Variational assimilation of albedo in a snowpack model and reconstruction of the spatial mass balance distribution of an alpine glacier
Abstract
Accurate knowledge of the spatial distribution of the mass balance of temperate glaciers is essential for a better understanding of the physical processes controlling the mass balance and for the monitoring of water resources. In relation to albedo variations, the shortwave radiation budget is a controlling variable of the surface energy balance of glaciers. Remotely sensed albedo observations are here assimilated in a snowpack model to improve the modeling of the spatial distribution of the glacier mass balance. The albedo observations are integrated in the snowpack simulation using a variational data assimilation scheme that modifies the surface grain conditions. The study shows that mesoscale meteorological variables and MODIS-derived albedo maps can be used to obtain a good reconstruction of the annual mass balance on Glacier de Saint-Sorlin, French Alps, on a 100 m x 100 m grid. Five hydrological years within the 2000-10 decade are tested. The accuracy of the method is estimated from comparison with field measurements. Sensitivity to roughness lengths and winter precipitation fields is investigated. Results demonstrate the potential contribution of remote-sensing data and variational data assimilation to further improve the understanding and monitoring of the mass balance of snowpacks and temperate glaciers.