Presentation | NG33B: Advances in Data Assimilation, Data Fusion, Machine Learning, Predictability, and Uncertainty Quantification in the Geosciences IV Poster
Poster
NG33B-0434: Nonlinear GenAI-Based Ensemble Data Assimilation Methods Applied to Convective-Scale Cloud Microphysical Parameter Estimation
Author(s): Derek Posselt, NASA Jet Propulsion Laboratory (First Author, Presenting Author) Hristo Chipilski, Florida State University
Poorly known cloud processes cause uncertainties in weather and climate predictions. Data assimilation can be used to reduce model errors, but the complex relationships between unknown processes and observations make this challenging. This presentation illustrates how machine-learning based nonlinear data assimilation can be used to mitigate model uncertainty.
