Articles | Volume 15, issue 24
https://doi.org/10.5194/gmd-15-9127-2022
https://doi.org/10.5194/gmd-15-9127-2022
Model description paper
 | 
21 Dec 2022
Model description paper |  | 21 Dec 2022

The Multiple Snow Data Assimilation System (MuSA v1.0)

Esteban Alonso-González, Kristoffer Aalstad, Mohamed Wassim Baba, Jesús Revuelto, Juan Ignacio López-Moreno, Joel Fiddes, Richard Essery, and Simon Gascoin

Related authors

Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter
Marco Mazzolini, Kristoffer Aalstad, Esteban Alonso-González, Sebastian Westermann, and Désirée Treichler
EGUsphere, https://doi.org/10.5194/egusphere-2024-1404,https://doi.org/10.5194/egusphere-2024-1404, 2024
Short summary
Time series of alpine snow surface radiative temperature maps from high precision thermal infrared imaging
Sara Arioli, Ghislain Picard, Laurent Arnaud, Simon Gascoin, Esteban Alonso-González, Marine Poizat, and Mark Irvine
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-55,https://doi.org/10.5194/essd-2024-55, 2024
Revised manuscript accepted for ESSD
Short summary
Future permafrost degradation under climate change in a headwater catchment of Central Siberia: quantitative assessment with a mechanistic modelling approach
Thibault Xavier, Laurent Orgogozo, Anatoly S. Prokushkin, Esteban Alonso-González, Simon Gascoin, and Oleg S. Pokrovsky
EGUsphere, https://doi.org/10.5194/egusphere-2023-3074,https://doi.org/10.5194/egusphere-2023-3074, 2024
Short summary
Rain-on-snow responses to warmer Pyrenees: a sensitivity analysis using a physically based snow hydrological model
Josep Bonsoms, Juan I. López-Moreno, Esteban Alonso-González, César Deschamps-Berger, and Marc Oliva
Nat. Hazards Earth Syst. Sci., 24, 245–264, https://doi.org/10.5194/nhess-24-245-2024,https://doi.org/10.5194/nhess-24-245-2024, 2024
Short summary
Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation
Esteban Alonso-González, Kristoffer Aalstad, Norbert Pirk, Marco Mazzolini, Désirée Treichler, Paul Leclercq, Sebastian Westermann, Juan Ignacio López-Moreno, and Simon Gascoin
Hydrol. Earth Syst. Sci., 27, 4637–4659, https://doi.org/10.5194/hess-27-4637-2023,https://doi.org/10.5194/hess-27-4637-2023, 2023
Short summary

Related subject area

Cryosphere
Refactoring the EVP solver for improved performance – a case study based on CICE v6.5
Till Andreas Soya Rasmussen, Jacob Poulsen, Mads Hvid Ribergaard, Ruchira Sasanka, Anthony P. Craig, Elizabeth Clare Hunke, and Stefan Rethmeier
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-40,https://doi.org/10.5194/gmd-2024-40, 2024
Revised manuscript accepted for GMD
Short summary
A novel numerical implementation for the surface energy budget of melting snowpacks and glaciers
Kévin Fourteau, Julien Brondex, Fanny Brun, and Marie Dumont
Geosci. Model Dev., 17, 1903–1929, https://doi.org/10.5194/gmd-17-1903-2024,https://doi.org/10.5194/gmd-17-1903-2024, 2024
Short summary
SnowPappus v1.0, a blowing-snow model for large-scale applications of the Crocus snow scheme
Matthieu Baron, Ange Haddjeri, Matthieu Lafaysse, Louis Le Toumelin, Vincent Vionnet, and Mathieu Fructus
Geosci. Model Dev., 17, 1297–1326, https://doi.org/10.5194/gmd-17-1297-2024,https://doi.org/10.5194/gmd-17-1297-2024, 2024
Short summary
OpenFOAM-avalanche 2312: Depth-integrated Models Beyond Dense Flow Avalanches
Matthias Rauter and Julia Kowalski
EGUsphere, https://doi.org/10.5194/egusphere-2024-210,https://doi.org/10.5194/egusphere-2024-210, 2024
Short summary
A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0)
Lizz Ultee, Alexander A. Robel, and Stefano Castruccio
Geosci. Model Dev., 17, 1041–1057, https://doi.org/10.5194/gmd-17-1041-2024,https://doi.org/10.5194/gmd-17-1041-2024, 2024
Short summary

Cited articles

Aalstad, K., Westermann, S., Schuler, T. V., Boike, J., and Bertino, L.: Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites, The Cryosphere, 12, 247–270, https://doi.org/10.5194/tc-12-247-2018, 2018. a, b, c, d, e, f, g, h, i
Aalstad, K., Westermann, S., and Bertino, L.: Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography, Remote Sens. Environ., 239, 111618, https://doi.org/10.1016/j.rse.2019.111618, 2020. a
Alonso González, E.: ealonsogzl/MuSA: v1.0 GMD submission (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7014570, 2022. a
Alonso-González, E.: Inputs (forcing and observations) ready for use by “MuSA: The Multiscale Snow Data Assimilation System (v1.0)”, Zenodo [data set], https://doi.org/10.5281/zenodo.7248635, 2022. a
Alonso-González, E., López-Moreno, J. I., Gascoin, S., García-Valdecasas Ojeda, M., Sanmiguel-Vallelado, A., Navarro-Serrano, F., Revuelto, J., Ceballos, A., Esteban-Parra, M. J., and Essery, R.: Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014, Earth Syst. Sci. Data, 10, 303–315, https://doi.org/10.5194/essd-10-303-2018, 2018. a
Download
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.