Ensemble-based snow depth data assimilation for a multi-layer snow scheme over the European Arctic

Abstract

Reliable estimates of Earth system conditions are important for weather forecasting, hydrological modelling and their downstream applications. Both real-time prediction systems and historical reanalyses use a combination of observations and physical laws embedded in numerical models to generate gapless and accurate estimates of weather, climate and hydrological conditions. Data assimilation systems merge information from model estimates and observations in an objective way, accounting for their respective uncertainties. In this work we present a regional reanalysis system, focusing on the land surface component. The system uses a multi-layer snow model together with the ensemble-based Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme. The system is run for a 4 year period over the European Arctic, assimilating in situ snow depth observations. Evaluation of the new snow depth analysis showed reduced errors compared to existing products and positive impact of the data assimilation over the domain. Furthermore, a significant difference in total accumulated snow water was seen over the domain, implying a potential impact on downstream hydrological applications. The ensemble correlations between the total snow depth and the multivariate control vector indicated that the ensemble was able to represent snow compaction processes. The LETKF is thus able to account for processes which are often neglected in snow depth data assimilation. The system presented in this study allows for future extensions, including other types of observations and analyses beyond snow variables.