Journal metrics

Journal metrics

  • IF value: 4.524 IF 4.524
  • IF 5-year value: 5.558 IF 5-year 5.558
  • CiteScore value: 4.84 CiteScore 4.84
  • SNIP value: 1.425 SNIP 1.425
  • SJR value: 3.034 SJR 3.034
  • IPP value: 4.65 IPP 4.65
  • h5-index value: 52 h5-index 52
  • Scimago H index value: 55 Scimago H index 55
Volume 10, issue 3 | Copyright
The Cryosphere, 10, 1021-1038, 2016
https://doi.org/10.5194/tc-10-1021-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

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 Charrois1,2, Emmanuel Cosme1, Marie Dumont2, Matthieu Lafaysse2, Samuel Morin2, Quentin Libois1, and Ghislain Picard1 Luc Charrois et al.
  • 1Université Grenoble Alpes-CNRS, LGGE, UMR 5183, Grenoble, France
  • 2Météo-France/CNRS, CNRM UMR 3589, CEN, Grenoble, France

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.

Download & links
Publications Copernicus
Download
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.
This study investigates the assimilation of optical reflectances, snowdepth data and both...
Citation
Share