Detection and Analysis on Deep Convective Clouds in a Frontal Cyclone Using Multispectral Remote Sensing Data

Zhu Yaping; Cheng Zhoujie; Liu Jianwen

Vol.20, NO.4, 2009

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2009 > 20(4): 428-436

Zhu Yaping, Cheng Zhoujie, Liu Jianwen. Detection and analysis on deep convective clouds in a frontal cyclone using multispectral remote sensing data. J Appl Meteor Sci, 2009, 20(4): 428-436.

Citation:

Zhu Yaping, Cheng Zhoujie, Liu Jianwen. Detection and analysis on deep convective clouds in a frontal cyclone using multispectral remote sensing data. J Appl Meteor Sci, 2009, 20(4): 428-436.

Zhu Yaping, Cheng Zhoujie, Liu Jianwen. Detection and analysis on deep convective clouds in a frontal cyclone using multispectral remote sensing data. J Appl Meteor Sci, 2009, 20(4): 428-436.

Citation:

Zhu Yaping, Cheng Zhoujie, Liu Jianwen. Detection and analysis on deep convective clouds in a frontal cyclone using multispectral remote sensing data. J Appl Meteor Sci, 2009, 20(4): 428-436.

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Detection and Analysis on Deep Convective Clouds in a Frontal Cyclone Using Multispectral Remote Sensing Data

1.
Navy Marine Hydrometeorologic Center, Beijing 100073
2.
Institute of Aviation Meteorology, Beijing 100085

Received Date: 2008-04-11
Rev Recd Date: 2009-05-14
Publish Date: 2009-08-31

Abstract

Abstract

Detection and analysis on deep convective clouds in a frontal cyclone using NOAA-16/AMSU-B and GOES-9 data are investigated. A series of detection algorithms and discrimination are adopted, including the microwave brightness temperatures detection from the two window channels, water vapor channel microwave brightness differences identification based on the NOAA-16/AM SU-B data, infrared brightness thresholds detection of cloud top temperatures, the water vapor and infrared window temperature differences determination, and the classification of cumulonimbus clouds correlating with deep convective clouds applying with infrared/water vapor spectral features. These methods are validated by overlaying surface conventional data on the results and comparing them. The results show that microwave brightness temperatures from window channels can discriminate deep convective clouds effectively, while the brightness temperatures of 89 GHz are affected by surface features and the cold water surfaces are mistaken to convective clouds. The brightness temperatures of 150 GHz are just slightly influenced by surface characteristics, so the detection areas are coincident with those from water vapor channel microwave brightness differences identification, which can identify the deep convective clouds well and depend on the thresholds less. As to GOES-9, different infrared brightness thresholds bring about significant detection differences. Single thresholds are applicable to the local areas and the thresholds applicable to global regions should vary in spatial and temporal scales. The water vapor and infrared window temperature differences can detect convective regions well, while the determination areas are smaller. The stepwise cluster can identify cumulonimbus clouds correlating with deep convective clouds by means of infrared/water vapor spectral features, which can classify clouds objectively by combining image and pattern recognition. The detection areas are coincident with NOAA-16/AM SU-B detection areas, and the surface conventional data can validate the results, including hazards weather and cumulonimbus clouds.
- microwave remote sensing;
- optical remote sensing;
- deep convective cloud

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References(16)

Figures and Tables

图 1 2004年6月16日06:25 GO ES-9红外通道 (10.7 μm) 卫星图像

Fig. 1 GOES-9 infrared (10.7 μm) image at 06:25 16 June 2004

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图 2 2004年6月16日06：29 NOAA16/AMSUB各通道微波亮温（单位：K）

（a）89GHz，（b）150GHz，（c）183.3±1GHz，（d）183.3±3GHz，（e）183.3±7GHz

Fig. 2 NOAA16/AMSUB microwave brightness temperature images at 06:29 16 June 2004 (unit: K)

(a)89GHz, (b)150GHz, (c)183.3±1GHz,(d)183.3±3GHz, (e)183.3±7GHz

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图 3 微波两窗区通道亮温阈值及3个水汽通道间亮温差识别结果

（a）T_B89＜240K，（b）T_B150＜220K，（c）ΔT₁₇≥0K，ΔT₁₃≥0K，ΔT₃₇≥0K

Fig. 3 Identification results based on NOAA16/AMSUB brightness temperatures and brightness temperature differences

(a) T_B89 < 240K, (b) T_B150 < 220K, (c) ΔT₁₇≥0K, ΔT₁₃≥0K, ΔT₃₇≥0K

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图 4 GOES-9不同亮温阈值、水汽和红外通道亮温差及多光谱逐个修改聚类识别结果 (a)233.15 K, (b)230.15 K, (c)215.15 K, (d)208.15 K, (e) T _B6.7-10.7 >0 K, (f) 红外/水汽多光谱云型识别结果

Fig. 4 Identification results based on GOES-9 brightness temperatures thresholds, brightness temperature differences and step-wise classification (a)233.15 K, (b)230.15 K, (c)215.15 K, (d)208.15 K, (e) T _B6.7-10.7 >0 K, (f) step-wise classification of IR and WV spectral features

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图 5 地面常规观测资料叠加结果

Fig. 5 Results matching conventional data

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