Abstract

Algorithms for classifying cloud images based on MODIS and VIIRS satellite data using artificial neural networks and fuzzy logic methods are considered. A combined classification of recognized cloud types is presented. The results of using the cloud classification for solving some problems of climatology and meteorology are analyzed. A statistical model of the image texture for various cloud types and physical parameters of clouds based on two-parameter distributions is proposed. A description of the algorithm for detecting weather fronts and determining their types from satellite data and the results of its testing for the territory of Western Siberia are presented. An approach to studying internal waves in the atmosphere based on the analysis of their cloud manifestation parameters is considered. The results are presented of studying the long-term variability for some of their parameters over the water area of the Kuril Islands. The methodology and results are discussed of studying the long-term variability of the structure of global cloud fields and their parameters over the natural zones of Western Siberia in summer during 2001 to 2019.

中文翻译：

---

利用基于卫星数据的云分类结果解决气候和气象问题

摘要

考虑使用人工神经网络和模糊逻辑方法基于 MODIS 和 VIIRS 卫星数据对云图像进行分类的算法。提出了公认的云类型的组合分类。分析了利用云分类解决气候学和气象学中一些问题的结果。提出了一种基于二参数分布的各种云类型和云物理参数的图像纹理统计模型。介绍了用于检测天气前沿并根据卫星数据确定其类型的算法及其在西西伯利亚领土上的测试结果。考虑了一种基于分析其云表现参数来研究大气中内波的方法。研究了千岛群岛水域某些参数的长期变化的结果。讨论了研究2001年至2019年夏季西西伯利亚自然区全球云场结构及其参数的长期变化的方法和结果。