ECMWF–ESA Workshop on Machine Learning for Earth Observation and Prediction


This page lists all poster files provided to ECMWF for this event.

Onboard cloud detection and atmospheric correction with end-to-end deep learning emulators
Giacomo Acciarini (Trillium Technologies)
ML emulation of a local-scale UK climate model
Henry Addison (University of Bristol)
Preliminary steps on AI-based active deformation processes classification and time series forecasting
Héctor Aguilera Alonso (Geological Survey of Spain (IGME-CSIC))
Estimation of dynamical instability (local Lyapunov exponents) in chaotic systems
Daniel Ayers (University of Reading)
Post-processing Quantitative Precipitation Forecasts Using Machine Learning in Southern Brazil
Cesar Beneti (SIMEPAR - Parana Meteorological Service)
Giant Antarctic icebergs – segmentation using a deep neural network
Anne Braakmann-Folgmann (University of Leeds, CPOM)
Machine learning-based crop yield forecasting in the Pannonian Basin and its skill in years of severe drought
Emanuel Bueechi
Machine learning estimation of storm updrafts
Randy Chase (School of Computer Science, University of Oklahoma)
Understanding cloud systems over land and ocean using energy - based deep learning neural networks
Dwaipayan Chatterjee (Institute for Geophysics and Meteorology, University of Cologne)
Hybrid data assimilation for model error estimation and correction at ECMWF
Marcin Chrust (ECMWF)
Precipitation correction and downscaling applied to the ERA5 dataset
Fenwick Cooper
Recursive climate feature selection for regional weather prediction
Kevin Donkers (Met Office), Nathan Creaser (Met Office)
Sensitivity Analysis and Machine Learning of a Sea Ice Melt Pond Parametrisation
Simon Driscoll (University of Reading)
FluViSat: Measuring Streamflow from Space
Nick Everard (UK Centre for Ecology and Hydrology)
Error covariance free variational assimilation with deep prior
Arthur Filoche (Sorbonne Université - LIP6)
Deep learning of subgrid-scale parametrisations for sea-ice dynamics
Tobias Finn (CEREA, École des Ponts and EDF R&D (France))
A 1-D QBO model testbed for data-driven gravity wave parameterization: Generalization and calibration
Edwin Gerber (New York University)
Flood risk forecasting using a combination of hydrological modelling and machine learning methods
Boris Gratadoux (Thales), Laure Chaumat (Thales)
Mapping Malaria Prevalence in sub-Saharan Africa with Deep Learning and Satellite Imagery
Iwona Hawryluk (Imperial College London)
Bertrand Le Saux (ESA/ESRIN)
AtmoRep: Large Scale Representation Learning for Atmospheric Data
Christian Lessig (Otto-von-Guericke-Universität Magdeburg)
Probabilistic machine learning for predicting convection initiation
Greta Miller (University of Oxford)
Development of multi-parametric Geophysical Modulation Function for scatterometry wind vector retrievals
Alexey Mironov (eOdyn)
Coupling regional air quality simulations of EURAD-IM with street canyon observations - a machine learning approach
Charlotte Neubacher (Forschungszentrum Jülich, Institute of Energy and Climate Research - Troposphere(IEK - 8))
Machine Learning-Based Approaches to Predict the Autoconversion Rates from Satellite Data
Maria Carolina Novitasari (University College London)
Intercomparison of deep learning architectures for the prediction of precipitation fields
Noelia Otero (Institute of Geography and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland)
Earth System Deep Learning for Global Wildfire Forecasting
Ioannis Papoutsis (National Observatory of Athens)
Coarse-graining Wastes Data: A Transfer Learning Approach for Leveraging All High-Resolution Data in Machine Learning Parameterizations.
Raghul Parthipan (University of Cambridge)
An assessment of Machine Learning Methods for emulating 2D quasi-geostrophic dynamics
Stephen Penny (Sofar Ocean Technologies, CIRES)
Synthetic data generation using machine learning for improved prediction of dynamic viscosity
Cesar Quilodran-Casas
Machine Learning for Research and Applications using NASA’s Black Marble Nighttime Lights Product Suite
Miguel Román (Leidos Headquarters)
Wide-Area land cover mapping with sentinel-1 imagery using deep learning semantic segmentation models
Sanja Scepanovic (Nokia Bell Labs)
Physics-Enriched Co-registration for Satellite On-Board Processing
Andrea Spichtinger (OroraTech GmbH)
NO2 Forecast with Lightweight Machine Learning Models Based on POD Dimensionality Reduction
Marco Stricker (DFKI)
Using machine learning to classify severe summer convective storms based on a multi-data approach: A 0-4h warning system prototype at DWD
Cornelia Strube (Deutscher Wetterdienst)