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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, 2023, Vol. 20, No. 6, pp. 35-48

Neural network approaches to precipitation nowcasting: A review and test of existing methods

M.O. Kuchma 1, 2 , S.I. Malkovsky 1 , A.I. Andreev 1, 2 , V.D. Bloshchinsky 1, 2 
1 Computing Center FEB RAS, Khabarovsk, Russia
2 Far Eastern Center of SRC "Planeta", Khabarovsk, Russia
Accepted: 18.10.2023
DOI: 10.21046/2070-7401-2023-20-6-35-48
In recent years, there has been a growing interest among researchers and meteorological agencies in different countries in the field of accurate current weather forecasting using ground-based and satellite observation systems. In this regard, this paper provides an overview of recent advances in the field of current weather forecasting. In particular, modern neural network nowcasting methods are considered to solve the problem of extrapolating a sequence of digital matrices containing information about precipitation rate from previous observation dates. In this case, special attention is paid to the possibility of forecasting precipitation where data from geostationary spacecraft is used as the main source of information, which is due to the large territorial coverage, especially for remote areas. Using the publicly available SEVIR dataset, which combines ground-based and satellite observations of precipitation and hazardous weather events, the authors tested selected neural network architectures to solve the nowcasting problem. Conclusions are drawn about the possibility of using this neural network models for current weather forecasting in the Far Eastern region of Russia.
Keywords: nowcasting, current weather forecast, precipitation, artificial intelligence, machine learning, neural networks, hydrometeorology
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