Articles | Volume 10, issue 1
https://doi.org/10.5194/gmd-10-85-2017
https://doi.org/10.5194/gmd-10-85-2017
Development and technical paper
 | 
06 Jan 2017
Development and technical paper |  | 06 Jan 2017

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

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

Related authors

Data-driven emulation of melt ponds on Arctic sea ice
Simon Driscoll, Alberto Carrassi, Julien Brajard, Laurent Bertino, Einar Ólason, Marc Bocquet, and Amos Lawless
EGUsphere, https://doi.org/10.5194/egusphere-2024-2476,https://doi.org/10.5194/egusphere-2024-2476, 2024
Short summary
Exploring the potential of history matching for land surface model calibration
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024,https://doi.org/10.5194/gmd-17-5779-2024, 2024
Short summary
Reconstruction of Arctic sea ice thickness (1992–2010) based on a hybrid machine learning and data assimilation approach
Léo Edel, Jiping Xie, Anton Korosov, Julien Brajard, and Laurent Bertino
EGUsphere, https://doi.org/10.5194/egusphere-2024-1896,https://doi.org/10.5194/egusphere-2024-1896, 2024
Short summary
Monitoring the coastal-offshore water interactions in the Levantine Sea using ocean color and deep supervised learning
Georges Baaklini, Julien Brajard, Leila Issa, Gina Fifani, Laurent Mortier, and Roy El Hourany
EGUsphere, https://doi.org/10.5194/egusphere-2024-1168,https://doi.org/10.5194/egusphere-2024-1168, 2024
Short summary
Improving short-term sea ice concentration forecasts using deep learning
Cyril Palerme, Thomas Lavergne, Jozef Rusin, Arne Melsom, Julien Brajard, Are Frode Kvanum, Atle Macdonald Sørensen, Laurent Bertino, and Malte Müller
The Cryosphere, 18, 2161–2176, https://doi.org/10.5194/tc-18-2161-2024,https://doi.org/10.5194/tc-18-2161-2024, 2024
Short summary

Related subject area

Climate and Earth system modeling
An improved representation of aerosol in the ECMWF IFS-COMPO 49R1 through the integration of EQSAM4Climv12 – a first attempt at simulating aerosol acidity
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024,https://doi.org/10.5194/gmd-17-7539-2024, 2024
Short summary
At-scale Model Output Statistics in mountain environments (AtsMOS v1.0)
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024,https://doi.org/10.5194/gmd-17-7629-2024, 2024
Short summary
Impact of ocean vertical-mixing parameterization on Arctic sea ice and upper-ocean properties using the NEMO-SI3 model
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024,https://doi.org/10.5194/gmd-17-7445-2024, 2024
Short summary
Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024,https://doi.org/10.5194/gmd-17-7365-2024, 2024
Short summary
A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024,https://doi.org/10.5194/gmd-17-7141-2024, 2024
Short summary

Cited articles

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.
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.
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.
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.
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.
Download
Short summary
The objective of this work is to deliver the adjoint model of SECHIBA obtained with software called YAO, in order to perform 4D-VAR data assimilation. The SECHIBA module of the ORCHIDEE land surface model describes the exchanges of water and energy between the surface and the atmosphere. A distributed version is available when only the land surface temperature is used as an observation, with two examples and documentation.