93 citations · 102 across the 19 of their papers we have counts for
20 papers
Drift Field Net: Learning Ocean Lagrangian advection fields from in-situ and satellite observations
Théo Archambault, Pierre Garcia, Mattia Romero +2
The North Pacific Subtropical Gyre (NPSG) is a major accumulation zone for floating plastic debris, resulting from basin-scale convergent ocean circulation. Effective cleanup strat…
BG4Sea: Biogeochemical Seasonal Forecastability via Progressive Information Scaling
Gabriela Martinez Balbontin, Anastase Charantonis, Dominique Bereziat +1
Marine biogeochemical forecasting is increasingly important for managing marine ecosystems and the carbon cycle, yet global, seasonal forecast products lag far behind physical ocea…
Emulating the Forced Response of Climate Models with Generative Machine Learning
Graham Clyne, Julia Kaltenborn, Peer Nowack +2
Global climate models are essential tools to simulate past and potential future pathways of climate change, as well as associated climate impacts. Shared Socioeconomic Pathways (SS…
Generative Unsupervised Downscaling of Climate Models via Domain Alignment: Application to Wind Fields
Julie Keisler, Boutheina Oueslati, Anastase Charantonis +2
General Circulation Models (GCMs) are widely used for future climate projections, but their coarse spatial resolution and systematic biases limit their direct use for impact studie…
Super-Resolving Coarse-Resolution Weather Forecasts With Flow Matching
Aymeric Delefosse, Anastase Charantonis, Dominique Béréziat
Machine learning-based weather forecasting models now surpass state-of-the-art numerical weather prediction systems, but training and operating these models at high spatial resolut…
STIPP: Space-time in situ postprocessing over the French Alps using proper scoring rules
David Landry, Isabelle Gouttevin, Hugo Merizen +2
We propose Space-time in situ postprocessing (STIPP), a machine learning model that generates spatio-temporally consistent weather forecasts for a network of station locations. Gri…
SerpentFlow: Generative Unpaired Domain Alignment via Shared-Structure Decomposition
Julie Keisler, Anastase Alexandre Charantonis, Yannig Goude +2
Domain alignment refers broadly to learning correspondences between data distributions from distinct domains. In this work, we focus on a setting where domains share underlying str…
Quantile Regression, Variational Autoencoders, and Diffusion Models for Uncertainty Quantification: A Spatial Analysis of Sub-seasonal Wind Speed Prediction
Ganglin Tian, Anastase Alexandre Charantonis, Camille Le Coz +2
This study aims to improve the spatial representation of uncertainties when regressing surface wind speeds from large-scale atmospheric predictors for sub-seasonal forecasting. Sub…
ArchesClimate: Probabilistic Decadal Ensemble Generation With Flow Matching
Graham Clyne, Guillaume Couairon, Guillaume Gastineau +2
Internal variability is a dominant contributor to the uncertainty of predictions at the interannual to decadal timescale. A typical approach to separating the internal variability…
Generating ensembles of spatially-coherent in-situ forecasts using flow matching
David Landry, Claire Monteleoni, Anastase Charantonis
We propose a machine-learning-based methodology for in-situ weather forecast postprocessing that is both spatially coherent and multivariate. Compared to previous work, our Flow MA…
ORCAst: Operational High-Resolution Current Forecasts
Pierre Garcia, Inès Larroche, Amélie Pesnec +6
We present ORCAst, a multi-stage, multi-arm network for Operational high-Resolution Current forecAsts over one week. Producing real-time nowcasts and forecasts of ocean surface cur…
ArchesWeather & ArchesWeatherGen: a deterministic and generative model for efficient ML weather forecasting
Guillaume Couairon, Renu Singh, Anastase Charantonis +2
Weather forecasting plays a vital role in today's society, from agriculture and logistics to predicting the output of renewable energies, and preparing for extreme weather events.…
Improving sub-seasonal wind-speed forecasts in Europe with a non-linear model
Ganglin Tian, Camille Le Coz, Anastase Alexandre Charantonis +3
Sub-seasonal wind speed forecasts provide valuable guidance for wind power system planning and operations, yet the forecast skills of surface winds decrease sharply after two weeks…
Leveraging deterministic weather forecasts for in-situ probabilistic temperature predictions via deep learning
David Landry, Anastase Charantonis, Claire Monteleoni
We propose a neural network approach to produce probabilistic weather forecasts from a deterministic numerical weather prediction. Our approach is applied to operational surface te…
ArchesWeather: An efficient AI weather forecasting model at 1.5° resolution
Guillaume Couairon, Christian Lessig, Anastase Charantonis +1
One of the guiding principles for designing AI-based weather forecasting systems is to embed physical constraints as inductive priors in the neural network architecture. A popular…
Learning of Sea Surface Height Interpolation from Multi-variate Simulated Satellite Observations
Theo Archambault, Arthur Filoche, Anastase Charantonis +2
Satellite-based remote sensing missions have revolutionized our understanding of the Ocean state and dynamics. Among them, space-borne altimetry provides valuable Sea Surface Heigh…
Learning 4DVAR inversion directly from observations
Arthur Filoche, Julien Brajard, Anastase Charantonis +1
Variational data assimilation and deep learning share many algorithmic aspects in common. While the former focuses on system state estimation, the latter provides great inductive b…
Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting
Vincent Bouget, Dominique Béréziat, Julien Brajard +2
Short- or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management or flood risk monitoring. Existing data-driven appro…
Representing ill-known parts of a numerical model using a machine learning approach
Julien Brajard, Anastase Charantonis, Jérôme Sirven
In numerical modeling of the Earth System, many processes remain unknown or ill represented (let us quote sub-grid processes, the dependence to unknown latent variables or the non-…
Reconstruction of Sub-Surface Velocities from Satellite Observations Using Iterative Self-Organizing Maps
Christopher Chapman, Anastase Alexandre Charantonis
In this letter a new method based on modified self-organizing maps is presented for the reconstruction of deep ocean current velocities from surface information provided by satelli…