ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 5, pp. 63-75

Using CloudSat CPR data to improve the efficiency of the neural network approach to estimating cloud base height in Aqua MODIS satellite images

A.V. Skorokhodov 1 , K.V. Kuryanovich 1 
1 V.E. Zuev Institute of Atmospheric Optics SB RAS, Tomsk, Russia
Accepted: 26.10.2022
DOI: 10.21046/2070-7401-2022-19-5-63-75
We propose a modified algorithm for estimating the cloud base height from passive satellite data. The synchronous results of CPR (CloudSat) and MODIS (Aqua) scans of the Earth’s surface are used, as well as the data products of their processing. The use of radar measurements makes it possible to determine quite reliably the base height of middle-level and convective clouds with optical thickness τ ≤ 30. The main idea of the performed transformations is the use of two independent Kohonen self-organizing neural networks. The first (the original version of the algorithm) is trained on CALIOP lidar data (CALIPSO), and the second (presented here) is based on information obtained by the CPR radar. Using two neural networks makes it possible to compensate for differences between lidar and radar imagery. We discuss the results of estimating the base height of single-layer clouds on MODIS images obtained over the territory of Western Siberia in the summer from May to September. It was found that the results achieved by the proposed algorithm to estimate the cloud base height with 10 < τ ≤ 30 satisfy the existing NOAA NESDIS requirements in this area in 71 % of cases.
Keywords: CALIOP, CPR, cloud base height, image processing, MODIS, neural network, satellite data
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