Tuesday 5 November

Chairperson: J.-N. Thépaut (ECMWF)

13:00–13:20 Welcome F. Rabier, ECMWF Director General

13:20–13:40 Scope and motivations for the workshop V.-H. Peuch, ECMWF

13:40–14:00 Copernicus Big Data, the DIASes and AI D. Quintard, EC/DG-GROW

14:00–14:20 The Rise of Artifi cial Intelligence for Earth Observation P.-P. Mathieu, ESA

14:20– 14:40 Progress and challenges for the use of deep learning to improve weather forecast P. Dueben, ECMWF

14:40– 15:00 Network analysis and climate science, global and regional opportunities - remote talk

A. Bracco, F. Falasca, L. Novi, J. Crétat, P. Braconnot, Georgia Tech

15:00–15:30 Coff ee break

Chairperson: C. Vitolo (ECMWF)

15:30–15:50Machine Learning applications in environmental remote-sensing: moving from data reproduction to spatial representation

H. Meyer, C. Reudenbach and T. Nauss, Uni. Muenster and Uni. Marburg

15:50–16:10 Artifi cial Intelligence in Earth Observation - Application in the Copernicus Programme

P. Helber, B. Bischke, J. Hees and A. Dengel, DKFI

16:10–16:30 Super Resolution of SENTINEL- 2 images with Deep Neural Networks

L. De Juan and D. Nobileau, Capgemini

16:30–16:50BIGMIG: Use of Deep Convolutional LSTMs and Sentinel 2 for spatio-temporal semantic segmentation of smallholder agriculture in Mozambique

J. Reay, GMV

16:50–17:10On the detection of metocean features on SAR images using Deep Learning: perspectives for Copernicus Sentinel-1

N. Longepe, C. Wang, A. Mouche, P. Tandeo and R. Husson, CLS

17:10–17:30 Potential of Deep Learning Technique in Satellite based Fire Detection and Emission Estimation Study

T. Zhang, M. Wooster and D. Fisher, King’s College London

17:30–17:50

Python for Earth Observation (PYEO) - a machine-learning-based, automated processing chain for Sentinel-2 image stack classifi cation and change detection

Y. Gou, Uni. Leicester

18:00–20:00 Poster session (drinks and fi nger food)

Copernicus Climate Change ServiceCopernicus Atmosphere Monitoring Service

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The 1st Artifi cial Intelligence for Copernicus Workshop. ECMWF, Reading (UK)5-7 November 2019Agenda

Attend remotely using the vimeo invitationhttps://vimeo.com/event/16576

Thursday 7 November

09:00–13:00 4 parallel discussion sessions continued

1. Lecture Theatre Splinter group 1 2. Large Committee Room Splinter group 23. Meeting Room 1 Splinter group 34. Meeting Room 6 Splinter group 4

10:45–11:15 Coff ee break

13:00–14:00 Lunch (ECMWF canteen)

Chairperson: J.-N. Thépaut (ECMWF)

14:00–14:2014:20–14:4014:40–15:0015:00–15:20

Report from Splinter group 1 discussionReport from Splinter group 2 discussionReport from Splinter group 3 discussionReport from Splinter group 4 discussion

15:20–16:00 General discussion and concluding remarks

16:00 End of the workshop

Wednesday 6 November

Chairperson: S. Siemen (ECMWF)

09:00–09:20 Using Deep Learning to identify weather patternsJ. Kunkel, B. Lawrence, D. Galea and J. Adie, Uni. Readingand NVIDIA AI Tech Center

09:20–09:40 Wavelet-based retrieval of weather analogues from ERA5 B. Raoult, ECMWF

09:40–10:00 Fusing radar and imager data for improved cloud classification D. Watson-Parris et al., Uni. Oxford

10:00–10:20 Using Self-Organising Maps to understand non-linear cloud-circulation couplings

S. Adams and M. Webb, UK Met Offi ce

10:20–10:40 Improving Advection Baselines for Precipitation Nowcasting with Deep Learning

