Articles | Volume 15, issue 24
https://doi.org/10.5194/gmd-15-9127-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-9127-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
21 Dec 2022
Model description paper |
| 21 Dec 2022
| 21 Dec 2022
The Multiple Snow Data Assimilation System (MuSA v1.0)
Esteban Alonso-González
CORRESPONDING AUTHOR
Centre d'Etudes Spatiales de la Biosphère, Université de Toulouse, CNRS–CNES–IRD–INRA–UPS, Toulouse, France
Kristoffer Aalstad
Department of Geosciences, University of Oslo, Oslo, Norway
Mohamed Wassim Baba
Center for Remote Sensing Application (CRSA), Mohammed VI Polytechnic University (UM6P), Ben Guerir, Morocco
Jesús Revuelto
Instituto Pirenaico de Ecología, CSIC, Zaragoza, Spain
Juan Ignacio López-Moreno
Instituto Pirenaico de Ecología, CSIC, Zaragoza, Spain
Joel Fiddes
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Richard Essery
School of GeoSciences, University of Edinburgh, Edinburgh, UK
Simon Gascoin
Centre d'Etudes Spatiales de la Biosphère, Université de Toulouse, CNRS–CNES–IRD–INRA–UPS, Toulouse, France
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Cited
27 citations as recorded by crossref.
- Snow Water Equivalent from airborne Ku-band data: the Trail Valley Creek 2018/19 snow experiment B. Montpetit et al.
- The Multiple Snow Data Assimilation System (MuSA v1.0) E. Alonso-González et al.
- A Flexible Snow Model (FSM 2.1.1) including a forest canopy R. Essery et al.
- Ensemble-based data assimilation improves hyperresolution snowpack simulations in forests E. Alonso-González et al.
- Exploring the potential of forest snow modeling at the tree and snowpack layer scale G. Mazzotti et al.
- Retrieving the irrigation actually applied at district scale: Assimilating high-resolution Sentinel-1-derived soil moisture data into a FAO-56-based model P. Laluet et al.
- Learning to filter: snow data assimilation using a Long Short-Term Memory network G. Blandini et al.
- Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas L. Bührle et al.
- Enhancing simulations of snowpack properties in land surface models with the Soil, Vegetation and Snow scheme v2.0 (SVS2) V. Vionnet et al.
- Enhancing snow depth estimation with snow cover geometrical descriptors L. Ferrarin et al.
- Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment E. Alonso-González et al.
- Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation E. Alonso-González et al.
- Rain-on-snow responses to warmer Pyrenees: a sensitivity analysis using a physically based snow hydrological model J. Bonsoms et al.
- Future permafrost degradation under climate change in a headwater catchment of central Siberia: quantitative assessment with a mechanistic modelling approach T. Xavier et al.
- Remote sensing of mountain snow from space: status and recommendations S. Gascoin et al.
- Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter M. Mazzolini et al.
- Evaluating the utility of Sentinel-1 in a Data Assimilation System for estimating snow depth in a mountainous basin B. Mirza et al.
- Advances and prospects in reconstruction approaches for snow cover mapping using polar-orbiting satellites J. Zhang et al.
- Recent Advances in Snow Monitoring from Local to Global Scales J. Revuelto et al.
- TopoPyScale: A Python Package for Hillslope Climate Downscaling S. Filhol et al.
- Numerical simulations of recent and future evolution of Monte Perdido glacier A. Mateos-García et al.
- Spatio‐temporal wet snow dynamics from model simulations and remote sensing: A case study from the Rofental, Austria E. Rottler et al.
- Improved modelling of mountain snowpacks with spatially distributed precipitation bias correction derived from historical reanalysis M. von Kaenel & S. Margulis
- A combined data assimilation and deep learning approach for continuous spatio-temporal SWE reconstruction from sparse ground tracks M. Guidicelli et al.
- The response of marginal snowpacks to climate warming J. LÓpez Moreno et al.
- Advancing snow data assimilation with a dynamic observation uncertainty D. Dunmire et al.
- Forest Snow Patterns Derived Using ClustSnow Are Temporally Persistent Under Variable Environmental Conditions J. Geissler et al.
27 citations as recorded by crossref.
- Snow Water Equivalent from airborne Ku-band data: the Trail Valley Creek 2018/19 snow experiment B. Montpetit et al.
- The Multiple Snow Data Assimilation System (MuSA v1.0) E. Alonso-González et al.
- A Flexible Snow Model (FSM 2.1.1) including a forest canopy R. Essery et al.
- Ensemble-based data assimilation improves hyperresolution snowpack simulations in forests E. Alonso-González et al.
- Exploring the potential of forest snow modeling at the tree and snowpack layer scale G. Mazzotti et al.
- Retrieving the irrigation actually applied at district scale: Assimilating high-resolution Sentinel-1-derived soil moisture data into a FAO-56-based model P. Laluet et al.
- Learning to filter: snow data assimilation using a Long Short-Term Memory network G. Blandini et al.
- Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas L. Bührle et al.
- Enhancing simulations of snowpack properties in land surface models with the Soil, Vegetation and Snow scheme v2.0 (SVS2) V. Vionnet et al.
- Enhancing snow depth estimation with snow cover geometrical descriptors L. Ferrarin et al.
- Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment E. Alonso-González et al.
- Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation E. Alonso-González et al.
- Rain-on-snow responses to warmer Pyrenees: a sensitivity analysis using a physically based snow hydrological model J. Bonsoms et al.
- Future permafrost degradation under climate change in a headwater catchment of central Siberia: quantitative assessment with a mechanistic modelling approach T. Xavier et al.
- Remote sensing of mountain snow from space: status and recommendations S. Gascoin et al.
- Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter M. Mazzolini et al.
- Evaluating the utility of Sentinel-1 in a Data Assimilation System for estimating snow depth in a mountainous basin B. Mirza et al.
- Advances and prospects in reconstruction approaches for snow cover mapping using polar-orbiting satellites J. Zhang et al.
- Recent Advances in Snow Monitoring from Local to Global Scales J. Revuelto et al.
- TopoPyScale: A Python Package for Hillslope Climate Downscaling S. Filhol et al.
- Numerical simulations of recent and future evolution of Monte Perdido glacier A. Mateos-García et al.
- Spatio‐temporal wet snow dynamics from model simulations and remote sensing: A case study from the Rofental, Austria E. Rottler et al.
- Improved modelling of mountain snowpacks with spatially distributed precipitation bias correction derived from historical reanalysis M. von Kaenel & S. Margulis
- A combined data assimilation and deep learning approach for continuous spatio-temporal SWE reconstruction from sparse ground tracks M. Guidicelli et al.
- The response of marginal snowpacks to climate warming J. LÓpez Moreno et al.
- Advancing snow data assimilation with a dynamic observation uncertainty D. Dunmire et al.
- Forest Snow Patterns Derived Using ClustSnow Are Temporally Persistent Under Variable Environmental Conditions J. Geissler et al.
Saved (final revised paper)
Latest update: 02 May 2026
Short summary
Snow cover plays an important role in many processes, but its monitoring is a challenging task. The alternative is usually to simulate the snowpack, and to improve these simulations one of the most promising options is to fuse simulations with available observations (data assimilation). In this paper we present MuSA, a data assimilation tool which facilitates the implementation of snow monitoring initiatives, allowing the assimilation of a wide variety of remotely sensed snow cover information.
Snow cover plays an important role in many processes, but its monitoring is a challenging task....
