利用FY-2E红外和水汽波段对强对流云团的识别和演变研究

使用FY-2E静止气象卫星的红外1(10.3~11.3μm)和水汽波段(6.3~7.6μm)时序图像,对强对流云进行识别和短时预测。亮温阈值法是将强对流云和其他高云区分开的常用方法,但是合适的亮温阈值是随着时间和空间而变化的,过高的阈值会将许多卷云包括进来,太低的阈值会排除掉云顶发展还不是很高的强对流云。水汽波段所在的位置是水汽的一个强吸收带,而高度在400 h Pa上下的大气层是水汽波段的一个强吸收层,大气在垂直方向上的对水汽波段辐射吸收的分布模式使得卫星接收到的水汽波段辐射主要来自于400 h Pa以上的大气中高层,而卫星接收到的红外波段辐射主要来自于大气中低层,两个波段间辐射来源的差异使得不同光学厚度的高云的辐射观测值在红外—水汽光谱空间中的分布具有明显差别,并且这种差异具有时空的稳定性。本文将一定范围内的云团的象元测值在红外—水汽光谱空间中的分布的拟合直线斜率作为强对流云识别的依据,结果表明相对于亮温阈值法,本文的识别方法不仅能够较好地区别卷云和强对流云,同时也能更有效地识别未达到旺盛阶段的对流云。在对强对流云进行识别后,根据相邻时间段的卫星图像,利用交叉相关法反演得到强对流云团顶部的位移矢量场,并根据后向轨迹法对强对流云团位置形状进行短时预测,预报结果在短时间内(0~1 h)较好,并且对面积较大的云团的预报效果要优于较小的对流云团。此外文中还利用逐半小时的云顶黑体温度(Temperature of Black Body,TBB)资料分析了云顶亮温的分布变化,得到了整个强对流过程的演变特征。

Study on the detection and evolution of intense convective cloud with data from the FY-2E VISSR infrared and water vapor bands

In this study,the time sequential images of the FY-2E VISSR(Visible and Infrared Spin Scan Radiometer) IR(Infrared) and WV(Water Vapor) bands are used to recognize and short-time forecast deep convective clouds.In many researches regarding the distinguishing of deep convective clouds from cirrus clouds,the BT (Brightness Temperature) threshold technique is a frequently applied method,of which the defect lies in its variance with time and space,rendering it difficult to find a proper threshold to all weather conditions.The fact that water vapor has strong absorption in the location of the WV band along with the vertical distribution of water vapor in the atmosphere makes it difficult for satellites to receive the radiation emitted at the WV band by clouds under the height of around 400 hPa and at ground,while the satellite detected energy of the IR band mainly originates from the middle-low level of atmosphere.With the aid of disparity in the radiation source,the increase in optical depth of high-level clouds leads to a gradual change in the distribution pattern of pixels of satellite images in IR-WV spectral space,which is invulnerable to time and space,in contrast to the BT threshold technique.In the present study,pixels are identified as deep convective clouds if the fitted slope of their IR and WV BT is greater than a given threshold.The backward trajectory method is used to predict the location and shape of the detected cloud in future hours.The motion vector field of the target area is retrieved using the cross-correlation method from two neighbouring images,with a time resolution of 30 minutes.The detection and forecast methods are applied to an MCC(Mesoscale Convective Complex) which occurred in southeastern China,and the evolution of the MCC during its entire lifecycle is obtained by the analysis of its cloud top TBB(Temperature of Black Body) distribution.The results show that the detection algorithm in this article,compared to other methods using IR data only,functions more effectively in discriminating thin cirrus from intense convective clouds,as well as in detecting convective clouds with lower height.The forecast approach performs well in a short time range,and the results are more accurate for clouds with large spatial dimensions than small ones.