Articles | Volume 22, issue 11
Hydrol. Earth Syst. Sci., 22, 5711–5734, 2018
https://doi.org/10.5194/hess-22-5711-2018

Special issue: Integration of Earth observations and models for global water...

Hydrol. Earth Syst. Sci., 22, 5711–5734, 2018
https://doi.org/10.5194/hess-22-5711-2018

Research article 05 Nov 2018

Research article | 05 Nov 2018

Assimilation of passive microwave AMSR-2 satellite observations in a snowpack evolution model over northeastern Canada

Fanny Larue et al.

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Cited articles

Andreadis, K. M. and Lettenmaier, D. P.: Implications of representing snowpack stratigraphy for the assimilation of passive microwave satellite observations, J. Hydrometeorology, 13, 1493–1506, https://doi.org/10.1175/JHM-D-11-056.1, 2012. 
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Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE T. Signal Proces., 50, 174–188, 2002. 
Brankart, J.-M., E. Cosme, C.-E. Testut, P. Brasseur, and J. Verron: Efficient Adaptive Error Parameterizations for Square Root or Ensemble Kalman Filters: Application to the Control of Ocean Mesoscale Signals, Mon. Weather Rev., 138, 932–950, 2010. 
Brown, R. and Tapsoba, D.: Improved mapping of snow water equivalent over Quebec, 64th Eastern Snow Conference, St. John's, Newfoundland, Canada, 2007. 
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Short summary
A data assimilation scheme was developed to improve snow water equivalent (SWE) simulations by updating meteorological forcings and snowpack states using passive microwave satellite observations. A chain of models was first calibrated to simulate satellite observations over northeastern Canada. The assimilation was then validated over 12 stations where daily SWE measurements were acquired during 4 winters (2012–2016). The overall SWE bias is reduced by 68 % compared to original SWE simulations.