Cloud bottom height estimation methods for optical imaging terminal guidance

Shuai Liang; Meng Liu; Zhongyang Wang; Tianxu Zhang

doi:10.1117/12.2535716

14 February 2020 Cloud bottom height estimation methods for optical imaging terminal guidance

Shuai Liang, Meng Liu, Zhongyang Wang, Tianxu Zhang

Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 1143008 (2020) https://doi.org/10.1117/12.2535716
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

We use domestic and foreign meteorological satellite data to carry out the research of Operational Regional meteorology which can be used for optical imaging terminal guidances. Attacks on areas covered by clouds can be divided into the following two scenarios: 1. Clouds are medium-high clouds, because the cloud base height of this kind of cloud layer is relatively high, generally more than 2500 meters, it will not have much influence on the optical imaging terminal guidance; 2. With low cloud coverage but not completely covered, the cloud can be detected and segmented, avoiding the cloud to hit the target. We use machine learning algorithm training model to divide the cloud into multi-layer cloud and single layer cloud, and the classification accuracy reaches 82.1%. Then for single-layer clouds, there are two methods to estimate the cloud bottom height: 1. We can use the MODIS data of the Aqua meteorological satellite to identify clouds of different attributes for cloud height estimation. 2. The height of single layer clouds can be calculated directly by using the physical characteristics of clouds, the average calculation error is 16.5%.

Citation Download Citation

Shuai Liang, Meng Liu, Zhongyang Wang, and Tianxu Zhang "Cloud bottom height estimation methods for optical imaging terminal guidance", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 1143008 (14 February 2020); https://doi.org/10.1117/12.2535716

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KEYWORDS

Clouds

MODIS

Particles

Satellites

Optical imaging

Machine learning

Mass attenuation coefficient

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