An Objective TC Intensity Estimation Method Based on Satellite Data

Lu Xiaoqin; Lei Xiaotu; Yu Hui; Zhao Bingke

Vol.25, NO.1, 2014

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2014 > 25(1): 52-58

Lu Xiaoqin, Lei Xiaotu, Yu Hui, et al. An objective TC intensity estimation method based on satellite data. J Appl Meteor Sci, 2014, 25(1): 52-58.

Citation:

Lu Xiaoqin, Lei Xiaotu, Yu Hui, et al. An objective TC intensity estimation method based on satellite data. J Appl Meteor Sci, 2014, 25(1): 52-58.

Lu Xiaoqin, Lei Xiaotu, Yu Hui, et al. An objective TC intensity estimation method based on satellite data. J Appl Meteor Sci, 2014, 25(1): 52-58.

Citation:

Lu Xiaoqin, Lei Xiaotu, Yu Hui, et al. An objective TC intensity estimation method based on satellite data. J Appl Meteor Sci, 2014, 25(1): 52-58.

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An Objective TC Intensity Estimation Method Based on Satellite Data

Shanghai Typhoon Institute, CMA, Shanghai 200030

Received Date: 2013-04-24
Rev Recd Date: 2013-09-23
Publish Date: 2014-01-31

Abstract

Abstract

Researches prove that TC (tropical cyclone) intensity is mainly determined by the top cloud convection strength, distribution and size. Then how to extract this information from TC cloud image is very important for TC intensity estimation. In 1988, Adler put forward a method named CST (convective-stratiform technique) to extract tropical convective cores from TC cloud image. Using MTSAT (multi-functional transport satellite) IR1 black body temperature data, the TC cloud top strong convection is extracted. Based on the convective cores number, convective core distance to TC center and convective core black body temperature extreme value, which are closely related to TC intensity, a TC intensity (expressed by V_max, the maximum sustained wind speed near surface TC center) estimation model is put forward using stepwise regress method. The experiment result shows that there is a linear correlation between their estimation error and their intensity for V_max >40 m·s^-1 and V_max < 18 m·s^-1 samples. So according to the estimation error distribution a linear revision is carried out.Statistical tests show this model is equivalent to Dvorak method and AMSU in TC intensity estimation accuracy. It's also reliable based on the relationship between the convective cores, convective cores distribution, brightness temperature and TC intensity. It could be used in all TC life span automatically and objectively, so it could be applied in the operation.Comparing with the advanced objective dvorak technique (AODT) and objective Dvorak technique (ODT), this algorithm gives accurate results in the Western North Pacific, but it's simpler with no complicated pattern types identifying process or other rules. A fixed radius of 135 km area is used as TC convective cores searching effective area in the model, but actually the maximum wind speed radius of the TC is variable, the TC size and the strongest convective area size are different for different TC in different stage. So using the fixed searching area may affect TC intensity estimation accuracy. The research on how to get the dynamical maximum wind speed radius would be carried out in the future.
- black body temperature data;
- convective core;
- TC intensity

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References(23)

Figures and Tables

图 1 热带气旋强度和估算误差绝对值的关系

(a) 全部样本, (b)V_max>40 m·s^-1，(c)V_max < 18 m·s^-1

Fig. 1 The relation between tropical cyclone intensity and the absolute errors of tropical cyclone intensity estimation

(a) all samples, (b)V_max>40 m·s^-1, (c)V_max < 18 m·s^-1

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Table 1 Correlation coefficients between convective core information and tropical cyclone intensity in different areas

对流核因子	100 km	135 km	200 km	300 km	400 km	500 km
C_LON	0.074	0.053	0.051	0.046	0.043	0.041
C_LAT	0.144	0.128	0.113	0.098	0.091	0.085
D_min	-0.095	-0.111	-0.133	-0.144	-0.133	-0.139
D_max	0.244	0.191	0.178	0.110	0.051	0.019
T_min	-0.241	-0.197	-0.172	-0.141	-0.123	-0.119
T_max	-0.433	-0.488	-0.468	-0.352	-0.222	-0.106
N	0.483	0.535	0.533	0.493	0.436	0.374
T_mean	-0.017	-0.334	-0.303	-0.243	-0.210	0.201
D_mean	0.047	-0.015	-0.146	-0.210	-0.227	-0.264
T_dif	-0.053	-0.268	-0.132	0.000	0.056	0.091
T_index	-0.271	-0.471	-0.456	-0.361	-0.282	-0.232
D_index	0.147	0.143	0.055	-0.023	-0.076	-0.115

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Table 2 Tropical cyclone intensity estimation errors

统计量	非独立样本	独立样本
平均绝对误差/(m·s^-1)	7.3	7.4
均方根误差/(m·s^-1)	9.2	9.6

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Table 3 The maximum sustained wind speed estimation errors in different intensity groups

样本类型	热带气旋强度分级	样本量	平均绝对误差/(m·s^-1)	均方根误差/(m·s^-1)
独立样本	热带低压	91	7.2	9.2
	热带风暴	118	5.7	7.3
	强热带风暴	76	5.3	6.4
	台风	72	5.7	7.0
	强台风	29	15.0	16.0
	超强台风	20	21.7	22.7
非独立样本	热带低压	574	7.0	8.7
	热带风暴	308	5.1	6.7
	强热带风暴	209	5.7	6.8
	台风	250	7.6	9.3
	强台风	115	13.3	14.5
	超强台风	37	18.6	19.3

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Table 4 The modified tropical cyclone intensity estimation errors

统计量	非独立样本	独立样本
平均绝对误差/(m·s^-1)	5.5	5.9
均方根误差/(m·s^-1)	6.9	7.7

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