Adaptive soil moisture profile filtering for horizontal information propagation in the independent column-based CLM2.0
- Author
- Gabriëlle De Lannoy (UGent) , Paul Houser, Niko Verhoest (UGent) and Valentijn Pauwels (UGent)
- Organization
- 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.
- Keywords
- MODEL, STATISTICS, ERROR COVARIANCE PARAMETERS, HYDROLOGIC DATA ASSIMILATION, ENSEMBLE KALMAN FILTER, ATMOSPHERIC DATA ASSIMILATION, FORECAST, IMPACT, NOISE, FIELD
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-673136
- MLA
- De Lannoy, Gabriëlle, et al. “Adaptive Soil Moisture Profile Filtering for Horizontal Information Propagation in the Independent Column-Based CLM2.0.” JOURNAL OF HYDROMETEOROLOGY, vol. 10, no. 3, 2009, pp. 766–79, doi:10.1175/2008JHM1037.1.
- 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. https://doi.org/10.1175/2008JHM1037.1
- Chicago author-date
- 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): 766–79. https://doi.org/10.1175/2008JHM1037.1.
- Chicago author-date (all authors)
- 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. doi:10.1175/2008JHM1037.1.
- 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.
- IEEE
- [1]G. De Lannoy, P. Houser, N. Verhoest, and V. Pauwels, “Adaptive soil moisture profile filtering for horizontal information propagation in the independent column-based CLM2.0,” JOURNAL OF HYDROMETEOROLOGY, vol. 10, no. 3, pp. 766–779, 2009.
@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, Gabriëlle and Houser, Paul and Verhoest, Niko and Pauwels, Valentijn}},
issn = {{1525-755X}},
journal = {{JOURNAL OF HYDROMETEOROLOGY}},
keywords = {{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://doi.org/10.1175/2008JHM1037.1}},
volume = {{10}},
year = {{2009}},
}
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