LDAS-Monde Sequential Assimilation of Satellite Derived Observations Applied to the CONtiguous US: An ERA-5 Driven Reanalysis of the Land Surface Variables

• DOI: 10.20944/preprints201809.0105.v1
• Publication Date: 2018-09-10T06:06:22Z
• Authors: Clément Albergel; Simon Munier; Aymeric Bocher; Bertrand Bonan; Yongjun Zheng; Clara Draper; Delphine J. Leroux; Jean-Christophe C...
• Keywords: Water cycle; Land Cover; Biosphere model

LDAS-Monde, an offline land data assimilation system with global capacity, is applied over the CONtiguous US (CONUS) domain to enhance monitoring accuracy for water and energy states fluxes. LDAS-Monde ingests satellite-derived Surface Soil Moisture (SSM) Leaf Area Index (LAI) estimates constrain Interactions between Soil, Biosphere, Atmosphere (ISBA) Land Model (LSM) coupled CNRM (Centre National de Recherches Météorologiques) version of Total Runoff Integrating Pathways (CTRIP) continental hydrological (ISBA-CTRIP). forced by ERA-5 atmospheric reanalysis from European Center For Medium Range Weather Forecast (ECMWF) 2010 2016 leading a 7-yr, quarter degree spatial resolution Variables (LSVs) CONUS. The impact assimilating LAI SSM into assessed North America, comparison satellite-driven model evapotranspiration Global Evaporation Amsterdam (GLEAM) project, upscaled ground-based observations gross primary productivity FLUXCOM project. Also, taking advantage relatively dense networks CONUS, we also evaluate against in-situ measurements soil moisture USCRN network (US Climate Reference Network) are used in evaluation, together river discharges United States Geophysical Survey (USGS) Data Centre (GRDC). Those sets highlight added value satellite derived compared open-loop simulation (i.e. no assimilation). It shown that has ability not only monitor surface variables but forecast them, providing improved initial conditions which impacts persist through time. potential be extreme events like agricultural drought, also. Finally, limitations related current exposed as well several insights on how use alternative datasets analyze vegetation state.