Data Assimilation

Assimilation of diverse observation-based data 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 or assimilating climate model output with the observations. Well-curated benchmark datasets have the potential to advance several geoscience problems.