Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
doi:10.5194/hess-2016-581
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
22 Nov 2016
Review status
A revision of this discussion paper was accepted for the journal Hydrology and Earth System Sciences (HESS) and is expected to appear here in due course.
Role of forcing uncertainty and model error background characterization in snow data assimilation
Sujay V. Kumar1, Jiarui Dong2, Christa D. Peters-Lidard3, David Mocko4,1, and Breogán Gómez5 1Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
2I. M. Systems Group Inc., Environmental Modeling Center, NOAA NCEP, College Park, MD, USA
3Hydrosphere, Biosphere and Geophysics, Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, MD, USA
4Science Applications International Corporation, McLean, VA, USA
5Data assimilation and ensembles, UK Met Office, Exeter, UK
Abstract. Accurate specification of the model error covariances in data assimilation systems is a challenging issue. Ensemble land data assimilation methods rely on stochastic perturbations of input forcing and model prognostic fields for developing representations of input model error covariances. This article examines the limitations of using a single forcing dataset for specifying forcing uncertainty inputs for assimilating snow depth retrievals. Using an idealized data assimilation experiment, the article demonstrates that the use of hybrid forcing input strategies (either through the use of an ensemble of forcing products or through the added use of the forcing climatology) provide a better characterization of the model error background, which leads to improved data assimilation results, especially during the snow accumulation and melt time periods. The use of hybrid forcing ensembles is then employed for assimilating snow depth retrievals from the AMSR2 instrument over two domains in the Continental U.S. with different snow evolution characteristics. Over a region near the Great Lakes where the snow evolution tends to be ephemeral, the use of hybrid forcing ensembles provide significant improvements relative to the use of a single forcing dataset. Over the Colorado Headwaters characterized by large snow accumulation, the impact of using the forcing ensemble is less prominent and is largely limited to the snow transition time periods. The results demonstrate that the availability of a better model error background through the forcing ensemble enables the assimilation system to better incorporate the observational information.

Citation: Kumar, S. V., Dong, J., Peters-Lidard, C. D., Mocko, D., and Gómez, B.: Role of forcing uncertainty and model error background characterization in snow data assimilation, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-581, in review, 2016.
Sujay V. Kumar et al.
Sujay V. Kumar et al.
Sujay V. Kumar et al.

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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.
Data assimilation deals with the blending of model forecasts and observations based on their...
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