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: | S. V. Kumar, J. Dong, C. D. Peters-Lidard, D. Mocko, B. Gómez |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2017-06-01
|
Series: | Hydrology and Earth System Sciences |
Online Access: | http://www.hydrol-earth-syst-sci.net/21/2637/2017/hess-21-2637-2017.pdf |
Similar Items
-
Impact of Surface Albedo Assimilation on Snow Estimation
by: Sujay Kumar, et al.
Published: (2020-02-01) -
Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling
by: R. S. Kim, et al.
Published: (2021-02-01) -
Relevance of climatological background error statistics for mesoscale data assimilation
by: Jelena Bojarova, et al.
Published: (2019-01-01) -
The Land surface Data Toolkit (LDT v7.2) – a data fusion environment for land data assimilation systems
by: K. R. Arsenault, et al.
Published: (2018-09-01) -
Construction of non-diagonal background error covariance matrices for global chemical data assimilation
by: K. Singh, et al.
Published: (2011-04-01)