Multi-scale Satellite Data Sensitivity Study on Cloud Analysis of Strong Typhoon

Liu Jian; Jiang Jianying

Vol.25, NO.1, 2014

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Liu Jian, Jiang Jianying. Multi-scale satellite data sensitivity study on cloud analysis of strong typhoon. J Appl Meteor Sci, 2014, 25(1): 1-10.

Citation:

Liu Jian, Jiang Jianying. Multi-scale satellite data sensitivity study on cloud analysis of strong typhoon. J Appl Meteor Sci, 2014, 25(1): 1-10.

Liu Jian, Jiang Jianying. Multi-scale satellite data sensitivity study on cloud analysis of strong typhoon. J Appl Meteor Sci, 2014, 25(1): 1-10.

Citation:

Liu Jian, Jiang Jianying. Multi-scale satellite data sensitivity study on cloud analysis of strong typhoon. J Appl Meteor Sci, 2014, 25(1): 1-10.

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Multi-scale Satellite Data Sensitivity Study on Cloud Analysis of Strong Typhoon

Liu Jian^,,
Jiang Jianying

National Satellite Meteorological Center, Beijing 100081

Received Date: 2012-10-02
Rev Recd Date: 2013-07-08
Publish Date: 2014-01-31

Abstract

Abstract

The traditional obervation interval of Fengyun geostationary meteorological series satellite is 1 hour for a single satellite. During flooding season, the obervation frequency is imporived to half an hour. Double satellite observation mode can provide remote sensing data every 15 minutes. Due to the difference of observation angles and calibration error between different satellites, remote sensing data sometimes appear consistency and uniformity deviation. So improving observation frequency for a single satellite is the best way to get high quality remote sensing data. Rapid scan mode of geostationary meteorological satellites is an important method to monitor all kinds of weather processes.National Satellite Meteorological Center uses retired FY-2C satellite to carry out high frequency rapid regional scan observation trials and get continuous data with an average of 10-minute interval. Based on high frequency observations, Hovmöller diagram and coefficient of variation are used to analyze the sensitivity of multi-scale satellite data on monitoring the structure of a strong typhoon Muifa (2011).The research results show that the high frequency observations can clearly demonstrate the evolution of a strong typhoon cloud structure. Each channel with different spatial and temporal resolution has different sensitiveness in monitoring the structure feature of cloud. Reflectivity at visible channel with 1.25-kilometer spatial resolution and 10-minute temporal resoution can well show features of typhoon cloud. Under the same observation temporal resolution condition, lowering spatial resolution has great impact on monitoring the structure of cloud. If the spatial resolution keeps the same, reduced observation temporal resolution has less effect on extracting the characteristics of clouds. Using Hovmöller diagram to compare cloud brightness temperature characteristics through infrared window channel under different temporal resolution, it can be seen that there is no great difference between 10-minute and 30-minute observation modes. The cloud features are greatly reduced after the observation interval being changed to 60 minutes. The results also show that the cloud characteristics change greatly during 60 minutes based on brightness temperature coefficient of variation difference. Because the brightness temperature coefficient of variation at water vapor channel is smaller than infrared channel, the evolution of cloud characters observed by infrared window channel is more sensitive than that by water vapor channel. So improved observation temporal resolution can get more cloud information through infrared window channel.
- resolution;
- satellite data;
- strong typhoon;
- cloud analysis

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References(15)

Figures and Tables

Fig. 1 The reflectivity image of typhoon Muifa (2011) of FY-2C visible channel during 0303 UTC—0450 UTC on 3 August 2011 with the spatial resolution of 1.25 km and the temporal resolution about 10 minutes

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Fig. 2 Reflectivity image of typhoon Haikui (2012) of FY-2F visible channel at 0100 UTC 7 Aug 2012 (a) typhoon image with the spatial resilution of 1.25 km, (b) typhoon image with the spatial resolution of 5 km, (c) typhoon eye image with the spatial resolution of 1.25 km

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Fig. 3 The reflectivity Hovmöller diagram of typhoon Muifa (2011) during 0303 UTC— 0703 UTC on 3 Aug 2011 with the spatial resolution of 5 km (a) and 1.25 km (b) and the temporal resolution about 10 minutes

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图 4 2011年8月3日04:10 1.25 km空间分辨率可见光通道反射率图像

(图中黑框为Hovmöller分析区范围)

Fig. 4 The reflectivity image of visible channel at 0410 UTC 3 Aug 2011 with the spatial resolution of 1.25 km

(black box in the image shows the analysis area used by Hovmöller diagram)

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Fig. 5 The reflectivity Hovmöller diagram of typhoon Muifa (2011) during 0303 UTC— 0703 UTC on 3 Aug 2011 with the spatial resolution of 1.25 km and the temporal resolution about 30 minutes (a) and 10 minutes (b)

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Fig. 6 Reflectivity profile around eye and convective wall of typhoon Muifa (2011) with different spatial resolutions at 0303 UTC 3 Aug 2011(a) and with different temperal resolutions during 0300 UTC—0900 UTC on 3 Aug 2011(b)

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Fig. 7 The brightness temperature Hovmöller diagram of typhoon Muifa (2011) of infrared window channel during 0303 UTC—1450 UTC on 3 Aug 2011 with spatial resolution of 5 km and the temporal resolutions about 10 minutes (a), 30 minutes (b) and 60 minutes (c)

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Fig. 8 The brightness temperature Hovmöller diagram of typhoon Muifa (2011) of water vapor channel during 0303 UTC—1450 UTC on 3 Aug 2011 with the spatial resolution of 5 km and the temporal resolution about 10 minutes (a), 30 minutes (b) and 60 minutes (c)

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Fig. 9 The change of eye area brightness temperature of typhoon Muifa (2011) and typhoon Haikui (2012) of infrared window channel and water vapor channel with different temporal resolutions

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Fig. 10 The distribution of C_V difference between different observation temporal resolutions as the function of latitude for infrared window channel (a) and water vapor channel (b)

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Table 1 The pixel percentage of different brightness temperature level between extrapolated and observed data of infrared window channel and water vapor channel

推算值与实测值差值分类	红外通道1		水汽通道
推算值与实测值差值分类	10 min	30 min	10 min	30 min
[0, 1)	58.7%	41.7%	65.2%	41.7%
[1, 2)	21.7%	8.3%	19.6%	33.3%
[2, 3)	10.9%	25.0%	10.9%	16.7%
[3, 4)	4.3%	16.7%	4.3%	8.3%
[4, 5)	2.1%	0.0%
[5, 6)	0.0%	8.3%
[6, 7)	2.1%

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