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CREATED:20260314T124127Z
DESCRIPTION:Improving ENSO forecasts using Bayesian model averaging\n\n\n\nProfessor Pao-Shin Chu\n\nDepartment of Atmospheric Sciences, S.O.E.S.T.\n\nUniversity of Hawaii at Manoa\n\n\n                            \nSeminar Date:      Wednesday, October 2, 2019\n\nRefreshments:      3:00pm at MSB courtyard\n\n                             Cookies, Coffee & Tea Provided\n\nSeminar Time:      3:30pm\n\nLocation:              Marine Sciences Building, MSB 100\n\n\n\nAbstract:\n\n\nThe Bayesian Model Averaging (BMA) method was developed to combine the NCEP/CPC three statistical and one dynamical forecast products of seasonal Ocean Niño Index (ONI) from 1982 to 2010. The BMA weights were derived directly from the predictive performance of the combined models. The highly efficient expectation–maximization (EM) algorithm was used to achieve numerical solutions. We show that the BMA method can be used to assess the performance of the individual models and assign greater weights to better performing models. The continuous ranked probability score is used to evaluate the BMA probabilistic forecasts. As an elaboration of the reliability diagram, the attributes diagram that includes the calibration function, refinement distribution, and reference lines is also used. The combination of statistical and dynamical models is found to provide a more skillful and reliable prediction of the ONI than only using a suite of statistical models, a single dynamical model, or the equally weighted average forecasts from all four models. Probability forecasts of El Niño events based only on winter ONI values are reliable and exhibit sharpness. In contrast, an under-forecasting bias and less reliable forecasts are noted for La Niña.
DTEND;TZID=Pacific/Honolulu:20191003T023000Z
DTSTAMP:20260314T124127Z
DTSTART;TZID=Pacific/Honolulu:20191003T013000Z
LAST-MODIFIED:20260314T124127Z
LOCATION:Marine Sciences Building, MSB 100
PRIORITY:5
SEQUENCE:0
SUMMARY;LANGUAGE=en-us:Atmospheric Sciences Seminar
TRANSP:OPAQUE
UID:177352808736124web-support-l@lists.hawaii.edu
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