Regional land surface variable reanalysis: LDAS-ARRA

Surface reanalyses are an important tool for examining past natural hazards, such as wildfires, sudden floods and droughts, in great detail and on a consistent scale. With the surge in deep learning, these reanalyses could contribute to the development of new tools for identifying, characterizing and anticipating such hazards. Météo-France has just conducted a 60-year reanalysis of Western Europe using the AROME numerical weather prediction model, known as ARRA. Here, we use these atmospheric conditions to reanalyse the continental surface of the same area using the Land Data Assimilation System (LDAS-Monde). 

ARRA reanalyses are used to drive the Interaction Surface Biosphere Atmosphere (ISBA) land surface model via the SURFEX platform, incorporating prognostic soil moisture, leaf area index and woody biomass at kilometer scale. These modeled variables are then corrected by assimilating Copernicus Land Monitoring Service LAI satellite data and ESA-CCI above-ground biomass satellite data using a simplified extended Kalman filter. The reanalysis period will range between 2000 and 2025 to coincide with CLMS LAI observation availability. Incorporating LAI into ISBA enhances the model's representation of the carbon and water cycles, as well as addressing agricultural practices not currently modeled by ISBA. We will additionally evaluate the impact on river discharge using CTRIP river routing.

The additional focus on biomass in the reanalyses stems from the need to improve the representation of living fuel moisture content and dead biomass in our model, as these are two critical variables for providing an early warning of fire ignition. To this end, above-ground biomass will be assimilated to adjust the distribution of biomass between leaves, wood, and litter.