Climate Modeling and Analysis: Difference between revisions
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*'''[[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. |
*'''[[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. |
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*'''[[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. |
*'''[[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. |
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*'''Working with climate models''': |
*'''Working with climate models''': Climate models can be extremely complex, and climate predictions are often made using the outputs of 20+ climate models. ML can help streamline existing climate models. For instance, ML can help identify and leverage relationships between variables within climate models, in order to streamline these models. ML can also help intelligently combine the outputs of multiple climate models in order to simplify computation with these ensembles. |
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=== Forecasting extreme events === |
=== Forecasting extreme events === |
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