Articles | Volume 24, issue 9
https://doi.org/10.5194/hess-24-4291-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-4291-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Sep 2020
Research article |
| 02 Sep 2020
| 02 Sep 2020
Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces
Clément Albergel
CORRESPONDING AUTHOR
CNRM, Université de Toulouse, Météo-France, CNRS,
Toulouse, France
now at: European Space Agency Climate Office, ECSAT, Harwell Campus, Oxfordshire, Didcot OX11 0FD, UK
Yongjun Zheng
CNRM, Université de Toulouse, Météo-France, CNRS,
Toulouse, France
Bertrand Bonan
CNRM, Université de Toulouse, Météo-France, CNRS,
Toulouse, France
Emanuel Dutra
Instituto Dom Luiz, IDL, Faculty of Sciences, University of Lisbon, Lisbon, Portugal
Nemesio Rodríguez-Fernández
CESBIO, Université de Toulouse, CNRS, CNES, IRD, Toulouse,
France
Simon Munier
CNRM, Université de Toulouse, Météo-France, CNRS,
Toulouse, France
Clara Draper
CIRES/NOAA Earth System Research Laboratories, Boulder, CO, USA
Patricia de Rosnay
European Centre for Medium-Range Weather Forecasts, Shinfield Road,
Reading RG2 9AX, UK
Joaquin Muñoz-Sabater
European Centre for Medium-Range Weather Forecasts, Shinfield Road,
Reading RG2 9AX, UK
Gianpaolo Balsamo
European Centre for Medium-Range Weather Forecasts, Shinfield Road,
Reading RG2 9AX, UK
David Fairbairn
European Centre for Medium-Range Weather Forecasts, Shinfield Road,
Reading RG2 9AX, UK
Catherine Meurey
CNRM, Université de Toulouse, Météo-France, CNRS,
Toulouse, France
Jean-Christophe Calvet
CNRM, Université de Toulouse, Météo-France, CNRS,
Toulouse, France
Data sets
LDAS-Monde technical documentation and contact points CNRM https://opensource.umr-cnrm.fr/projects/openldasmonde/files
Short summary
LDAS-Monde is a global offline land data assimilation system (LDAS) that jointly assimilates satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the ISBA (Interaction between Soil Biosphere and Atmosphere) land surface model (LSM). This study demonstrates that LDAS-Monde is able to detect, monitor and forecast the impact of extreme weather on land surface states.
LDAS-Monde is a global offline land data assimilation system (LDAS) that jointly assimilates...
