Variational assimilation of land surface temperature within the ORCHIDEE Land Surface Model Version 1.2.6-Reference-Cited by-同舟云学术

Variational assimilation of land surface temperature within the ORCHIDEE Land Surface Model Version 1.2.6

Published:2017-01-06 Issue:1 Volume:10 Page:85-104
ISSN:1991-9603
Container-title:Geoscientific Model Development
language:en
Short-container-title:Geosci. Model Dev.

Author:

Benavides Pinjosovsky Hector Simon^ORCID,Thiria Sylvie,Ottlé Catherine^ORCID,Brajard Julien^ORCID,Badran Fouad,Maugis Pascal

Abstract

Abstract. The SECHIBA module of the ORCHIDEE land surface model describes the exchanges of water and energy between the surface and the atmosphere. In the present paper, the adjoint semi-generator software called YAO was used as a framework to implement a 4D-VAR assimilation scheme of observations in SECHIBA. The objective was to deliver the adjoint model of SECHIBA (SECHIBA-YAO) obtained with YAO to provide an opportunity for scientists and end users to perform their own assimilation. SECHIBA-YAO allows the control of the 11 most influential internal parameters of the soil water content, by observing the land surface temperature or remote sensing data such as the brightness temperature. The paper presents the fundamental principles of the 4D-VAR assimilation, the semi-generator software YAO and a large number of experiments showing the accuracy of the adjoint code in different conditions (sites, PFTs, seasons). In addition, a distributed version is available in the case for which only the land surface temperature is observed.

Publisher

Copernicus GmbH

Link

https://gmd.copernicus.org/articles/10/85/2017/gmd-10-85-2017.pdf

Reference34 articles.

1. Aubinet, M., Vesala, T., and Papale, D.: Eddy Covariance: A Practical Guide to Measurement and Data Analysis, Springer Atmospheric Sciences Editions, United States of America, 2012.

2. Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel, W., Paw, K. T., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities, B. Am. Meteorol. Soc., 82, 2415–2434, https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2, 2001.

3. Bateni, S. M., Entekhabi, D., and Jeng, D. S.: Variational assimilation of land surface temperature and the estimation of surface energy balance components, J. Hydrol., 481, 143–156, https://doi.org/10.1016/j.jhydrol.2012.12.039, 2013.

4. Benavides Pinjosovsky, H. S.: Variarional data assimilation in the land surface model ORCHIDEE using YAO, Earth Sciences, Université Pierre et Marie Curie – Paris VI, available at: http://www.theses.fr/2014PA066590, last access: 14 September 2014.

5. Bischof, C. H., Bouaricha, A., Khademi, P. M., and Mor, J. J.: Computing gradients in large-scale optimization using automatic differentiation, Informs J. Comput., 9, 185–194, 1997.

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