ESA-ECMWF Workshop 2021 Machine Learning for Earth System Observation and Prediction

Name: ESA-ECMWF Workshop 2021 Machine Learning for Earth System Observation and Prediction
Start: 2021-11-15T10:00:00+01:00
End: 2021-11-18T15:30:00+01:00
Location: Virtual

Overview
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Posters

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Sessions*

Keynote: ML for the ESOP - Setting the scene

Session 1.1: Enhancing Satellite Observation with ML

Session 1.2: Enhancing Satellite Observation with ML

Session 1.3: Enhancing Satellite Observation with ML

Session 2.1: Hybrid Data Assimilation - ML Approaches

Session 2.2: Hybrid Data Assimilation - ML Approaches

Session 3.1: Geophysical Forecasting with ML and Hybrid Models

Session 3.2: Geophysical Forecasting with ML and Hybrid Models

Session 4.1: ML for Post-Processing and Dissemination

Session 4.2: ML for Post-Processing and Dissemination

Session 4.3: ML for Post-Processing and Dissemination

Working Groups - Thematic areas

Session 5: Working Groups plenary discussion and close

Opening

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Contributions*

A spatiotemporal ensemble machine learning framework for predictive mapping: One model to rule them all? (in session "Session 4.1: ML for Post-Processing and Dissemination")

Atmospheric Physics-Guided Machine Learning: Towards Physically-Consistent, Data-Driven, and Interpretable Models of Convection (in session "Session 3.1: Geophysical Forecasting with ML and Hybrid Models")

Atmospheric Retrievals in a Machine Learning Context: A Radiometric Story Over the Ocean (in session "Session 1.1: Enhancing Satellite Observation with ML")

Autonomous Robotic Teams, Machine Learning, and the Next Generation of Earth Observing System (in session "Session 1.2: Enhancing Satellite Observation with ML")

Benefits and opportunities of Explainable Machine Learning in the environmental sciences (in session "Session 1.1: Enhancing Satellite Observation with ML")

CNN-based Detection of Tiny Objects in Remote Areas Using Spatiotemporal Earth Observation Data (in session "Session 1.2: Enhancing Satellite Observation with ML")

Combining data assimilation and machine learning to extract more information from earth observations (in session "Keynote: ML for the ESOP - Setting the scene")

Correcting model error with an online Artificial Neural Network (in session "Session 2.1: Hybrid Data Assimilation - ML Approaches")

Data Assimilation + Machine Learning = Data Learning (in session "Session 2.1: Hybrid Data Assimilation - ML Approaches")

Data-Driven Surrogate Model with Latent Data assimilation for Wildfire Forecasting (in session "Session 2.2: Hybrid Data Assimilation - ML Approaches")

Deep Learning Implementations to Facilitate the Assimilation of Satellite Observations. A Case Study for LST and SST Using IASI Observations (in session "Session 1.3: Enhancing Satellite Observation with ML")

Description of Working Group (in session "Session 4.3: ML for Post-Processing and Dissemination")

Downscaling air pollution levels by fusing Geospatial Vector Data and Sentinel 5P observations over Europe (in session "Session 4.2: ML for Post-Processing and Dissemination")

Estimate of XCO2 from OCO-2 Observations Using a Neural Network Approach (in session "Session 1.2: Enhancing Satellite Observation with ML")

Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning (in session "Session 4.3: ML for Post-Processing and Dissemination")

Free High Resolution Imagery - Open-Source Models and Package for Sentinel 2 Super-Resolution (in session "Session 1.3: Enhancing Satellite Observation with ML")

Gaussian Assimilation of non-Gaussian Image Data via Pre-Processing by Variational Auto-Encoder (VAE) (in session "Session 2.1: Hybrid Data Assimilation - ML Approaches")

How Earth Observations and Machine Learning are Supporting Agricultural Monitoring and Food Security Globally (in session "Session 4.3: ML for Post-Processing and Dissemination")

Integrating Reanalysis and Satellite Cloud Information to Estimate Downward Long-wave Radiation Fluxes Using Multivariate Adaptive Regression Splines: Application to EUMETSAT LSA-SAF (in session "Session 4.1: ML for Post-Processing and Dissemination")

Learning parameters of a numerical model from observations (in session "Session 3.2: Geophysical Forecasting with ML and Hybrid Models")

Machine learning based climate modelling and analysis (in session "Session 3.1: Geophysical Forecasting with ML and Hybrid Models")

Machine Learning for Seamless Thunderstorm Nowcasting from Multiple Data Sources (in session "Session 3.2: Geophysical Forecasting with ML and Hybrid Models")

Machine learning on the Earth System with remote sensing: towards machines that we can understand and interact with (in session "Keynote: ML for the ESOP - Setting the scene")

Machine Learning Techniques for Automated ULF Wave Recognition in Swarm Time Series (in session "Session 1.3: Enhancing Satellite Observation with ML")

Machine Learning-Based Post-Process Correction of the High-Resoulution Multi-Wavelength Sentinel-3 Synergy Aerosol Product (in session "Session 4.1: ML for Post-Processing and Dissemination")

Model error correction with data assimilation and machine learning (in session "Session 2.2: Hybrid Data Assimilation - ML Approaches")

Neural-Network Parametrization of Subgrid Momentum Transport Learned from a High-Resolution Simulation (in session "Session 3.1: Geophysical Forecasting with ML and Hybrid Models")

Permutation invariance and uncertainty in multitemporal image super-resolution (in session "Session 1.1: Enhancing Satellite Observation with ML")

Post-processing of precipitation with Bernstein quantile distribution networks (in session "Session 4.1: ML for Post-Processing and Dissemination")

Poster session

Seamless rainfall forecast using encoder-decoder networks: bridging weather and interannual forecast horizons (in session "Session 3.1: Geophysical Forecasting with ML and Hybrid Models")

Smartriver: Artificial Intelligence for Effective Water Resoures Forecast and Management (in session "Session 3.2: Geophysical Forecasting with ML and Hybrid Models")

The Self-Attentive Ensemble Transformer: Representing Ensemble Interactions in Neural Networks (in session "Session 4.2: ML for Post-Processing and Dissemination")

Towards the Direct Assimilation of Scatterometer Backscatter Triplet (in session "Session 2.2: Hybrid Data Assimilation - ML Approaches")

Use of AI to facilitate the NWP assimilation of EOs: retrieval, radiative transfer and physical integration of satellite products (in session "Session 2.1: Hybrid Data Assimilation - ML Approaches")

Using Convolutional Neural Networks to Detect Emissions Plumes from TROPOMI Data (in session "Session 4.2: ML for Post-Processing and Dissemination")

Welcome and introduction ECMWF (in session "Opening")

Welcome and introduction ESA (in session "Opening")

WG1 - Enhancing Satellite Observations with ML - chaired by Begüm Demir and Bertrand Le Saux (in session "Working Groups - Thematic areas")

WG2 - Hybrid Data Assimilation - ML Approaches - chaired by Rossella Arcucci and Alan Geer (in session "Working Groups - Thematic areas")

WG3 - Geophysical Forecasting with ML and Hybrid Models - chaired by Claudia Vitolo and Peter Dueben (in session "Working Groups - Thematic areas")

WG4 - ML for Post-Processing and Dissemination - chaired by Rochelle Schneider and Massimo Bonavita (in session "Working Groups - Thematic areas")

WG Chairs finalise reports (in session "Working Groups - Thematic areas")

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Events scheduled on*

15/11/2021

16/11/2021

17/11/2021

18/11/2021

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