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Climate Modeling and Analysis: Difference between revisions
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=== Uniting data, ML, and climate science ===
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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.
*'''Working with climate models''': It is possible to streamline existing climate models, pruning them down to key relationships simplifying computation with ensembles.
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=== Forecasting extreme events ===
*'''[[Storm Tracking|Storm tracking]]''': While climate models can forecast long-term changes in the climate system, separate systems are required to detect specific extreme weather phenomena, like cyclones, atmospheric rivers, and tornadoes. Identifying extreme events in climate model outputs can inform scientific understanding of where and when these events may occur. ML can help classify, detect, and track climate-related extreme events such as hurricanes in climate model outputs.
*'''[[Localized Extreme Event Forecasting|Local forecasts]]''': Storms, droughts, fires, floods, and other extreme events are expected to become stronger and more frequent as climate change progresses. Machine learning can be used to refine what are otherwise coarse-grained forecasts (e.g., generated from climate or weather prediction models) of these extreme weather events. These high-resolution forecasts can guide improvements in system robustness and resilience.
== Background Readings ==
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