120th Data Assimilation and Prediction Science Seminar

Speaker: Professor Lall (Columbia University)
Title: "Taming the Storm: Can We Predict, Engineer, and Reduce Losses from Climate and Weather Extremes?"
Abstract: Climate and weather extremes — storms, heat waves, floods, droughts, and compound events — have become the defining natural hazard challenge of the 21st century. Their growing frequency and intensity are overwhelming engineered infrastructure, disrupting global supply chains, and propagating risks across societies through teleconnections that no single country can insulate itself against. While climate change mitigation through decarbonization remains an urgent priority, even optimistic emissions trajectories leave us facing decades of increasing exposure. Climate adaptation efforts — improved infrastructure design, financial instruments, early warning systems — are essential but are constrained by limited data, deep uncertainty in future projections, and the diffuse question of who bears responsibility for action.
This talk argues that a third pillar is emerging and demands serious scientific and institutional attention: Climate Stabilization, or the deliberate modification of developing weather and climate extremes to reduce their societal impact. Rather than waiting for disasters to unfold and recovering afterward, this paradigm asks whether the physical dynamics of the atmosphere offer leverage points — windows in time and space — where strategically placed, small perturbations could redirect the trajectory of an extreme event. This is the core idea of Weather Jiu-Jitsu: exploiting the inherent instabilities and nonlinear sensitivities of atmospheric circulation to achieve large-scale redirection of an extreme using energy borrowed from the circulation itself, not brute-force external forcing. J
The talk will address the foundational questions this agenda raises for a forecasting and Earth science community: What physical mechanisms enable or constrain atmospheric steering? How can ensemble prediction systems, adjoint methods, and emerging AI tools be harnessed to identify intervention points and compute impact outcomes with spatial specificity? What are the data and modeling gaps? How do we frame the ethical and governance dimensions as this moves from laboratory curiosity to potential operational deployment and commercial application?
 I will sketch a research roadmap integrating chaos-informed perturbation theory to AI-enabled adaptive control optimization that builds on AI-accelerated impact forecasting to provide the foundation for Climate Stabilization as a rigorous scientific enterprise and, within a decade, a viable business with measurable returns to investors and societies alike. We hope that this will stimulate discussion with RIken's Moonshot Goal 8 program, which is exploring similar scientific and technological frontiers.

Speaker: Professor Mengqian Lu (Hong Kong University of Science and Technology)
Title: Bridging Climate Data to Actionable Decision-Making Across Industries
Abstract: Extreme weather is escalating—impacting infrastructure, supply chains, and profitability across the world. At the same time, sustainability targets demand that businesses go green without sacrificing growth. The question is no longer if climate risk matters, but how to act on it—quickly and smartly. This talk presents climate solutions that combine advanced climate modeling with AI to deliver industry-specific, actionable insights. Developed at HKUST through the Center for Climate Resilience and Sustainability (CCRS) and the World Sustainable Development Institute (WSDI), this AI–dynamical hybrid system is already being applied across key sectors, including renewable energy, Arctic logistics, and disaster risk management. These tools enable organizations to make faster, more informed decisions under uncertainty. Backed by UNESCO and the WMO, this Research-to-Operation (R2O) framework turns complex climate data into operational tools that drive resilience, reduce losses, and uncover new opportunities. Real-world case studies will be shared to spark cross-sector collaboration between science, business, and policy.

Speaker: Professor Hyun-Han Kwon (University of Seoul)
Title: Bayesian Mixture Extreme-Value Modeling of Nonstationary Extreme Precipitation Across U.S. Regions
Abstract Extreme precipitation is a major driver of flood risk, infrastructure stress, and climate-related disaster losses. However, annual maximum rainfall often reflects multiple physical mechanisms, including frontal or convective systems, tropical-cyclone-related rainfall, and transitional atmospheric regimes. Treating these extremes as samples from a single homogeneous process can obscure how regional rainfall risks are changing. This talk presents an ongoing study of nonstationary extreme precipitation using a Bayesian mixture extreme-value model. The model represents annual maximum daily precipitation as a combination of latent low- and high-intensity rainfall regimes, with time-varying component behavior and regime probabilities. This allows changes in return levels to be separated into contributions from baseline rainfall intensity, high-intensity event magnitude, and the probability of entering an extreme-producing regime. The framework is applied to long-term U.S. station records across the Southeast/Gulf, Mid-Atlantic, Northeast, and inland-control regions. Tropical-cyclone proximity and ERA5-based atmospheric diagnostics are used as external physical evidence, rather than as imposed predictors in the likelihood, to interpret the latent high-intensity regime and its regional variability. The broader goal is to move extreme-value analysis beyond stationary design estimation toward mechanism-aware and decision-relevant understanding of climate risk. By linking Bayesian uncertainty quantification, hydrometeorological interpretation, and regional comparison, this work provides a basis for improved infrastructure planning, impact-based forecasting, and future AI-enabled climate risk services.