A5068612716- Cryospheric studies and observations
- Soil Moisture and Remote Sensing
- Meteorological Phenomena and Simulations
- Plant Water Relations and Carbon Dynamics
- Hydrology and Watershed Management Studies
- Climate variability and models
- Climate change and permafrost
- Soil erosion and sediment transport
- Remote Sensing in Agriculture
- Geology and Paleoclimatology Research
- Winter Sports Injuries and Performance
- Landslides and related hazards
- Precipitation Measurement and Analysis
- Geophysics and Gravity Measurements
- Soil and Unsaturated Flow
- Arctic and Antarctic ice dynamics
- Tree-ring climate responses
- Flood Risk Assessment and Management
- 3D Modeling in Geospatial Applications
- Hydrology and Drought Analysis
- Atmospheric and Environmental Gas Dynamics
- Remote Sensing and Land Use
- Land Use and Ecosystem Services
- Urban Heat Island Mitigation
- Environmental and Agricultural Sciences
Université de Toulouse
2019-2025
Centre National de la Recherche Scientifique
2018-2025
Météo-France
2018-2025
Centre National de Recherches Météorologiques
2022-2025
University of Reading
2015-2017
Laboratoire Jean Kuntzmann
2012-2014
Université Grenoble Alpes
2014
Institut national de recherche en informatique et en automatique
2012-2014
Abstract The task of quantifying spatial and temporal variations in terrestrial water, energy, vegetation conditions is challenging due to the significant complexity heterogeneity these conditions, all which are impacted by climate change anthropogenic activities. To address this challenge, Earth Observations (EOs) land their utilization within data assimilation (DA) systems vital. Satellite EOs particularly relevant, as they offer quasi‐global coverage, non‐intrusive, provide uniformity,...
Land data assimilation system (LDAS)-Monde, an offline land 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) model (LSM) coupled CNRM (Centre National de Recherches Météorologiques) version of total runoff integrating pathways (CTRIP)...
This study aims to assess the potential of LDAS-Monde platform, a land data assimilation system developed by Météo-France, monitor impact on vegetation state 2018 summer heatwave over Western Europe. The is driven ECMWF’s (i) ERA5 reanalysis, and (ii) Integrated Forecasting System High Resolution operational analysis (IFS-HRES), used in conjunction with Copernicus Global Land Service (CGLS) satellite-derived products, namely Surface Soil Moisture (SSM) Leaf Area Index (LAI). long time series...
Abstract. This paper introduces an ensemble square root filter (EnSRF) in the context of jointly assimilating observations surface soil moisture (SSM) and leaf area index (LAI) Land Data Assimilation System LDAS-Monde. By ingesting those satellite-derived products, LDAS-Monde constrains Interaction between Soil, Biosphere Atmosphere (ISBA) land model (LSM), coupled with CNRM (Centre National de Recherches Météorologiques) version Total Runoff Integrating Pathways (CTRIP) to improve...
Abstract. Green roofs are promoted to provide ecosystem services and mitigate climate change in urban areas. This is largely due their supposed benefits for biodiversity, rainwater management, evaporative cooling, carbon sequestration. One scientific challenge quantifying the various contributions of green using reliable methods. In this context, roof module already running Town Energy Balance canopy model water energy exchanges was improved by implementing CO2 fluxes sequestration...
To estimate the effect of vegetation stress and changes in biogenic volatile organic compound (BVOC) emissions on urban ozone (O3) levels we perform a systematic, observation-based analysis relationship between formaldehyde (HCHO) mixing ratios, meteorological parameters, measurement-based drought indicators O3 over central European city Vienna, Austria. In addition, numerical models SURface EXternalisée (SURFEX), Model Emissions Gases Aerosols from Nature (MEGAN) Vers.2.1 3 MOdèle de Chimie...
This study focuses on the ability of global Land Data Assimilation System, LDAS-Monde, to improve representation land surface variables (LSVs) over Burkina-Faso through joint assimilation satellite derived soil moisture (SSM) and leaf area index (LAI) from January 2001 June 2018. The LDAS-Monde offline system is forced by latest European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis ERA5 as well ERA-Interim former reanalysis, leading reanalyses LSVs at 0.25° ×...
