基于FY-4A的一种自适应阈值对流云提取算法

An adaptive thresholding algorithm for convective cloud recognition based on FY-4A satellite

以台风“利奇马”带来的强对流天气过程为例,利用FY-4A气象卫星先进静止轨道辐射成像仪的水汽与红外窗区通道亮温数据,对照全国气象雷达组合反射率拼图数据中反射率因子大于35 dBZ的对流云区域,设计了一种自适应阈值对流云提取算法。结果表明:(1)FY-4A气象卫星使用水汽—红外窗区亮温差法提取对流云,其最佳阈值为-2 K,使用该阈值可最大程度地减少卷云噪声干扰,同时可最大化提取对流云的准确率与识别率。(2)该算法通过控制水汽—红外窗区亮温差大于-2 K区域所占云团面积比使其大于80%提取较为完整的对流云,且可进一步过滤卷云等噪声。自适应阈值对流云提取算法原理清晰,操作简单,具有良好的拓展性,可为强对流天气的短临预报等研究提供参考。

Based on the water vapor channel and infrared window channel of FY-4A advanced geosynchronous radiation imager,compared with the convective cloud regions with reflectivity factor greater than 35 dBZ in the national weather radar combined reflectivity mosaic data,an adaptive thresholding algorithm for convective cloud recognition by taking the severe convective weather caused by typhoon“Lekima”as an example was designed.Results show that:(1)-2 K is the best threshold for convective cloud recognition by using the brightness temperature difference between water vapor and infrared window channel of FY-4A meteorological satellite,which can reduce the noise interference of cirrus cloud and maximize the accuracy and recognition rate of convective cloud recognition.(2)The algorithm can extract relatively complete convective clouds by controlling the area ratio of the cloud cluster in the region where the brightness temperature difference between water vapor and infrared window is greater than-2 K to make it greater than 80%,a nd can further filter cirrus and other noises.The algorithm has clear principle,simple operation and good expansibility,which can provide reference for short-term and impending forecast on severe convective weather.