Virtual Event: ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction


This page lists all poster files presented at this event.

Application of the Long-Short Term Memory neural networks to model bias correction: idealized experiments with the Lorenz-96 model
Arata Amemiya (RIKEN Center for Computational Science)
Toward an adaptive numerical integration scheme dependent on ML-predicted accuracy
Daniel Ayers (University of Reading)
Machine learning based pollution anomaly detection using TROPOMI NO2 columns: a COVID-19 case study.
Jerome Barre (ECMWF)
Comparative study of One-Stage ship detection techniques in SAR images
Filippos Bellos (National and Kapodistrian University of Athens), Theodore Betsas (National Technical University of Athens)
Emulation of a Full Suite of Atmospheric Physics Parameterizations in NCEP GFS using a Neural Network
Alexei Belochitski (IMSG at NOAA/NWS/NCEP/EMC)
High-Resolution Radar Echo Prediction with Machine Learning
Matej Choma (Meteopress)
Improving air pollution forecast with machine learning
Sergey Dyshko (Science and Technology B.V.)
Using machine learning to correct model error and application to data assimilation with a quasi-geostrophic model
Alban Farchi (CEREA, ENPC)
DAN - An optimal Data Assimilation framework based on machine learning recurrent Networks
Anthony Fillion (ANITI)
Simulating precipitation with supervised and generative learning models
Carlos Alberto Gomez Gonzalez (Barcelona Supercomputing Center)
EO-ALERT: Machine Learning-Based On-Board Satellite Processing for Very-Low Latency Convective Storm Nowcasting
Robert Hinz (DEIMOS Space S.L.U.), Juan Ignacio Bravo (DEIMOS Space S.L.U.)
Artificial intelligence reconstructs missing climate information
Christopher Kadow (German Climate Computing Center (DKRZ))
Using Deep Learning to Extract Cyclone Regions of Interest (ROI)
Christina Kumler (CIRES/NOAA/GSL)
Machine learning surrogate models for oceanic simulations' parameter tuning
Redouane Lguensat (LSCE-IPSL, LOCEAN-IPSL)
Exploring Bayesian deep learning for weather forecasting with the Lorenz 84 system
Yang Liu (Netherlands eScience Center)
Bow echo detection in forecasts and radar data with a U-Net
Arnaud MOUNIER (Meteo France)
Accelerating Climate Model Computation by Neural Networks: A Comparative Study
Maha Mdini (RIKEN)
Toward an integrated NWP-DA-AI system for precipitation prediction
Shigenori Otsuka (RIKEN)
Automated NN generation: from the symbolic computation to the design of NN architectures for numerical predictions
Deep Learning for the Verification of Warm Conveyor Belts in NWP and Climate Models
Julian Quinting (Karlsruhe Institute of Technology)
Automatic fronts detection using a convolutional neural network.
Lucie ROTTNER (Météo-France)
NWP Data Integration for Power Grids Risk Assessment Platform
Alla Sapronova
A Neural Network-Based Observation Operator for Coupled Ocean-Acoustic Variational Data Assimilation
Andrea Storto (Centre for Maritime Research and Experimentation)
Deriving global absorbing aerosol optical depth from satellite ultra-violet aerosol index: a deep learning approach
Jiyunting Sun (Royal Netherlands Meteorological Institute, R & D Satellite Observations; Delft University of technology, Geoscience & Remote Sensing)
RRTMGP-NN: Fast and Accurate Predictions of Atmospheric Optical Properties by using Neural Networks
Peter Ukkonen (University of Copenhagen / Danish Meteorological Institute)
Machine learning parameterization of tropical cyclone boundary layer
Leyi Wang (Nanjing University)
Mode-decomposition Diagnosis for the Dynamical Processes of Sudden Stratospheric Warming Events
Zheng Wu (ETH Zurich, Switzerland )
Forecasting short-duration heavy rain based on deep learning
Jie Xiahou
Quantification of Rain to Improve Scatterometer Wind Speed by a Support Vector Machine Method
Xingou Xu (The Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences)
Towards AI-based model-data fusion in precipitation nowcasting
Mingming Zhu (State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences)
Physics-augmented Deep Learning to Improve Tropical Cyclone Intensity and Size Estimation from Satellite Imagery
Jing-Yi Zhuo (Nanjing University)