Abstract. 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 Atmosphere) model (LSM). This study demonstrates able to detect, monitor forecast impact extreme weather on states. Firstly, run globally at 0.25∘ spatial resolution over 2010–2018. It forced by state-of-the-art ERA5 reanalysis (LDAS_ERA5) from European...
Abstract. Clay shrinkage, which consists of a reduction in the volume clay soils during dry periods, can affect buildings and cause subsidence damage. In France, losses due to are estimated at more than EUR 16 billion for period 1989–2021 (CCR, 2021) expected increase under effect climate warming. This work aims improve current understanding conditions triggering by proposing an innovative drought index. We use daily soil wetness index (SWI) develop new annual that be related The SWI is...
Abstract. Root zone soil moisture (RZSM) is critical for water resource management, drought monitoring and sub-seasonal flood climate prediction. While RZSM not directly observable from space, several products are available widely used at global continental scales. This study conducts a comprehensive quantitative evaluation of eight using observations 58 in situ stations over the Huai River basin (HRB) China. Attention drawn to potential factors that contribute uncertainties model-based...
This paper investigates to what extent soil moisture and vegetation density information can be extracted from the Advanced Scatterometer (ASCAT) satellite-derived radar backscatter (σ°) in a data assimilation context. The impact of independent estimates surface (SSM) leaf area index (LAI) diverse types on ASCAT σ° observations is simulated over southwestern France using water cloud model (WCM). LAI SSM variables used by WCM are derived satellite Interactions between Soil, Biosphere,...
A Deep Neural Network (DNN) is used to estimate the Advanced Scatterometer (ASCAT) C-band microwave normalized backscatter (σ40o), slope (σ′) and curvature (σ″) over France. The Interactions between Soil, Biosphere Atmosphere (ISBA) land surface model (LSM) produce variables (LSVs) that are input DNN. DNN trained simulate σ40o, σ′ σ″ from 2007 2016. predictive skill of evaluated during an independent validation period 2017 2019. Normalized sensitivity coefficients (NSCs) computed study ASCAT...
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...
Abstract. Estimating the contribution of Antarctica and Greenland to sea-level rise is a hot topic in glaciology. Good estimates rely on our ability run precisely calibrated ice sheet evolution model starting from reliable initial state. Data assimilation aims provide an answer this problem by combining equations with observations. In paper we aim study state-of-the-art ensemble Kalman filter (ETKF) address problem. This method implemented validated twin experiments framework for shallow...
LDAS-Monde is a global land data assimilation system (LDAS) developed by Centre National de Recherches Météorologiques (CNRM) to monitor surface variables (LSV) at various scales, from regional global. With LDAS-Monde, it possible jointly assimilate satellite-derived observations of soil moisture (SSM) and leaf area index (LAI) into the interactions between biosphere atmosphere (ISBA) model (LSM) in order analyze profile together with vegetation biomass. In this study, we investigate...
Abstract. Predicting the evolution of ice sheets requires numerical models able to accurately track migration sheet continental margins or grounding lines. We introduce a physically based moving-point approach for flow on conservation local masses. This allows be tracked explicitly. Our is also well suited capture waiting-time behaviour efficiently. A finite-difference scheme derived and applied in simplified context (continental radially symmetrical shallow approximation). The scheme, which...
Observed by satellites for more than a decade, surface soil moisture (SSM) is an essential component of the Earth system. Today, with Sentinel missions, SSM can be derived at sub-kilometer spatial resolution. In this work, aggregated 1 km × observations combining Sentinel-1 (S1) and Sentinel-2 (S2) data are assimilated first time into Interactions between Soil, Biosphere, Atmosphere (ISBA) land model using global Land Data Assimilation System (LDAS-Monde) tool Meteo-France. The ISBA...
ASCAT normalized backscatter (σ40o) and slope (σ′) contain valuable information about soil moisture vegetation. While σ40o has been assimilated to constrain moisture, sometimes together with Leaf Area Index (LAI), this study is the first assimilate σ′ directly vegetation states. Here, we into ISBA-A-gs LSM using Simplified Extended Kalman Filter (SEKF) a Deep Neural Network (DNN) as observation operator. The performances of data assimilation (DA) open loop (OL) are evaluated against in-situ...