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CLASS:PUBLIC
CREATED:20260305T212308Z
DESCRIPTION:Analysis of machine learning model used in typhoon intensity prediction\n\n\n\nXingyue Hu\n\nMasters Candidate\n\nDepartment of Atmospheric Sciences\n\nSchool of Ocean and Earth Science and Technology\n\nUniversity of Hawai’i at Manoa\n\n\n\nYou are invited to a Zoom meeting.\n\nWhen: March 24, 2021 at 3:30PM HST\n\n\n\nRegister in advance for this meeting:\nhttps://hawaii.zoom.us/meeting/register/tJYvfu2urD8jEtb2-aKc4ISPr4W9YuHre-tE\n\n\n\nAfter registering, you will receive a confirmation email containing information about joining the meeting.\n\n\n\nAbstract:\n\n\nThere are about eighty to a hundred tropical cyclones generate every year. The Northwest Pacific has the highest frequency of tropical cyclones (TC), with about one third of the global total. And China is most affected by the tropical cyclones among the countries in the Northwest Pacific and South China Sea. We counted the number of TC landings from 1980 to 2017 and found that there are 99 tropical cyclones made the first landfall over China. Generally, when a TC moves closer to the inshore zone, the intensity of it tends to weaken, but there are also some situations that the TC enhances during this process.\n\n\n\nNow more and more artificial intelligence is used in the field of weather forecasting. If the two can be well combined, disaster prevention and mitigation can be better achieved. In this seminar, machine learning methods are used to build a preliminary model of typhoon intensity changes prediction.
DTEND;TZID=Pacific/Honolulu:20210325T023000Z
DTSTAMP:20260305T212308Z
DTSTART;TZID=Pacific/Honolulu:20210325T013000Z
LAST-MODIFIED:20260305T212308Z
LOCATION:Virtual Meeting
PRIORITY:5
SEQUENCE:0
SUMMARY;LANGUAGE=en-us:Atmospheric Sciences Seminar
TRANSP:OPAQUE
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