Tuesday 5 November

12:30–13:00 Registration Weather Room

Chairperson: J.-N. Thépaut (ECMWF) Lecture Theatre

13:00–13:20 Welcome + Scope and motivations for the workshop V.-H Peuch, ECMWF

13:20–13:40 ECMWF vision for Big Data, AI and cloud Computing Martin Palkovic, ECMWF

13:40–14:00 Copernicus Big Data, the DIASes and AI D. Quintart, 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 Lobby

Chairperson: C. Vitolo (ECMWF) Lecture Theatre

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, A. Prieto Nemesio, E. C. de Grandi, A. S. López, 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) Weather Room, Lobby and Canteen

Copernicus Climate Change ServiceCopernicus Atmosphere Monitoring Service

IMPLEMENTED BY

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

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

Thursday 7 November

08:30–09:00 Registration Weather Room

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 Lobby

13:00–14:00 Lunch ECMWF Canteen

Chairperson: V.-H. Peuch ECMWF Lecture Theatre

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 J.-N. Thepaut and F. Rabier, ECMWF

16:00 End of the workshop

Wednesday 6 November

08:30–09:00 Registration Weather Room

Chairperson: S. Siemen (ECMWF) Lecture Theatre

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

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 Lobby

Chairperson: P. Dueben (ECMWF) Lecture Theatre

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) Lecture Theatre

14:00–14:20 Wavelet-based retrieval of weather analogues from ERA5 B. Raoult, ECMWF

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 Lobby

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