J. Xavier Prochaska, professor, UC Santa Cruz<\/em><\/strong><\/p>\n\n\n\n I will describe lessons learned from our team\u2019s program to apply machine learning to large remote sensing datasets of sea surface temperature (SST) on submesoscales (i.e. ~10-100km).\u00a0 Adopting unsupervised techniques, we first identified outliers in MODIS SST imagery on these scales and associated them with the most dynamic regions of the global ocean (e.g. western boundary currents).\u00a0We next developed a \u201clanguage\u2019\u2019 (a.k.a., data manifold) to compactly describe the great diversity of patterns and structure in SST at the submesoscale.\u00a0 This enables one to search for and analyze specific dynamical signatures in SST across the globe and over the past two decades.\u00a0 Most recently, we used a Large Language-inspired model trained on ECCO outputs to successfully predict SST under masked (e.g., cloudy) data.\u00a0 I will conclude by highlighting ongoing work for the retrieval of inherent optical properties from hyperspectral images in anticipation of forthcoming NASA\/PACE datasets.<\/p>\n","protected":false},"excerpt":{"rendered":" Speaker J. Xavier Prochaska, professor, UC Santa Cruz Lessons Learned from Applying Machine Learning to Remote Sensing Observations of Sea Surface Temperature Abstract I will describe lessons learned from our team\u2019s program to apply machine learning to large remote sensing datasets of sea surface temperature (SST) on submesoscales (i.e. ~10-100km).\u00a0 Adopting unsupervised techniques, we first […]<\/p>\n","protected":false},"author":2120,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[1],"tags":[],"class_list":["post-184049","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"acf":[],"_links":{"self":[{"href":"https:\/\/web.uri.edu\/gso\/wp-json\/wp\/v2\/posts\/184049","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/web.uri.edu\/gso\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/web.uri.edu\/gso\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/web.uri.edu\/gso\/wp-json\/wp\/v2\/users\/2120"}],"replies":[{"embeddable":true,"href":"https:\/\/web.uri.edu\/gso\/wp-json\/wp\/v2\/comments?post=184049"}],"version-history":[{"count":1,"href":"https:\/\/web.uri.edu\/gso\/wp-json\/wp\/v2\/posts\/184049\/revisions"}],"predecessor-version":[{"id":184050,"href":"https:\/\/web.uri.edu\/gso\/wp-json\/wp\/v2\/posts\/184049\/revisions\/184050"}],"wp:attachment":[{"href":"https:\/\/web.uri.edu\/gso\/wp-json\/wp\/v2\/media?parent=184049"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/web.uri.edu\/gso\/wp-json\/wp\/v2\/categories?post=184049"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/web.uri.edu\/gso\/wp-json\/wp\/v2\/tags?post=184049"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Lessons Learned from Applying Machine Learning to Remote Sensing Observations of Sea Surface Temperature<\/h2>\n\n\n\n
Abstract<\/h2>\n\n\n\n