Predicting rain and lightning using statistical and machine learning techniques

dc.contributor.author	Schumacher, Courtney
dc.date.accessioned	2023-02-02T20:22:41Z
dc.date.available	2023-02-02T20:22:41Z
dc.date.copyright	2022	en_US
dc.date.issued	2023-02-02
dc.description.abstract	Convective storms are highly intermittent and intense, making their occurrence and strength difficult to predict. This is especially true for climate models, which have grid resolutions much coarser (e.g., 100 km) than the scale of a storm’s microphysical and dynamical processes (< 1 km). Physically-based parameterizations struggle to account for this scale mismatch, causing large model errors in rain and lightning. This talk will explore some avenues of using statistical techniques (such as generalized linear and log-Gaussian Cox process models) and machine learning methods (such as random forests and neural networks) that are trained by satellite observations of thunderstorms to see how well they can improve upon existing physical parameterizations in producing accurate rain and lightning characteristics given a set of large-scale environmental conditions.	en_US
dc.description.reviewstatus	Unreviewed	en_US
dc.description.scholarlevel	Faculty	en_US
dc.description.sponsorship	Lansdowne Lecture Series	en_US
dc.identifier.uri	http://hdl.handle.net/1828/14721
dc.language.iso	en	en_US
dc.title	Predicting rain and lightning using statistical and machine learning techniques	en_US
dc.type	Video	en_US

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