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Performance Improvement for FY-2E Convection Monitoring Using a Spatial-response Matched Filter Method
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摘要: 建立空间响应匹配滤波 (spatial-response matched filter, SRMF) 方法，针对强对流低温研究目标，开展我国风云二号E星 (FY-2E) 红外亮温订正计算，并选取2013—2014年典型对流天气进行统计分析，从对流的空间分布、发展过程、云团结构等多角度进行方法性能评估。结果表明：对于对流云团结构，SRMF方法可改进FY-2E卫星红外波段对对流云团识别的准确度，减小高温背景对低温对流云团的邻近像元效应，增加了FY-2E卫星对中尺度对流内部小尺度精细化结构的揭示能力；对于对流空间分布，SRMF方法降低了对流判识空间分布统计误差，减少极短时间、极小范围强对流天气的漏判；对于对流识别时间响应，SRMF方法能够正确且提前显现出云团由弱对流向强对流的发展潜势，提高FY-2E卫星探测仪器对强对流天气的临近预警能力。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.
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图 1 2013年6月11日15:00 TBB(a) 和T′BB(b) 及ΔTBB(c)
Fig. 1 TBB distribution with difference at 1500 BT 11 Jun 2013
(a)TBB, (b)T′BB, (c)ΔTBB
图 2 2014年6月5日13:30 TBB(a) 和T′BB(b) 及ΔTBB(c) (图中红色实线为CloudSat卫星过境轨道)
Fig. 2 TBB distribution with difference at 1330 BT 5 Jun 2014
(the solid red line denotes CloudSat satellite orbit) (a)TBB, (b)T′BB, ΔTBB
图 4 2014年6月5日14:00 TBB(a) 和T′BB(b)
Fig. 4 Distribution of TBB(a) and T′BB(b) at 1400 BT 5 Jun 2014
图 5 北京及周边地区2013年6月雷暴日数分布 (单位：d)
Fig. 5 The number of thunderstorm days surrounding Beijing in Jun 2013(unit:d)
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TBB, (b) 基于T′BB
Fig. 6 The deep convection frequency distribution surrounding Beijing in Jun 2013
(a) based on TBB, (b) based on T′BB
图 7 典型对流个例的亮温差直方图统计
(a) 全部数据，(b)TBB≤-32℃，(c)TBB≤-52℃
Fig. 7 Difference histogram statistics of TBB for all typical convection cases
(a) all TBB, (b)TBB ≤-32℃, (c)TBB≤-52℃
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)TBB, (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)TBB, (b)T′BB
表 1 SRMF方法订正前后对5次对流过程的强对流发展趋势统计
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 ΔTBB＜0的面积占总对流面积比例/% 全部数据 TBB≤-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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