NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Back to Results
Role of Forcing Uncertainty and Background Model Error Characterization in Snow Data AssimilationAccurate 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 (Advanced Microwave Scanning Radiometer 2) 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.
Document ID
20170005490
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
Authors
Kumar, Sujay V.
(NASA Goddard Space Flight Center Greenbelt, MD United States)
Dong, Jiarul
(I.M. Systems Group, Inc. Rockville, MD, United States)
Peters-Lidard, Christa D.
(NASA Goddard Space Flight Center Greenbelt, MD United States)
Mocko, David
(Science Applications International Corp. McLean, VA, United States)
Gomez, Breogan
(MET Office (Meteorological Office) Exeter, United Kingdom)
Date Acquired
June 12, 2017
Publication Date
June 2, 2017
Publication Information
Publication: Hydrology and Earth System Sciences
Publisher: Copernicus
Volume: 21
Issue: 6
ISSN: 1027-5606
e-ISSN: 1607-7938
Subject Category
Meteorology And Climatology
Earth Resources And Remote Sensing
Report/Patent Number
GSFC-E-DAA-TN43370
Funding Number(s)
CONTRACT_GRANT: NNG15HQ01C
Distribution Limits
Public
Copyright
Other
Keywords
assimilation
covariances
climatology

Available Downloads

There are no available downloads for this record.
No Preview Available