Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation

Alonso-González, Esteban; Aalstad, Kristoffer; Pirk, Norbert; Mazzolini, Marco; Treichler, Désirée; Leclercq, Paul; Westermann, Sebastian; López-Moreno, Juan Ignacio; Gascoin, Simon

doi:https://doi.org/10.5194/hess-27-4637-2023

Articles | Volume 27, issue 24

https://doi.org/10.5194/hess-27-4637-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-27-4637-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 27, issue 24

Research article

|

22 Dec 2023

Research article |

| 22 Dec 2023

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

Data sets

Inputs (forcing and observations) ready for use by 'MuSA: The Multiscale Snow Data Assimilation System Esteban Alonso González https://doi.org/10.5281/zenodo.7248635

Model code and software

MuSA: The Multiple Snow data Assimilation System Esteban Alonso González https://doi.org/10.5281/zenodo.7906965

Short summary

Here we explore how to improve hyper-resolution (5 m) distributed snowpack simulations using sparse observations, which do not provide information from all the areas of the simulation domain. We propose a new way of propagating information throughout the simulations adapted to the hyper-resolution, which could also be used to improve simulations of other nature. The method has been implemented in an open-source data assimilation tool that is readily accessible to everyone.