A5069413430- Meteorological Phenomena and Simulations
- Oceanographic and Atmospheric Processes
- Ocean Waves and Remote Sensing
- Geophysics and Gravity Measurements
- Precipitation Measurement and Analysis
- Seismic Imaging and Inversion Techniques
- Flood Risk Assessment and Management
- Reservoir Engineering and Simulation Methods
- Energy Load and Power Forecasting
- Image and Signal Denoising Methods
- Computational Physics and Python Applications
- Underwater Acoustics Research
- Geographic Information Systems Studies
- Model Reduction and Neural Networks
- Environmental Monitoring and Data Management
- Remote Sensing and LiDAR Applications
- Climate variability and models
- Neural Networks and Applications
- Time Series Analysis and Forecasting
International Centre for Radio Astronomy Research
2024
The University of Western Australia
2024
Sorbonne Université
2021-2023
Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques
2023
Laboratoire de Recherche en Informatique de Paris 6
2021-2023
Centre National de la Recherche Scientifique
2021-2023
Université Paris Cité
2023
Short- or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management flood risk monitoring. Existing data-driven approaches, especially deep learning models, have shown significant skill at this task, using only radar images inputs. In order to determine whether other meteorological parameters wind would improve forecasts, we trained model on fusion of and velocity produced by weather forecast model. The network was compared similar...
Abstract Satellite‐based remote sensing missions have revolutionized our understanding of the Ocean state and dynamics. Among them, space‐borne altimetry provides valuable Sea Surface Height (SSH) measurements, used to estimate surface geostrophic currents. Due sensor technology employed, important gaps occur in SSH observations. Complete maps are produced using linear Optimal Interpolations (OI) such as widely Data Unification Altimeter Combination System ( duacs ). On other hand,...
Accurate wind speed and direction forecasting is paramount across many sectors, spanning agriculture, renewable energy generation, bushfire management. However, conventional models encounter significant challenges in precisely predicting conditions at high spatial resolutions for individual locations or small geographical areas (< 20 km2) capturing medium to long-range temporal trends comprehensive spatio-temporal patterns. This study focuses on a approach high-resolution gridded the height...
&lt;p&gt;Short or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management flood risks monitoring. Existing data-driven approaches, especially deep learning models, have shown significant skill at this task, using only radar images inputs. In order to determine whether other meteorological parameters wind would improve forecasts, we trained model on fusion of and velocity produced by weather forecast model. The network was...
Abstract Many applications in geosciences require solving inverse problems to estimate the state of a physical system. Data assimilation provides strong framework do so when system is partially observed and its underlying dynamics are known some extent. In variational flavor, it can be seen as an optimal control problem where initial conditions parameters. Such often ill-posed, regularization may needed using explicit prior knowledge enforce satisfying solution. this work, we propose use...
Satellite-based remote sensing missions have revolutionized our understanding of the Ocean state and dynamics. Among them, spaceborne altimetry provides valuable measurements Sea Surface Height (SSH), which is used to estimate surface geostrophic currents. However, due sensor technology employed, important gaps occur in SSH observations. Complete maps are produced by community using linear Optimal Interpolations (OI) such as widely-used Data Unification Altimeter Combination System (DUACS)....
Satellite-based remote sensing missions have revolutionized our understanding of the Ocean state and dynamics. Among them, spaceborne altimetry provides valuable measurements Sea Surface Height (SSH), which is used to estimate surface geostrophic currents. However, due sensor technology employed, important gaps occur in SSH observations. Complete maps are produced by community using linear Optimal Interpolations (OI) such as widely-used Data Unification Altimeter Combination System (DUACS)....
Satellite-based remote sensing missions have revolutionized our understanding of the Ocean state and dynamics. Among them, spaceborne altimetry provides valuable measurements Sea Surface Height (SSH), which is used to estimate surface geostrophic currents. However, due sensor technology employed, important gaps occur in SSH observations. Complete maps are produced by community using linear Optimal Interpolations (OI) such as widely-used Data Unification Altimeter Combination System (DUACS)....
Satellite-based remote sensing missions have revolutionized our understanding of the Ocean state and dynamics. Among them, spaceborne altimetry provides valuable measurements Sea Surface Height (SSH), which is used to estimate surface geostrophic currents. However, due sensor technology employed, important gaps occur in SSH observations. Complete maps are produced by community using linear Optimal Interpolations (OI) such as widely-used Data Unification Altimeter Combination System (DUACS)....
&lt;p&gt;The analogy between data assimilation and machine learning has already been shown is still being investigated to address the problem of improving physics-based models. Even though both techniques learn from data, focuses on inferring model parameters while concentrates hidden system state estimation with help a dynamical model.&amp;#160;&lt;br&gt;&amp;#160;&lt;br&gt;Also, neural networks more precisely ResNet-like architectures can be seen as systems...
Short- or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management flood risk monitoring. Existing data-driven approaches, especially deep learning models, have shown significant skill at this task, using only radar images inputs. In order to determine whether other meteorological parameters wind would improve forecasts, we trained model on fusion of and velocity produced by weather forecast model. The network was compared similar...