Citizen Weather Data and Machine Learning to identify urban climate risk at high spatio-temporal resolution
This is a hybrid meeting. Please find the Teams link in the abstract. Cake, tea and coffee before the talk and Pizza after the talk.
Abstract:
The rapid increase of global mean temperature and unprecedented heat events require new approaches to support and monitor the climate adaptation and heat resilience of cities. Crafting effective plans necessitates accurate data and tools that adapt to the ever-changing dynamics of urban environments.
This presentation will show the recent advances in diagnosing and treating accurately, city by city, overheated urban areas (in time and space) where climate adaptation should be prioritised to promote heat resilience. The research aims to fully integrate crowdsourced urban climate observations (citizen weather stations) with satellite and remote sensing data using machine learning techniques to generate high spatio-temporal resolution observations of urban atmospheric states and dynamics. The results will support the development of an urban heat diagnosis tool with global applicability to enable insight and evidence-supported actions to promote zero-carbon and sustainable cooling at different scales. This research is part of the Future of Cooling Programme of the Oxford Martin School.

Bio:
Jesus Lizana is Associate Professor in Engineering Science at the University of Oxford, with a unique experience profile in architecture and engineering. His research focuses on the cross-disciplinary challenges to support the transition towards zero carbon climate-responsive buildings.
At Oxford, Lizana is engaged in many research initiatives and has received several prestigious and extensive grants, including a Marie Curie Fellowship. He leads the research on Zero-Carbon Space Heating and Cooling at ZERO Institute and supports the interdisciplinary research in the Future of Cooling Programme of the Oxford Martin School. Alongside his academic career, Lizana also serves as a consultant on many building energy-related projects, data science, and sustainable cooling across various global locations, including the United Kingdom, India, Spain, Morocco, and Saudi Arabia.
Lizana received his PhD in low-carbon buildings at the University of Seville in Spain after completing a BSc in Architecture and an MSc in Building Engineering. Previously to his appointment at Oxford, he has lectured and conducted research at the University of Seville (Spain), the University of Edinburgh (Scotland), the Technical University of Munich (Germany), Universidade de Lisboa (Portugal), and the Spanish National Research Council (Spain).

Teams link: teams.microsoft.com/l/meetup-join/19%3ameeting_MzNiYmZmZWYtZGNmOS00NTMzLTg1Y2ItODQ1YTgyYzBkNjgx%40thread.v2/0?context=%7b%22Tid%22%3a%22cc95de1b-97f5-4f93-b4ba-fe68b852cf91%22%2c%22Oid%22%3a%22e44820d7-5edb-4030-9763-4c8cdc3aafd6%22%7d
Date: 13 March 2024, 17:00 (Wednesday, 9th week, Hilary 2024)
Venue: Wolfson College, Linton Road OX2 6UD
Venue Details: Levett Room
Speaker: Dr Jesus Lizana
Organising department: Wolfson College
Organisers: Mr Csaba Botos (University of Oxford), Dr. Yi Yin (Wolfson College, University of Oxford)
Organiser contact email address: yi.yin@wrh.ox.ac.uk
Part of: Oxford Cross-Disciplinary Machine Learning (OxfordXML) Research Cluster Seminar Series
Booking required?: Not required
Cost: Free (cake, tea and coffee provided)
Audience: Public
Editor: Yi Yin