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ML for Earth System Modelling and Analytics workshop 2021

DKRZ/HZG/GERICS co-organize a workshop, supported by Helmholtz AI, to facilitate knowledge exchange and discussion about the state of the art and future directions of applying ML methods in Earth System modelling, analytics, and impact research.
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03.05.2021 um 14:30 bis 04.05.2021 um 18:00 (Europe/Berlin / UTC200)
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To overcome limitations in computing and data analytics related to Earth System science, the uptake of artificial intelligence (AI) and machine learning (ML) methodologies is currently being explored. Multiple initiatives are now emerging to tackle open challenges such as subscale parametrization, detection of patterns and in-situ analysis, adoption of ML for alternative process models, or dedicated fast prediction systems to address specific end-user needs.

Following the first workshop held in February 2020, this second workshop will provide an update on the state of the art in applying and extending AI/ML techniques in topics relevant to Earth System science, from integrating ML with models to deriving new insights from observational data, to extending AI/ML to impact models and approaches. We call for contributions to the sessions, offering presentation and lightning talk slots for those interested in submitting an abstract (abstract submission is now closed). Contributions are welcome from all participants, from updates on ongoing research activities and future plans to technical insights worthwhile to share.

The workshop will take place on the afternoons of May 03/04, 2021, from about 14:30-18:00 CEST. The workshop will be an exclusively online event. The workshop is co-organized by DKRZ, HZG and GERICS with support from Helmholtz AI.

Workshop co-organizers:
Laurens Bouwer (GERICS), Christopher Kadow (DKRZ), Tobias Weigel (DKRZ/Helmholtz AI), Eduardo Zorita (HZG)

Registration is closed now.

 

Workshop schedule

Times are in CEST.

 

Day 1 - May 03, 2021

14:30

Welcome, logistics

14:45 - 15:15

Keynote 1

Marc van den Homberg (510/Netherlands Red Cross): Leveraging Artificial Intelligence and Big data across the disaster risk management cycle; opportunities and challenges for the Red Cross

15:15 - 15:20

Short break

15:20 - 16:45

Parallel Session A:

Earth System Modelling

Chaired by Christopher Kadow

David Greenberg (Helmholtz Center Hereon): An ML Perspective on Closed Loop Tuning in Earth Science Simulators

Kai Fan (CASUS/HZDR): A machine learning-based air quality forecast system for Pacific Northwest

Bing Gong (Juelich Supercomputing Center): Near-surface temperature forecasting by deep learning

Nicola Maher (U. Colorado, Boulder/MPI for Meteorology): Using machine learning techniques to classify ENSO events

David Hall (NVIDIA): The Frontiers of Deep Learning for the Earth System Sciences

Parallel Session B:

Extreme Events and Impacts

Chaired by Laurens Bouwer

Stefano Bianco (GFZ Potsdam): Machine learning model of the plasmasphere to forecast satellite charging caused by solar storms

Jan Walda (University of Hamburg): Denoising seismic data using a ResNeXt-50-based convolutional autoencoder

Shagun Garg (GFZ Potsdam): Artificial Intelligence for flood analysis: first results from the AI4Flood project

Jacopo Margutti (Netherlands Red Cross): Automated Damage Assessment

Lennart Marien (GERICS): Supervised Machine Learning to investigate Heat Waves and Myocardial Infarctions in Augsburg, Germany

16:45 - 17:00

Break

17:00

Christopher Irrgang

Neural Earth System Modelling

17:15 - 18:00

Discussion

Co-chaired by Christopher Kadow & Laurens Bouwer

Day 2 - May 04, 2021

14:30 - 15:10

Lightning talks

Chaired by Tobias Weigel

15:10 - 15:40

Lightning talks interaction session

 

15:40 - 15:45

Short break

15:45 - 17:15

Parallel Session C:

Earth System Modelling

Co-chaired by Eduardo Zorita & Christopher Kadow

Thorsten Kurth (NVIDIA): 3D bias correction with deep learning in the Integrated Forecasting System

Shruthi Nath (Climate Analytics/ETH Zürich): Building an Earth System Model emulator for local monthly temperature

Gabriel Stachura (Institute of Meteorology and Water Management (Poland): Machine learning based post-processing of 2-m air temperature model output – a multi-model approach

Silke Donayre Holtz (KIT): Lossy Compression of Climate Data using Convolutional Autoencoders

Julius Polz (KIT): Building a versioned pipeline for ML-based quality control of environmental sensor data

Parallel Session D:

Remote sensing and marine ecosystems

Chaired by Tobias Weigel

Andreas Wernecke (MPI for Meteorology): Quantifying the impact of bedrock uncertainty on ice sheet model simulations by Gaussian Process modelling

Milad Asgarimehr (GFZ Potsdam): Remote Sensing of Global Ocean Wind Speed using GNSS Reflectometry and AI

Markus Pfeil (Kiel University): Approximation of a marine ecosystem model using artificial neural networks

Long Duc Phan (AWI): Artificial Intelligence for Cold Regions (AI-CORE) - a Pilot to bridge Data Analytics and Infrastructure Development

17:15 - 17:30

Break

17:30 - 18:00

Keynote 2

Kelly Caylor (UCSB): A mile wide and a pixel deep: Integrating computer vision and satellite imagery for coupled-natural human system modeling

18:00 - 18:10

Wrap-up



 

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