Performance Improvement for FY-2E Convection Monitoring Using a Spatial-response Matched Filter Method

Wang Xin; Guo Qiang; Chen Yiyu

doi:10.11898/1001-7313.20160111

Vol.27, NO.1, 2016

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Article Navigation > Journal of Applied Meteorological Science > 2016 > 27(1): 102-111

Wang Xin, Guo Qiang, Chen Yiyu. Performance improvement for FY-2E convection monitoring using a spatial-response matched filter method. J Appl Meteor Sci, 2016, 27(1): 102-111. DOI: 10.11898/1001-7313.20160111.

Citation:

Wang Xin, Guo Qiang, Chen Yiyu. Performance improvement for FY-2E convection monitoring using a spatial-response matched filter method. J Appl Meteor Sci, 2016, 27(1): 102-111. DOI: 10.11898/1001-7313.20160111.

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Performance Improvement for FY-2E Convection Monitoring Using a Spatial-response Matched Filter Method

DOI: 10.11898/1001-7313.20160111

Wang Xin¹,
Guo Qiang^{1
,
,},
Chen Yiyu²

1.
National Satellite Meteorological Center, Beijing 100081
2.
Chinese Academy of Meteorological Sciences, Beijing 100081

Received Date: 2015-07-01
Rev Recd Date: 2015-11-26
Publish Date: 2016-01-31

Abstract

Abstract

In China, severe convective weather system often causes sudden disasters, and its occurrence time and falling area is difficult to be forecasted. The improvement of convection monitoring and forecasting closely depends on the advance of the monitoring capability. Geostationary satellites can provide a large range, full day cloud information, so they may be the most practical tools for monitoring the convection. In these years, the convective cloud identification method is mainly based on infrared channels of satellites using brightness temperature threshold. The accuracy of the brightness temperature is crucial for convective cloud identification, which depends on not only the satellite calibration, but also the satellite instruments sensitivity, especially for the mesoscale and small-scale targets. Based on the observation performance and principle of FY-2E meteorological satellite, a spatial-response matched filter (SRMF) method for FY-2E is set up and applied for convection observations in the thermal infrared band, the MTSAT/JAMI is used as the reference standard of the revised method, and the spatial-response of VISSR and JAMI is matched with the same infrared band. Accordingly, the infrared channel brightness temperature is corrected. Furthermore, some typical convection examples are selected during summer of 2013 and 2014 for statistics, the SRMF performance is evaluated focusing on convection spatial distribution, development process and convective cloud inner structure.Results indicate recovered images after SRMF processing show more sensitivity of convective cloud identification. For small-scale convective core in the mesoscale cloud and very short time convection, it has significant improvement for reducing effect from the high temperature background smoothing, and also it enhances the ability to reveal the mesoscale and finer scales cloud. Besides, after SRMF processing, the convection distribution statistical error is reduced, improving the missing problem for the short time and small scope convective cloud. The SRMF method also enhances the characterization ability for the convection development potential. These results all indicate that the SRMF is more suitable for convection nowcasting, such a progress is believed to be beneficial to convection monitoring and forecasting. In the future, monitoring operational work on the convective weather, for small-scale and mesoscale clouds, this SRMF technique could be applied to reduce the overall observation error, and then improve the spatial resolution for the deep convective cloud top identification. The method can also be extended to the detail inner structure of tropical cyclones.
- satellite spatial-response correction;
- infrared brightness temperature;
- severe convection

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References(18)

Figures and Tables

图 1 2013年6月11日15:00 T_BB(a) 和T′_BB(b) 及ΔT_BB(c)

Fig. 1 T_BB distribution with difference at 1500 BT 11 Jun 2013

(a)T_BB, (b)T′_BB, (c)ΔT_BB

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图 2 2014年6月5日13:30 T_BB(a) 和T′_BB(b) 及ΔT_BB(c) (图中红色实线为CloudSat卫星过境轨道)

Fig. 2 T_BB distribution with difference at 1330 BT 5 Jun 2014

(the solid red line denotes CloudSat satellite orbit) (a)T_BB, (b)T′_BB, ΔT_BB

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Fig. 3 Vertical section of CloudSat radar reflectivity corresponding Fig. 2 at 1335 BT 5 Jun 2014

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Fig. 4 Distribution of T_BB(a) and T′_BB(b) at 1400 BT 5 Jun 2014

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Fig. 5 The number of thunderstorm days surrounding Beijing in Jun 2013(unit:d)

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图 6 2013年6月北京及周边地区深对流活动频率的空间分布

(a) 基于T_BB, (b) 基于T′_BB

Fig. 6 The deep convection frequency distribution surrounding Beijing in Jun 2013

(a) based on T_BB, (b) based on T′_BB

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图 7 典型对流个例的亮温差直方图统计

(a) 全部数据，(b)T_BB≤-32℃，(c)T_BB≤-52℃

Fig. 7 Difference histogram statistics of T_BB for all typical convection cases

(a) all T_BB, (b)T_BB ≤-32℃, (c)T_BB≤-52℃

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图 8 2013年6月11日01：00—20：00北京强对流过程的Hovmöller时序分析

(沿40.5°N的经度-时间剖面) (a)T_BB, (b)T′_BB

Fig. 8 Hovmöller analysis during the convective processing from 0100 BT to 2000 BT on 11 June 2013

(along 40.5°N longitude-time cross section) (a)T_BB, (b)T′_BB

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Table 1 Nowcasting performance statistics using SRMF method for five typical convection cases

序号	对流统计时段	发生区域	ΔT_BB＜0的面积占总对流面积比例/%
序号	对流统计时段	发生区域	全部数据	T_BB≤-32℃
1	2013年6月4日08:00—24:00	北京西北部	74	80
2	2013年6月11日12:00—20:00	北京北部、城区	57	60
3	2013年6月30日18:00—24:00	北京北部	66	71
4	2014年6月15日12:00—15:00	北京中部、东南部	59	68
5	2014年6月17日13:00—24:00	北京、河北中部	50	78

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