淡江大學機構典藏:Item 987654321/113040
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/113040


    Title: Ending Storm Version of the 7-day Weighted Analog Intensity Prediction Technique for Western North Pacific Tropical Cyclones
    Authors: Hsiao-Chung Tsai;Russell L. Elsberry
    Keywords: Forecasting techniques
    Date: 2017-12
    Issue Date: 2018-03-29 12:12:17 (UTC+8)
    Publisher: American Meteorological Society
    Abstract: The weighted analog intensity prediction technique for western North Pacific (WAIP) tropical cyclones (TCs) was the first guidance product for 7-day intensity forecasts, which is skillful in the sense that the 7-day errors are about the same as the 5-day errors. Independent tests of this WAIP version revealed an increasingly large intensity overforecast bias as the forecast interval was extended from 5 to 7 days, which was associated with “ending storms” due to landfall, extratropical transition, or to delayed development. Thus, the 7-day WAIP has been modified to separately forecast ending and nonending storms within the 7-day forecast interval. The additional ending storm constraint in the selection of the 10 best historical analogs is that the intensity at the last matching point with the target TC track cannot exceed 50 kt (where 1 kt = 0.51 m s−1). A separate intensity bias correction calculated for the ending storm training set reduces the mean biases to near-zero values and thereby improves the mean absolute errors in the 5–7-day forecast interval for the independent set. A separate calibration of the intensity spreads for the training set to ensure that 68% of the verifying intensities will be within the 12-h WAIP intensity spread values results in smaller spreads (or higher confidence) for ending storms in the 5–7-day forecast intervals. Thus, some extra effort by the forecasters to identify ending storm events within 7 days will allow improved intensity and intensity spread forecast guidance.
    Relation: Weather and Forecasting 32(6), p.2229-2235
    DOI: 10.1175/WAF-D-17-0151.1
    Appears in Collections:[Graduate Institute & Department of Water Resources and Environmental Engineering] Journal Article

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