Climate Modeling and Analysis: Difference between revisions

*'''[[Remote Sensing|Collecting data for climate models]]''': Assimilation of diverse sources can improve climate models, and machine learning can transform raw sensor output into more relevant derived data. Relevant applications include sensor calibration and analyzing information in remote sensing data. Well-curated benchmark datasets have the potential to advance several geoscience problems.

*'''[[Accelerating Climate Models|Accelerating climate models]]''': Physical constraints are key ingredients for cloud, aerosol, ice sheet, and sea level models. Traditional solutions to these physics-based models are computationally expensive, but machine learning components can help alleviate the most problematic bottlenecks.

 

=== Forecasting extreme events ===