The Annual Frequency Prediction of Tropical Cyclones Affecting China

Ying Ming; Wan Rijin

Vol.22, NO.1, 2011

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Ying Ming, Wan Rijin. The annual frequency prediction of tropical cyclones affecting China. J Appl Meteor Sci, 2011, 22(1): 66-76.

Citation:

Ying Ming, Wan Rijin. The annual frequency prediction of tropical cyclones affecting China. J Appl Meteor Sci, 2011, 22(1): 66-76.

Ying Ming, Wan Rijin. The annual frequency prediction of tropical cyclones affecting China. J Appl Meteor Sci, 2011, 22(1): 66-76.

Citation:

Ying Ming, Wan Rijin. The annual frequency prediction of tropical cyclones affecting China. J Appl Meteor Sci, 2011, 22(1): 66-76.

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The Annual Frequency Prediction of Tropical Cyclones Affecting China

Ying Ming^,,
Wan Rijin

Laboratory of Typhoon Forecast Technique, Shanghai Typhoon Institute of China Meteorological Administration, Shanghai 200030

Received Date: 2010-01-19
Rev Recd Date: 2010-11-24
Publish Date: 2011-02-28

Abstract

Abstract

Seasonal prediction schemes are developed for the annual frequency of tropical cyclones (TCs) affecting China, which are more practicable than predicting the genesis frequency for disaster mitigation. The frequencies of TCs affecting the whole China, East China and South China are identified by using the China Meteorological Administration TC-induced wind and precipitation data under one of the three criteria: The storm precipitation heavier than 50 mm has been observed at more than one station in the region; the sustained wind severer than Beaufort scale 7 or wind gusts larger than Beaufort scale 8 has been observed at more than one station in the region; the storm precipitation heavier than 30 mm, and either the sustained wind severer than Beaufort scale 6 or wind gusts larger than Beaufort scale 7 has been observed at more than one station in the region. Seasonal prediction schemes are then developed for these TC frequencies (TCFs) according to their lag correlations with the sea surface temperature (SST) and atmospheric variables during the period of 1961—2000. The NOAA ER SST and the NCEP/NCAR reanalysis data, including sea level pressure, geopotential height at 200, 500 hPa and 850 hPa, and both the zonal and meridional components of wind vectors at 200, 500 hPa and 850 hPa, are used to derive the predictors. For better representing the variation of the circulation systems in three dimensions, the predictor series are constructed by averaging data within those adjoining significant areas of correlation at various levels in each month. For the frequencies of TCs affecting the South and East China, respectively, analyses on the predictors suggest that their predictors of previous autumn and winter are quite consistent with each other; however, their predictors of previous spring show more differences. For each model, the colinearity among the predictors, including data since 2001, is reduced by applying the Principal Component Analysis approach, and the optimal subset regression model is then developed based on those derived independent predictors. All prediction schemes for TCFs are validated using the data of 2001—2008 and the results indicate that all schemes show skills in predicting frequencies of TCs affecting China though they still can be further improved.
- influence of tropical cyclones;
- annual frequency;
- seasonal prediction

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References(47)

Figures and Tables

Fig. 1 Distribution of the 676 stations with observations more than 50 years in China Meteorological Adiministration tropical cyclone servere wind and precipitation dataset (a) and the annual number of stations (b)

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Fig. 2 Frequency of TCs (lines with markers) from 1949 to 2007 affecting China (TCFC), East China (TCFEC), and South China (TCFSC)(thick lines) with corresponding means during 1961—2000 (lines with markers), repectively, and the frequency of TCs (bars) offecting both the East China and South China

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Fig. 3 The mean geopotential heights of previous December from 1961 to 2000(contours, unit:gpm) and their lead correlations with the TCFEC (shaded with solid line means positive and that with dashed line means negative, only the correlations with confidence of 95.0%, 99.0% and 99.9% are plotted) at 200 hPa (a), 500 hPa (b) and 850 hPa (c)

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Fig. 4 Same as in Fig. 3, but for the geopotential heights of previous February (contours, unit:gpm) and their lead correlations with TCFSC (shaded)

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Fig. 5 Same as in Fig. 3, but for the SSTs (contours, unit:℃) in previous March (a), April (b) and May (c), and their leading correlations with TCFC (shaded)

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Fig. 6 The frequency of TCs affecting China and the trends of SST of the negative region over the mid-east tropical Pacific in previous April and Ma

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Fig. 7 The scheme of TC frequency prediction

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Table 1 The levels of collinearity among predictors or principal components for each TCF

项目	TCFC		TCFEC		TCFSC
项目	4月	6月	4月	6月	4月	6月
原始因子	822.49	388.56	3759.02	12897.80	944.50	244.06
主成分	5.53	18.15	4.07	8.50	5.13	23.80

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Table 2 Statistical test and hindcasts for TCF prediction models during 2001—2008

统计量	TCFC (平均值：14.2；标准差：3.7)		TCFEC (平均值：8.3；标准差：2.8)		TCFSC (平均值：10.3；标准差：2.9)
统计量	4月	6月	4月	6月	4月	6月
残差的标准差	1.6~2.2	1.4~1.9	1.5~1.9	1.2~1.5	1.1~1.6	0.9~1.7
残差正态性P	0.40~0.90	0.14~0.79	0.07~0.38	0.05~0.94	0.05~0.87	0.19~0.95
复相关系数R	0.81~0.92	0.86~0.92	0.72~0.85	0.85~0.92	0.88~0.93	0.84~0.96
解释方差	0.66~0.85	0.75~0.85	0.52~0.72	0.72~0.84	0.78~0.86	0.71~0.93
平均绝对误差	2.6	1.6	2.2	2.3	2.1	2.2
趋势一致率	5/8	6/8	4/8	4/8	5/8	4/8

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