K. Lenc, S. Ravuri, P. Mirowski, M. Wilson and S. Mohamed, Deepmind

10:45–11:15 Coff ee break

Chairperson: P. Dueben (ECMWF)

11.20–11:40 Deep Learning for satellite precipitation estimation S. Dewitte, A. Moraux, B. Cornelis, A. Munteanu, RMIB and VUB

11:40–12:00 Downscaling of Low Resolution Wind Fields using Neural Networks

M. Kern and K. Höhlen, Technical Uni. Munich

12:00–12:20 Can neural networks be effective replacements for parameterisation schemes?

M. Chantry, T. Palmer and P. Dueben, Uni. Oxford and ECMWF

12:20–12:40 On the use of CAMS data to improve air quality regional maps and forecasts through data fusion

J. Sousa, B. Maiheu, S. Vranckx, L. Janssens, VITO

12:40–13:00 A fl ood forecasting case study using diff erent machine learning models (ESoWC prize 2019)

L. Kugler and S. Lehner, Uni. Vienna

13:00–14:00 Lunch (ECMWF canteen)

Chairperson: V.-H. Peuch (ECMWF)

14:00–14:20 Deep Learning application for High Energy Physics: examples from the LHC S. Vallecorsa, CERN

14:20–14:40 Developing AI activities in the British Antarctic Survey A. Fleming, A. Faul and S. Hosking, British Antarctic Survey

14:40–15:00 Challenges in Bayesian Network Modelling of Climate and Weather Data - remote talk M. Scutari, IDSIA

15:00–15:20 Exascale Deep Learning for climate analytics - remote talk T. Kurth, LBL

15:20–15:40 On the suitability of convolutional neural networks for climate downscaling

J. Baño-Medina, R. Manzanas and J. M. Gutiérrez, Universidad de Cantabria

15:40–16:00 Predicting vegetation health in Kenya using Machine Learning and climate data (ESoWC prize 2019)

T. Lees, G. Tseng, S. Reece, S. Dadson, Uni. Oxford and Okra Solar

16:00–16:30 Coff ee break

16:30–18:00 4 parallel discussion sessions

16:30–16:45 Introduction to the discussion sessions (in splinter groups)

1. Tackling challenges in satellite-based climate monitoring with Artifi cial Intelligence

2. ADAM: a geospatial data hub for AI applications

3. Using the CMEMS data to feed a high-resolution process-based model in order to develop a sewage management tool based on artifi cial neural networks: Application to the sanitation system of Muskiz

4. Automatic land categorisation by processing S-2 images with transfer learned CNN

5. Machine Learning meets Wavelets in Magnetic Earth Observation

6. Post-processed correction of systematic numerical weather prediction temperature errors using machine learning

7. Automatic young tree detection on SAR data using machine learning algorithms

8. Resource management of image-processing workflows with Deep Reinforcement Learning

9. AsSISt: Aircraft Support & Maintenance Service

10. Convolutional Neural Networks for evaluating atmospherically-forced sea level variations

11. Swedish National Space Data Lab on Kubernetics

12. Lessons learned training AI cloud products for CLARA-A3 and CLAAS-3, with reduced retrieval

1. Lecture Theatre Splinter group 1 2. Large Committee Room Splinter group 23. Meeting Room 1 Splinter group 34. Meeting Room 6 Splinter group 4

U. Pfeifroth and S. Finkensieper, DWD

R. M. Figuera and S. Natali, SISTEMA GmbH

J. Garcia-Alba, Universidad de Cantabria

G. Margarit-Martin and O. Dutta,GMV

O. Kounchev,Bulgarian Academy of Science

R. Isaksson, SMHI

S. Daniel and S. Angeli

L. De Juan, Capgemini

L. De Juan, Capgemini

M. Lalande et al., CNES & IGE Uni. Grenoble Alpes

G. Kovács et al., Luleå Uni. Of Technology

Nina Håkansson, SMHI