IEEE R8 Climate Challenge: AI in Enhanced Weather Forecasting
IEEE IHTC 2024
As a result of climate change, extreme weather events are becoming increasingly frequent and resulting in a growing need for more accurate real-time updated weather prediction where short term weather forecasting (nowcasting) is gaining critical importance. With availability of real-time open-source data such as Numerical Weather Prediction (NWP) forecasts, satellite and weather radar imagery, and localized weather measurements, new and interdisciplinary possibilities are emerging in the way weather forecasts are generated. Multi-modal real-time data can now be paired with machine learning approaches to improve the accuracy and reliability of weather predictions. Similar approaches are already being recognized with example initiatives by world’s leading companies and associations in the domain of meteorology and artificial intelligence.
This session brings together information and communication technologies experts from the artificial intelligence domain and meteorological experts with the aim to merge data-based and physics-based approaches for enhancing short-term weather forecasts. Second part of the session will be devoted to finals of a IEEE Region 8 (Europe, Middle East and Africa) hackathon-style competition on “AI in Enhanced Weather Forecasting” (available at IEEE DataPort) that leverages multi-source and multi-modal weather data and combined them with statistic and machine learning algorithms to generate accurate and reliable short-term weather forecasts for three case studies selected as three biomes of IEEE Region 8: Savanna Preservation, Clean Urban Air and Resilient Fields. The session envisions exchanging approaches and algorithms, and pinpointing guidelines towards worldwide coverage of improving the accuracy of weather forecasting.
9:30 – Introduction to the Challenge by Hrvoje Novak, University of Zagreb Faculty of Electrical Engineering and Computing, Croatia
10:00 – Patrick Zippenfenig, Founder of Open-Meteo, UK (pending)
Coffee break
11:00 – Benjamin Rosman, Professor at University of the Witwatersrand, South Africa, Co-Founder of Deep Learning Indaba (pending)
11:30 – Florian Pappenberger, Deputy Director-General & Director of Forecasts and Services at the ECMWF (pending)
12:00 – Finalists Presentation – 10 min each
Vinko Lešić
IEEE Region 8 Vice-Chair, Member Activities
Vinko Lesic received his PhD degree in 2014 from University of Zagreb Faculty of Electrical Engineering and Computing, Zagreb, Croatia (UNIZG-FER). Today, he is an Associate Professor within the Laboratory for Renewable Energy Systems (LARES – https://www.lares.fer.hr) at the Department of Control and Computer Engineering of UNIZG-FER. His focus is on control algorithms, optimisation and artificial intelligence in renewable energy and sustainability topics or more particularly convex optimisation and machine learning methods for buildings and transport energy efficiency, microgrid operation, and agriculture. His patented PhD research he shaped into the technology transfer project with a multinational aerospace R&D company. Since then, he has been leading 8 scientific-research and development and 4 technology transfer projects, and an own research group of 25 scientists. He shares his scientific commercialisation experience through mentoring in local and international programmes for technology innovations. He co-authored over 80 journal and conference papers and 5 patents. He is an active volunteer in IEEE association, currently serving the role of Vice Chair of IEEE Region 8 Member Activities, coordinating programs on knowledge transfer and international networking of over 80,000 scientists and engineers from Europe, Africa and Middle East. He co-founded a program of IEEE Region 8 Entrepreneurship Initiative, aimed at design thinking, startups formation and acceleration, which examined over 800 technical innovations from 40 countries.
Hrvoje Novak
University of Zagreb Faculty of Electrical Engineering and Computing, Croatia
Hrvoje Novak (Member, IEEE) received the Ph.D. degree from the University of Zagreb Faculty of Electrical Engineering and Computing, Zagreb, Croatia, in 2019. Currently, he is a Lead Researcher with the Department of Control and Computer Engineering and a member of the Laboratory for Renewable Energy Systems. He has coauthored several scientific and professional articles in the field of smart transport, buildings, water distribution, and agriculture. His current research interests include the domain of predictive control and machine learning algorithms with applications in technical systems.