Articles | Volume 10, issue 3
Research article
13 May 2016
Research article |  | 13 May 2016

On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model

Luc Charrois, Emmanuel Cosme, Marie Dumont, Matthieu Lafaysse, Samuel Morin, Quentin Libois, and Ghislain Picard

Abstract. This paper examines the ability of optical reflectance data assimilation to improve snow depth and snow water equivalent simulations from a chain of models with the SAFRAN meteorological model driving the detailed multilayer snowpack model Crocus now including a two-stream radiative transfer model for snow, TARTES. The direct use of reflectance data, allowed by TARTES, instead of higher level snow products, mitigates uncertainties due to commonly used retrieval algorithms.

Data assimilation is performed with an ensemble-based method, the Sequential Importance Resampling Particle filter, to represent simulation uncertainties. In snowpack modeling, uncertainties of simulations are primarily assigned to meteorological forcings. Here, a method of stochastic perturbation based on an autoregressive model is implemented to explicitly simulate the consequences of these uncertainties on the snowpack estimates.

Through twin experiments, the assimilation of synthetic spectral reflectances matching the MODerate resolution Imaging Spectroradiometer (MODIS) spectral bands is examined over five seasons at the Col du Lautaret, located in the French Alps. Overall, the assimilation of MODIS-like data reduces by 45 % the root mean square errors (RMSE) on snow depth and snow water equivalent. At this study site, the lack of MODIS data on cloudy days does not affect the assimilation performance significantly. The combined assimilation of MODIS-like reflectances and a few snow depth measurements throughout the 2010/2011 season further reduces RMSEs by roughly 70 %. This work suggests that the assimilation of optical reflectances has the potential to become an essential component of spatialized snowpack simulation and forecast systems. The assimilation of real MODIS data will be investigated in future works.

Short summary
This study investigates the assimilation of optical reflectances, snowdepth data and both combined into a multilayer snowpack model. Data assimilation is performed with an ensemble-based method, the Sequential Importance Resampling Particle filter. Experiments assimilating only synthetic data are conducted at one point in the French Alps, the Col du Lautaret, over five hydrological years. Results of the assimilation experiments show improvements of the snowpack bulk variables estimates.