Application of Deep Neural Networks for Detecting Probable Areas of Precipitation and Thunderstorms

Author:

Chursin V. V.,Kostornaya A. A.

Publisher

Allerton Press

Reference16 articles.

1. M. V. Bukharov and D. V. Govorov, "Analysis of the Consistency between the Results of Automated Meteorological Interpretation of the Information from a Geostationary Satellite and the Weather Radar Data," Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa, No. 5, 1 (2008).

2. E. V. Volkova and A. V. Kukharsky, "The Automated Technology for Retrieving Cloud Cover Properties, Precipitation and Weather Hazards Based on the SEVIRI/Meteosat (MSG) Data for the European Part of Russia," Gidrometeorologicheskie Issledovaniya i Prognozy, No. 4 (2020).

3. Report on the Climate Features in the Russian Federation in 2020 (Roshydromet, Moscow, 2021) [in Russian].

4. I. L. Kaftannikov and A. V. Parasich, "Problems of Forming a Learning Sample in Machine Learning," Vestnik Yuzhno-Ural’skogo Gosudarstvennogo Universiteta, Ser. Komp’yuternye Tekhnologii, Upravlenie, Radioelektronika, No. 3, 16 (2016).

5. RD 52.27.284-91. Methodological Instructions. Performing Production (Operational) Tests of New and Improved Methods of Hydrometeorological and Heliographic Forecasting (Gidrometeoizdat, Leningrad, 1991) [in Russian].

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