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.
Highlights
Land surface models (LSMs) simulate the interactions between the atmosphere and the land surface, which directly influence the exchange of water, energy and carbon with the atmosphere
We focused on the SECHIBA module (Ducoudré et al, 1993), which is part of the ORCHIDEE land surface model dedicated to the resolution of the surface energy and water budgets
ORCHIDEE is a land surface model developed at the Institut Pierre Simon Laplace (IPSL) in France
Summary
Land surface models (LSMs) simulate the interactions between the atmosphere and the land surface, which directly influence the exchange of water, energy and carbon with the atmosphere. The authors worked with satellite data and directly assimilated soil skin temperatures They concluded that constraining the model with such observations improves model flux estimates, with respect to available measurements. Results suggest that the retrieved fluxes provide modest but statistically significant improvements These authors noted strong biases between LST estimates from in situ observations, land modeling, and satellite retrievals that vary with season and time of the day. They highlighted the importance of taking these biases into account; otherwise, large errors in surface flux estimates can result. SECHIBA-YAO provides an opportunity to control the most influent internal parameters of SECHIBA by assimilating LST (land surface temperature) observations.
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