Fall 2024 Lecture in Climate Data Science: JOSEPH LOCKWOOD
Address:
Columbia Innovation Hub – Tang Family Hall
2276 12th Avenue Room 202 New York, NY 10027
Event:
Title: “Modeling Tropical Cyclone Wind Fields and Deep Convection Drivers with Generative AI and Explainable Models“
Abstract: This presentation focuses on two projects: (1) generative super-resolution for downscaling tropical cyclone (TC) wind fields, and (2) explainable AI to refine integrated moisture convergence indices for deep convection cloud regimes and identify key environmental drivers.
The first project introduces a deep learning framework that downscales TC wind fieds from ERA5 data(0.25°) to high-resolution observations (0.05°). The framework includes a debiasing neural network and a conditional denoising diffusion probabilistic model (DDPM) for generative super-resolution. This approach captures fine-scale spatial details, enabling accurate high-resolution wind field prediction from coarse data.
In the second project, we refine the environmental indices governing integrated moisture convergence for deep convection cloud regimes. Existing indices such as saturation fraction and moist convective insability are often arbitrarily defined. Using a variational autoencoder (VAE), we uncover latent representations of deep convection cloud regimes, and symbolic regression identifies key environmental variables controlling moisture convergence. Preliminary results demonstrate the value of explainable AI in improving predictions of deep convection cloud processes.
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