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русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2025, V. 22, No. 3, pp. 53-63

Using neural network to retrieve cloud water path from MSU-GS radiometer measurements on board the Electro-L No. 4 satellite

A.A. Filei 1 , A.I. Andreev 1 
1 Far Eastern Center of SRC "Planeta", Khabarovsk, Russia
Accepted: 21.03.2025
DOI: 10.21046/2070-7401-2025-22-3-53-63
The paper presents a neural network method for the retrieval of cloud water path using MSU-GS radiometer daytime measurements on board the Electro-L No. 4 Russian hydrometeorological satellite. The method is based on physical principles of interaction of electromagnetic radiation with cloud particles in the MSU-GS radiometer channels at wavelengths of 0.6 and 4.0 μm. Using a fully connected feedforward neural network, a relationship is established between the measurements of cloud reflectivity in the satellite radiometer channels and its microphysical parameters: optical thickness and effective radius. When training the neural network, the role of a reference source of information was assigned to an MSU-GS measurements array simulated using the Libradtran radiation transfer model and the corresponding cloud water path values calculated based on optical thickness and effective radius of particles for droplet and crystal clouds. The neural network model obtained during training was used to estimate cloud water path on the basis of direct measurements of MSU-GS, which were then compared with similar estimates obtained by the classical algorithm for solving the inverse problem using statistical regularization method. According to the comparison results, the root-mean-square error of the cloud water path estimates did not exceed 44 g/m2. On average, the value of cloud water path of crystal cloudiness was overestimated by 12 g/m2, and that of droplet cloudiness underestimated by 4 g/m2. The obtained results allow us to conclude that the neural network algorithm is efficient and can be used in operational practice along with classical statistical algorithm, without being inferior in accuracy and winning in ease of implementation and calculation speed.
Keywords: MSU-GS, Electro-L No. 4, neural networks, cloud water path, optical thickness, effective radius, cloudiness
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