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
14 citations as recorded by crossref.
- The Multiple Snow Data Assimilation System (MuSA v1.0) E. Alonso-González et al. 10.5194/gmd-15-9127-2022
- Exploring the potential of forest snow modeling at the tree and snowpack layer scale G. Mazzotti et al. 10.5194/tc-18-4607-2024
- 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. 10.1016/j.agwat.2024.108704
- TopoPyScale: A Python Package for Hillslope Climate Downscaling S. Filhol et al. 10.21105/joss.05059
- Numerical simulations of recent and future evolution of Monte Perdido glacier A. Mateos-García et al. 10.18172/cig.5816
- Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas L. Bührle et al. 10.5194/tc-17-3383-2023
- Spatio‐temporal wet snow dynamics from model simulations and remote sensing: A case study from the Rofental, Austria E. Rottler et al. 10.1002/hyp.15279
- Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment E. Alonso-González et al. 10.5194/tc-17-3329-2023
- Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation E. Alonso-González et al. 10.5194/hess-27-4637-2023
- Rain-on-snow responses to warmer Pyrenees: a sensitivity analysis using a physically based snow hydrological model J. Bonsoms et al. 10.5194/nhess-24-245-2024
- A combined data assimilation and deep learning approach for continuous spatio-temporal SWE reconstruction from sparse ground tracks M. Guidicelli et al. 10.1016/j.hydroa.2024.100190
- Remote sensing of mountain snow from space: status and recommendations S. Gascoin et al. 10.3389/feart.2024.1381323
- Remote sensing of mountain snow from space: status and recommendations S. Gascoin et al. 10.3389/feart.2024.1381323
- Inferring surface energy fluxes using drone data assimilation in large eddy simulations N. Pirk et al. 10.5194/amt-15-7293-2022
12 citations as recorded by crossref.
- The Multiple Snow Data Assimilation System (MuSA v1.0) E. Alonso-González et al. 10.5194/gmd-15-9127-2022
- Exploring the potential of forest snow modeling at the tree and snowpack layer scale G. Mazzotti et al. 10.5194/tc-18-4607-2024
- 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. 10.1016/j.agwat.2024.108704
- TopoPyScale: A Python Package for Hillslope Climate Downscaling S. Filhol et al. 10.21105/joss.05059
- Numerical simulations of recent and future evolution of Monte Perdido glacier A. Mateos-García et al. 10.18172/cig.5816
- Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas L. Bührle et al. 10.5194/tc-17-3383-2023
- Spatio‐temporal wet snow dynamics from model simulations and remote sensing: A case study from the Rofental, Austria E. Rottler et al. 10.1002/hyp.15279
- Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment E. Alonso-González et al. 10.5194/tc-17-3329-2023
- Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation E. Alonso-González et al. 10.5194/hess-27-4637-2023
- Rain-on-snow responses to warmer Pyrenees: a sensitivity analysis using a physically based snow hydrological model J. Bonsoms et al. 10.5194/nhess-24-245-2024
- A combined data assimilation and deep learning approach for continuous spatio-temporal SWE reconstruction from sparse ground tracks M. Guidicelli et al. 10.1016/j.hydroa.2024.100190
- Remote sensing of mountain snow from space: status and recommendations S. Gascoin et al. 10.3389/feart.2024.1381323
Latest update: 20 Nov 2024
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....
