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Learn how to integrate high-resolution simulations, reanalysis datasets, and long-term observations to develop advanced atmospheric retrievals in this 45-minute conference presentation. Explore a novel methodology that uses simulations to train feature extractors, which are then fine-tuned with observational data through generative techniques to reduce experimental errors. Discover how this approach addresses the limitations of traditional Earth system assessments that rely on models, satellite remote sensing, and in situ measurements, where models often simplify small-scale processes and observations carry significant uncertainties. Examine the application of this method to two critical atmospheric variables: vertical wind velocity and cloud droplet number concentration, both essential for understanding cloud evolution, turbulence, and long-term climate change. Gain insights into important climate trends revealed over recent decades and their major implications for future climate projections, presented by a researcher from NASA's Global Modeling and Assimilation Office at IPAM's Mathematics and Machine Learning for Earth System Simulation Workshop.
