Preprints
https://doi.org/10.5194/gmd-2022-137
https://doi.org/10.5194/gmd-2022-137
Submitted as: model description paper
26 Aug 2022
Submitted as: model description paper | 26 Aug 2022
Status: this preprint is currently under review for the journal GMD.

MuSA: The Multiscale Snow Data Assimilation System (v1.0)

Esteban Alonso-González1,, Kristoffer Aalstad2,, Mohamed Wassim Baba3, Jesús Revuelto4, Juan Ignacio López-Moreno4, Joel Fiddes5, Richard Essery6, and Simon Gascoin1 Esteban Alonso-González et al.
  • 1Centre d’Etudes Spatiales de la Biosphère, Université de Toulouse,CNRS/CNES/IRD/INRA/UPS, Toulouse, France
  • 2Department of Geosciences, University of Oslo, Oslo, Norway
  • 3Center for Remote Sensing Application (CRSA), Mohammed VI Polytechnic University (UM6P), Ben Guerir, Morocco
  • 4Instituto Pirenaico de Ecología, CSIC, Zaragoza, Spain
  • 5WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
  • 6School of GeoSciences, University of Edinburgh, Edinburgh, UK
  • These authors contributed equally to this work.

Abstract. Accurate knowledge of the seasonal snow distribution is vital in several domains including ecology, water resources management, and tourism. Current spaceborne sensors provide a useful but incomplete description of the snowpack. Many studies suggest that the assimilation of remotely sensed products in physically based snowpack models is a promising path forward to estimate the spatial distribution of snow water equivalent (SWE). However, to date there is no standalone, open source software dedicated to snow data assimilation. Here we introduce a new data assimilation toolbox, the Multiscale Snow Data Assimilation System (MuSA). MuSA was developed to fuse remotely sensed information with the energy and mass balance Flexible Snow Model (FSM2). MuSA was designed to be user-friendly and scalable. It enables assimilation of different state variables such as the snow depth, SWE, snow surface temperature, binary or fractional snow-covered area, and snow albedo and could be easily upgraded to assimilate other variables such as liquid water content or snow density in the future. MuSA allows the joint assimilation of an arbitrary number of these variables, through the generation of an ensemble of FSM2 simulations. The characteristics of the ensemble (i.e. the number of particles and their covariance) may be controlled by the user, and it is generated by perturbing the meteorological forcing of FSM2. The observational variables may be assimilated using different algorithms including the particle filters and smoothers as well as ensemble Kalman filters and smoothers along with their iterative variants. We demonstrate the wide capabilities of MuSA through two snow data assimilation experiments. First, 5 m resolution snow depth maps derived from drone surveys are assimilated in a distributed fashion in the Izas catchment (Central Pyrenees). Furthermore, we conducted a joint assimilation experiment, fusing MODIS land surface temperature and fractional snow-covered area with FSM2 in a single cell experiment. In light of these experiments, we discuss the pros and cons of assimilation algorithms, including their computational cost.

Esteban Alonso-González et al.

Status: open (until 30 Oct 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-137', Anonymous Referee #1, 24 Sep 2022 reply

Esteban Alonso-González et al.

Data sets

Inputs for MuSA Esteban Alonso González https://doi.org/10.5281/zenodo.7015271

Model code and software

MuSA v1.0 Esteban Alonso González https://doi.org/10.5281/zenodo.7014570

Esteban Alonso-González et al.

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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 them with available observations (data assimilation). In this paper we present 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.