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

Model code and software

MuSA: The Multiple Snow data Assimilation System Esteban Alonso González

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.