Wildfires arrive outside their season. Storms hit places that never planned for them. As climate risks change, better forecasts, satellite monitoring, and risk models are increasingly shaping what gets insured, what gets built, where capital goes, and when people are warned.

But making a better forecast is only part of the story. What happens after the forecast?

Getting from prediction to action takes a long chain of work, and each link sits with a different group. A researcher building a model, a regulator deciding what counts as evidence, an insurer pricing exposure, and an engineer supplying the compute are all working on the same problem from different ends, and rarely in the same room.

This session brings that chain together: how climate data gets collected, how physical knowledge can improve models, how their outputs can be interpreted and explained, how they translate into policy and insurance, and what it takes to support these systems at scale.

Through short research presentations, a moderated panel, and audience discussion, we'll unpack these questions together.

This is the second session in a series by Lorong AI and Climate Change AI (CCAI) exploring how AI is shaping sustainability across different domains.

More About the Sharings

Dr Gianmarco Mengaldo will share on “From Physics to AI and Back.”

How can existing scientific knowledge improve AI systems, and how can AI in turn help us extract new knowledge?He'll share CondensNet, a physics-enhanced deep learning approach for representing cloud physics in climate models, alongside work on explainable AI for climate applications. Together, the two examples show how AI and physical knowledge can inform each other, with implications for how we build, interpret, and improve climate models. (Technical Level: 100)

Sang-Ho Yun will share on "Leveraging Radar Observations Before Reaching for AI in Climate Risk Monitoring".

Robust physics and direct observations should be leveraged whenever they're available. Interferometric SAR measures ground subsidence and forest structure to millimeter-to-centimeter precision using well-understood physical models, often outperforming AI-based alternatives that carry larger, harder-to-quantify uncertainty. Drawing on disaster response and carbon monitoring case studies, this talk makes the case for an observation-first approach to climate risk: build on rigorous physics and calibrated measurements as the foundation, and bring in AI only where it demonstrably adds value beyond what those methods already deliver. (Technical Level: 100)

More About the Speakers & Panelists

Dr Gianmarco Mengaldo (Moderator) is an Assistant Professor in the Department of Mechanical Engineering and the Department of Mathematics - by courtesy - at National University of Singapore. He is also a member of the Joint Advisory Group for the World Meteorological Organization (WMO), a United Nations (UN) agency. He received his BSc and MSc in Aerospace Engineering from Politecnico di Milano (Italy), and his PhD in Aeronautical Engineering from Imperial College London (United Kingdom).After his PhD he undertook various roles both in industry and academia, including at the European Centre for Medium-Range Weather Forecasts (ECMWF), and at the California Institute of Technology (Caltech). Dr Mengaldo adopts an interdisciplinary approach at the intersection of mathematical engineering, computational physics and AI to study complex systems that arise in various branches of applied science.His current research interests involve (i) integrating domain knowledge (e.g., physics) and AI; (ii) explainable AI, both theoretical and applied, among others. Dr Mengaldo’s main application areas include weather and climate, robotics, and finance.

Sang-Ho Yun is the Director of the Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS) and Associate Professor of the Asian School of the Environment (ASE) and the School of Electrical and Electronic Engineering (EEE) at Nanyang Technological University (NTU) in Singapore. To date, he has supported over 200 major disaster response efforts globally.Prior to joining NTU in 2021, he was a geophysicist and radar scientist at NASA’s Jet Propulsion Laboratory (JPL) for 14 years. Sang-Ho received the 2018 NASA Exceptional Public Achievement Medal and the 2014 NASA Exceptional Early Career Medal for innovative use of satellite Synthetic Aperture Radar (SAR) data in support of rapid post-disaster response. He also received JPL’s Voyager Award in 2015 and Mariner Award in 2012 for his outstanding achievements in humanitarian assistance using satellite observations.Prior to his work at NASA JPL, Sang-Ho was a postdoctoral fellow at the US Geological Survey in Menlo Park, California. He received his PhD in geophysics and MS in electrical engineering from Stanford University in California and his BS in earth system science from Seoul National University in Korea.

Dr. Chen Chen is Deputy Principal Research Scientist and Head of the Numerical Weather Prediction Branch at the Centre for Climate Research Singapore (CCRS). Her research combines weather and climate science with artificial intelligence, spanning timescales from weather forecasting to climate change projections. She contributed to Singapore’s Third National Climate Change Study (V3), which underpins national climate adaptation and resilience planning. Dr. Chen also serves on the Editorial Board of Environmental Data Science and the Program Committee of the Climate Informatics Workshop Series.

Zenia Chang works on climate risk and sustainability across Asia Pacific. As a Vice President at Marsh Risk Consulting, she helps companies understand how climate change could affect their businesses and what they can do about it. Her work covers both physical risks, such as extreme weather, and the business impacts of the transition to a lower-carbon economy. She holds an MPA in Environmental Science and Policy from Columbia University and is interested in how AI could make climate risk analysis more useful for real-world decisions.

More About the Co-host

Climate Change AI (CCAI) is a global non-profit working at the intersection of AI and climate. It curates the climate workshops at the leading AI conferences, runs an AI-for-climate Summer School with over 16,000 registrations from 175 countries, and is a core knowledge partner in the AI Climate Institute launched at COP30. In Asia, CCAI partners with Lorong AI on this series and works with organisations including Temasek Foundation.

Dr Ivan Poon is CCAI's Asia Lead, responsible for building the AI-for-climate ecosystem across the region, and curates this session. He holds a PhD in the built environment from NUS, with a research background in ML-driven modelling of urban solar energy and building performance, and works at the meeting point of technical research and implementation.

More About the Series

Off‑Script is a conversation series by that brings together practitioners to explore ideas at the intersection of technology, design, and society. Through candid dialogue, live feedback, demos, and shared experiences, the series creates space for thoughtful exchange beyond slide decks.

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