles Fierz
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Hyungjun Kim
Institute of Industrial Science, University of Tokyo, Tokyo, Japan
Anna Kontu
Finnish Meteorological Institute, Space and Earth Observation Centre, Sodankylä, Finland
Yves Lejeune
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Etudes de la Neige, Grenoble, France
Danny Marks
Northwest Watershed Research Center, Agricultural Research Service, Boise, Idaho, USA
Masashi Niwano
Climate Research Department, Meteorological Research Institute, Tsukuba, Japan
Mark Raleigh
National Snow and Ice Data Center (NSIDC), University of Colorado Boulder, Boulder, Colorado, USA
Libo Wang
Climate Research Division, Environment and Climate Change Canada, Toronto, Canada
Nander Wever
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO, USA
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35 citations as recorded by crossref.
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- The Influence of Key Climate Variables on Mass Balance of Naimona'nyi Glacier on a North‐Facing Slope in the Western Himalayas M. Zhu et al. 10.1029/2020JD033956
- Impact of a Multi‐Layer Snow Scheme on Near‐Surface Weather Forecasts G. Arduini et al. 10.1029/2019MS001725
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- Simulating the Dynamics of the Characteristics of Snow Cover Formation Regime in the Russian Federation Territory. 2. Forest Areas of ER in the Historical Period E. Gusev et al. 10.1134/S0097807823040115
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- The RHOSSA campaign: multi-resolution monitoring of the seasonal evolution of the structure and mechanical stability of an alpine snowpack N. Calonne et al. 10.5194/tc-14-1829-2020
- Modification and Validation of the Soil–Snow Module in the INM RAS Climate Model A. Chernenkov et al. 10.3390/atmos15040422
- ECLand: The ECMWF Land Surface Modelling System S. Boussetta et al. 10.3390/atmos12060723
- Simulating the Dynamics of the Characteristics of Snow Cover Formation Regime in the Russian Federation Territory. 2. Forest Areas of ER in the Historical Period E. Gusev et al. 10.31857/S0321059623040119
- Assimilation of surface reflectance in snow simulations: Impact on bulk snow variables J. Revuelto et al. 10.1016/j.jhydrol.2021.126966
- GEMS v1.0: Generalizable Empirical Model of Snow Accumulation and Melt, based on daily snow mass changes in response to climate and topographic drivers A. Umirbekov et al. 10.5194/gmd-17-911-2024
- Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling V. Sharma et al. 10.5194/gmd-16-719-2023
- A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning Y. Hu et al. 10.1080/20964471.2023.2177435
- Snow cover duration trends observed at sites and predicted by multiple models R. Essery et al. 10.5194/tc-14-4687-2020
- On the energy budget of a low-Arctic snowpack G. Lackner et al. 10.5194/tc-16-127-2022
- Laboratory, field, mast-borne and airborne spectral reflectance measurements of boreal landscape during spring H. Hannula et al. 10.5194/essd-12-719-2020
- AI methods application for classifying stratigraphic layers of snow cover with Snow micro pen device D. Frolov et al. 10.1051/bioconf/20248402009
- Scientific and Human Errors in a Snow Model Intercomparison C. Menard et al. 10.1175/BAMS-D-19-0329.1
- How does humidity data impact land surface modeling of hydrothermal regimes at a permafrost site in Utqiaġvik, Alaska? X. Huang et al. 10.1016/j.scitotenv.2023.168697
- Simulating the Dynamics of the Characteristics of Snow Cover Formation Regime in the Russian Federation Territory. 1. Field Areas of ER in the Historical Period E. Gusev et al. 10.31857/S0321059623040107
- COSIPY v1.3 – an open-source coupled snowpack and ice surface energy and mass balance model T. Sauter et al. 10.5194/gmd-13-5645-2020
- Evaluation of E3SM land model snow simulations over the western United States D. Hao et al. 10.5194/tc-17-673-2023
- Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review C. Largeron et al. 10.3389/feart.2020.00325
- Using Machine Learning to Classify Stratigraphic Layers of Snow According to the Snow Micro Pen Device D. Frolov et al. 10.7256/2453-8922.2024.1.69404
- Scenario Projections of Changes in Snow Water Equivalent Due to Possible Climate Changes in Different Regions of the Earth E. Gusev et al. 10.1134/S0097807821010176
- Characterizing snow instability with avalanche problem types derived from snow cover simulations B. Reuter et al. 10.1016/j.coldregions.2021.103462
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1 citations as recorded by crossref.
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Short summary
This paper describes long-term meteorological and evaluation datasets from 10 reference sites for use in snow modelling. We demonstrate how data sharing is crucial to the identification of errors and how the publication of these datasets contributes to good practice, consistency, and reproducibility in geosciences. The ease of use, availability, and quality of the datasets will help model developers quantify and reduce model uncertainties and errors.
This paper describes long-term meteorological and evaluation datasets from 10 reference sites...
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