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Adaptive soil moisture profile filtering for horizontal information propagation in the independent column-based CLM2.0

Gabriëlle De Lannoy UGent, Paul Houser, Niko Verhoest UGent and Valentijn Pauwels UGent (2009) JOURNAL OF HYDROMETEOROLOGY. 10(3). p.766-779
abstract
Data assimilation aims to provide an optimal estimate of the overall system state, not only for an observed state variable or location. However, large-scale land surface models are typically column-based and purely random ensemble perturbation of states will lead to block-diagonal a priori (or background) error covariance. This facilitates the filtering calculations but compromises the potential of data assimilation to influence (unobserved) vertical and horizontal neighboring state variables. Here, a combination of an ensemble Kalman filter and an adaptive covariance correction method is explored to optimize the variances and retrieve the off-block-diagonal correlations in the a priori error covariance matrix. In a first time period, all available soil moisture profile observations in a small agricultural field are assimilated into the Community Land Model, version 2.0 (CLM2.0) to find the adaptive second-order a priori error information. After that period, only observations from single individual soil profiles are assimilated with inclusion of this adaptive information. It is shown that assimilation of a single profile can partially rectify the incorrectly simulated soil moisture spatial mean and variability. The largest reduction in the root-mean-square error in the soil moisture field varies between 7% and 22%, depending on the soil depth, when assimilating a single complete profile every two days during three months with a single time-invariant covariance correction.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
MODEL, STATISTICS, ERROR COVARIANCE PARAMETERS, HYDROLOGIC DATA ASSIMILATION, ENSEMBLE KALMAN FILTER, ATMOSPHERIC DATA ASSIMILATION, FORECAST, IMPACT, NOISE, FIELD
journal title
JOURNAL OF HYDROMETEOROLOGY
J. Hydrometeorol.
volume
10
issue
3
pages
766 - 779
Web of Science type
Article
Web of Science id
000267420900011
JCR category
METEOROLOGY & ATMOSPHERIC SCIENCES
JCR impact factor
2.739 (2009)
JCR rank
14/63 (2009)
JCR quartile
1 (2009)
ISSN
1525-755X
DOI
10.1175/2008JHM1037.1
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
673136
handle
http://hdl.handle.net/1854/LU-673136
date created
2009-05-31 18:25:19
date last changed
2012-05-04 08:56:10
@article{673136,
  abstract     = {Data assimilation aims to provide an optimal estimate of the overall system state, not only for an observed state variable or location. However, large-scale land surface models are typically column-based and purely random ensemble perturbation of states will lead to block-diagonal a priori (or background) error covariance. This facilitates the filtering calculations but compromises the potential of data assimilation to influence (unobserved) vertical and horizontal neighboring state variables. Here, a combination of an ensemble Kalman filter and an adaptive covariance correction method is explored to optimize the variances and retrieve the off-block-diagonal correlations in the a priori error covariance matrix. In a first time period, all available soil moisture profile observations in a small agricultural field are assimilated into the Community Land Model, version 2.0 (CLM2.0) to find the adaptive second-order a priori error information. After that period, only observations from single individual soil profiles are assimilated with inclusion of this adaptive information. It is shown that assimilation of a single profile can partially rectify the incorrectly simulated soil moisture spatial mean and variability. The largest reduction in the root-mean-square error in the soil moisture field varies between 7\% and 22\%, depending on the soil depth, when assimilating a single complete profile every two days during three months with a single time-invariant covariance correction.},
  author       = {De Lannoy, Gabri{\"e}lle and Houser, Paul and Verhoest, Niko and Pauwels, Valentijn},
  issn         = {1525-755X},
  journal      = {JOURNAL OF HYDROMETEOROLOGY},
  keyword      = {MODEL,STATISTICS,ERROR COVARIANCE PARAMETERS,HYDROLOGIC DATA ASSIMILATION,ENSEMBLE KALMAN FILTER,ATMOSPHERIC DATA ASSIMILATION,FORECAST,IMPACT,NOISE,FIELD},
  language     = {eng},
  number       = {3},
  pages        = {766--779},
  title        = {Adaptive soil moisture profile filtering for horizontal information propagation in the independent column-based CLM2.0},
  url          = {http://dx.doi.org/10.1175/2008JHM1037.1},
  volume       = {10},
  year         = {2009},
}

Chicago
De Lannoy, Gabriëlle, Paul Houser, Niko Verhoest, and Valentijn Pauwels. 2009. “Adaptive Soil Moisture Profile Filtering for Horizontal Information Propagation in the Independent Column-based CLM2.0.” Journal of Hydrometeorology 10 (3): 766–779.
APA
De Lannoy, G., Houser, P., Verhoest, N., & Pauwels, V. (2009). Adaptive soil moisture profile filtering for horizontal information propagation in the independent column-based CLM2.0. JOURNAL OF HYDROMETEOROLOGY, 10(3), 766–779.
Vancouver
1.
De Lannoy G, Houser P, Verhoest N, Pauwels V. Adaptive soil moisture profile filtering for horizontal information propagation in the independent column-based CLM2.0. JOURNAL OF HYDROMETEOROLOGY. 2009;10(3):766–79.
MLA
De Lannoy, Gabriëlle, Paul Houser, Niko Verhoest, et al. “Adaptive Soil Moisture Profile Filtering for Horizontal Information Propagation in the Independent Column-based CLM2.0.” JOURNAL OF HYDROMETEOROLOGY 10.3 (2009): 766–779. Print.