Role of forcing uncertainty and background model error characterization in snow data assimilation
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 ar...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-06-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | http://www.hydrol-earth-syst-sci.net/21/2637/2017/hess-21-2637-2017.pdf |
| Summary: | 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 background model error, 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 USA
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 provides 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 of
the article demonstrate that improving the background model error through the
use of a forcing ensemble enables the assimilation system to better
incorporate the observational information. |
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| ISSN: | 1027-5606 1607-7938 |