Operational scheme for predicting tropical cyclone wind radius based on a statistical-dynamical approach and track pattern clustering

被引:0
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作者
Kim, Hye-Ji [1 ,2 ]
Moon, Il-Ju [1 ,4 ]
Won, Seong-Hee [3 ]
机构
[1] Jeju Natl Univ, Typhoon Res Ctr, Jeju, South Korea
[2] Chungnam Natl Univ, Res Inst Marine Sci, Daejeon, South Korea
[3] Korea Meteorol Adm, Natl Typhoon Ctr, Jeju, South Korea
[4] Jeju Natl Univ, Typhoon Res Ctr, 102 Jejudaehak Ro, Jeju 63243, South Korea
关键词
real-time prediction; statistical-dynamical model; track pattern clustering; tropical cyclone wind radius; TYPHOON TRACKS; SIZE; INTENSITY; CLIMATOLOGY; FORECASTS;
D O I
10.1002/met.2193
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
An operational scheme for predicting the symmetric R30 and R50 of tropical cyclones (TCs) in the western North Pacific was developed using a statistical regression method and track pattern clustering (four clusters). The statistical-dynamical model employs multiple linear regressions of two to eight variables at each cluster and forecast lead time. The dependent variable for prediction was the change in the 5-kt wind radius (R5)-a proxy of TC size-relative to the initial time. The performance of the model was compared for the training (2008-2016) and testing (2017-2018) periods. The effect of clustering on TC size prediction was evaluated by comparing the performance of the non-clustering and clustering models. The clustering model improved the prediction of TC size by 3%-24% at all lead times during the training period, especially with a significant improvement of up to 43% in Cluster 2. In Cluster 2, because most TCs tend to develop strongly and continue to increase in size, it greatly reduced the variability in TC size through clustering, allowed for smarter predictor selection, and ultimately improved TC size prediction. In the real-time R30 and R50 predictions for the 2017 and 2018 TCs, the error of the clustered model was 18%-19% less than that of the non-clustered model. The analysis results revealed that the real-time prediction errors of the current model increase when the TC tracks are difficult to classify into specific clusters, the predicted environments and TC tracks are inaccurate, and the size and intensity of a TC rapidly increase. The execution process of the statistical-dynamical model for real-time wind radii predictions in real time is as follows: (i) the forecast track and atmospheric and ocean forecast datasets were collected, (ii) the increment R5 and predictors were calculated, (iii) the cluster to which the predicted TC track belongs was determined and the model of assigned cluster was performed, and (iv) the symmetric wind radii were calculated using the increment R5, the initial R5, and the current intensity. image
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页数:16
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