基于CALIOP和MODIS的北极地区海雾检测研究  

Arctic sea fog detection using CALIOP and MODIS

作　　者：陈标 吴东[1,2] CHEN Biao;WU Dong(College of Information Science and Engineering,Ocean University of China,Qingdao 266100,China;Laboratory for Regional Oceanography and Numerical Modeling,Qingdao Laboratory for Marine Science and Technology,Qingdao 266200,China)

机构地区：[1]中国海洋大学信息科学与工程学院,山东青岛266100 [2]青岛海洋科学与技术试点国家实验室区域海洋动力学与数值模拟功能实验室,山东青岛266200

出　　处：《大气与环境光学学报》2022年第2期267-278,共12页Journal of Atmospheric and Environmental Optics

基　　金：国家自然科学基金,41376180。

摘　　要：极地地区的海雾给极地科考和海冰研究带来了挑战,但由于相关监测数据较少,因此对极地地区海雾的研究还相对匮乏。基于CALIOP传感器可以观测垂直方向上云雾信息的特性,使用准同步观测的MODIS中分辨率成像光谱仪对北极地区的云雾信息进行分析。首先使用深度神经网络模型反演云顶高度,再根据云高确定是否为海雾。并且就不同波段对反演结果的影响进行了分析。结果显示使用深度神经网络反演的云顶高度平均绝对误差要比传统方法的结果低1774.280 m,可以更好地对云顶高度进行反演,提高了海雾检测精度。Sea fog in polar regions poses a challenge to the research on polar science and sea ice. However, due to the lack of relevant cloud monitoring data in the polar region, the research on sea fog in polar regions is still relatively scare. Based on the CALIOP sensor′s ability to observe cloud information in the vertical direction, the MODIS medium resolution imaging spectrometer with plesiochronous observation is used to analyze cloud information in the Arctic region. Firstly, the deep neural network model is applied to invert the cloud top height. Then, according to the inverted cloud top height, whether it is sea fog can be ascertained. Furthermore, the influence of different wavebands on the inversion results is also analyzed. The results show that the average absolute error of the cloud top height inverted by the deep neural network is 1774.280 m lower than that of the traditional method, indicating that using deep neural network model can invert cloud top height better and more accurately, which can improve the detection accuracy of sea fog.

关 键 词：正交偏振云-气溶胶激光雷达 中分辨率成像光谱仪 海雾检测 云顶高度 深度学习 

分 类 号：P407.5[天文地球—大气科学及气象学]