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TZID:Pacific/Honolulu
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CLASS:PUBLIC
CREATED:20261008T073459Z
DESCRIPTION:Calibrating Nino 3.4 SST Forecast Ensembles using Bayesian Model Averaging\n\n\n\nHanpei Zhang\n\nAtmospheric Sciences M.S. Candidate\n\nDepartment of Atmospheric Sciences, SOEST\n\nUniversity of Hawaii at Manoa\n\n\n                            \nSeminar Date:      Wednesday, November 8, 2017\n\nRefreshments:      3:00pm at MSB lanai\n\n                             Free Cookies, Coffee & Tea Provided\n                             (Please Bring Your Own Cup)\n\nSeminar Time:      3:30pm\n\nLocation:              Marine Sciences Building, MSB 100\n\n\n\nAbstract:\n\n\nIndividual models have strengths and weaknesses which lead model evaluation studies to conclude that “no single model can be considered the ‘best’ and it is important to utilize the results from a range of different models.” Bayesian model averaging (BMA) is an effective tool not only for describing uncertainties associated with each model simulation but also for providing forecast performance evaluation of different models. The BMA method in this study was developed for multi-model ensemble forecasts of seasonal sea surface temperatures (SST) in the Niño 3.4 region. We aim to use the BMA method to weight the different SST forecast models in such a way that the weighted estimate is a better predictor of SST than any of the individual models of the ensemble.
DTEND;TZID=Pacific/Honolulu:20171109T023000Z
DTSTAMP:20261008T073459Z
DTSTART;TZID=Pacific/Honolulu:20171109T013000Z
LAST-MODIFIED:20261008T073459Z
LOCATION:Marine Sciences Building, MSB 100
PRIORITY:5
SEQUENCE:0
SUMMARY;LANGUAGE=en-us:Atmospheric Sciences Seminar
TRANSP:OPAQUE
UID:179148089932198web-support-l@lists.hawaii.edu
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