Articles | Volume 21, issue 6
https://doi.org/10.5194/hess-21-2637-2017
https://doi.org/10.5194/hess-21-2637-2017
Research article
 | 
02 Jun 2017
Research article |  | 02 Jun 2017

Role of forcing uncertainty and background model error characterization in snow data assimilation

Sujay V. Kumar, Jiarui Dong, Christa D. Peters-Lidard, David Mocko, and Breogán Gómez

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Cited articles

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Bosilovich, M. G., Robertson, F. R., Takacs, L., Molod, A., and Mocko, D.: Atmospheric Water Balance and Variability in the MERRA-2 Reanalysis, J. Climate, 30, 1177–1196, https://doi.org/10.1175/JCLI-D-16-0338.1, 2017.
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Crow, W. and Wood, E.: The assimilation of remotely sensed soil brightness temperature imagery into a land surface model using ensemble Kalman filtering: A case study based on ESTAR measurements during SGP97, Adv. Water Resour., 26, 137–149, 2003.
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Short summary
Data assimilation deals with the blending of model forecasts and observations based on their relative errors. This paper addresses the importance of accurately representing the errors in the model forecasts for skillful data assimilation performance.