Standard

Development of precipitation nowcasting method using geostationary satellite data. / Andreev , A. I.; Pererva, N. I.; Kuchma, M. O.

в: Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa, Том 17, № 6, 2020, стр. 18-22.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Andreev , AI, Pererva, NI & Kuchma, MO 2020, 'Development of precipitation nowcasting method using geostationary satellite data', Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa, Том. 17, № 6, стр. 18-22. https://doi.org/10.21046/2070-7401-2020-17-6-18-22

APA

Andreev , A. I., Pererva, N. I., & Kuchma, M. O. (2020). Development of precipitation nowcasting method using geostationary satellite data. Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa, 17(6), 18-22. https://doi.org/10.21046/2070-7401-2020-17-6-18-22

Vancouver

Andreev AI, Pererva NI, Kuchma MO. Development of precipitation nowcasting method using geostationary satellite data. Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa. 2020;17(6):18-22. https://doi.org/10.21046/2070-7401-2020-17-6-18-22

Author

Andreev , A. I. ; Pererva, N. I. ; Kuchma, M. O. / Development of precipitation nowcasting method using geostationary satellite data. в: Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa. 2020 ; Том 17, № 6. стр. 18-22.

BibTeX

@article{0eada423d6944360941f1eb283e87f39,
title = "Development of precipitation nowcasting method using geostationary satellite data",
abstract = "The paper considers the development of a model for precipitation field nowcasting using the data obtained from the Himawari-8 satellite and a GFS numerical forecast model. The nowcasting method employs a convolutional and recurrent neural network architecture. A peculiarity of the developed model is a possibility to make a forecast using no ground-based meteorological radars data. The authors present preliminary research results as exemplified by the precipitation field nowcasting for a 30-minute period and the 60-minute forecast of the cloud cover optical depth distribution. Finally, the paper outlines the areas for further research with the account to the identified drawbacks of the existing forecasting algorithm software implementation.",
author = "Andreev, {A. I.} and Pererva, {N. I.} and Kuchma, {M. O.}",
note = "Funding Information: In the process of the nowcasting model development the data of CCU “FEB RAS Data Center” (Sorokin et al., 2017). The computations were conducted by the methods and technologies developed with the funding of the Russian Foundation for Basic Research (RFBR) in the framework of a scientific project No. 18-29-03196. Publisher Copyright: {\textcopyright} 2020 Space Research Institute of the Russian Academy of Sciences. All rights reserved.",
year = "2020",
doi = "10.21046/2070-7401-2020-17-6-18-22",
language = "русский",
volume = "17",
pages = "18--22",
journal = "СОВРЕМЕННЫЕ ПРОБЛЕМЫ ДИСТАНЦИОННОГО ЗОНДИРОВАНИЯ ЗЕМЛИ ИЗ КОСМОСА",
issn = "2070-7401",
publisher = "Институт космических исследований Российской академии наук",
number = "6",

}

RIS

TY - JOUR

T1 - Development of precipitation nowcasting method using geostationary satellite data

AU - Andreev , A. I.

AU - Pererva, N. I.

AU - Kuchma, M. O.

N1 - Funding Information: In the process of the nowcasting model development the data of CCU “FEB RAS Data Center” (Sorokin et al., 2017). The computations were conducted by the methods and technologies developed with the funding of the Russian Foundation for Basic Research (RFBR) in the framework of a scientific project No. 18-29-03196. Publisher Copyright: © 2020 Space Research Institute of the Russian Academy of Sciences. All rights reserved.

PY - 2020

Y1 - 2020

N2 - The paper considers the development of a model for precipitation field nowcasting using the data obtained from the Himawari-8 satellite and a GFS numerical forecast model. The nowcasting method employs a convolutional and recurrent neural network architecture. A peculiarity of the developed model is a possibility to make a forecast using no ground-based meteorological radars data. The authors present preliminary research results as exemplified by the precipitation field nowcasting for a 30-minute period and the 60-minute forecast of the cloud cover optical depth distribution. Finally, the paper outlines the areas for further research with the account to the identified drawbacks of the existing forecasting algorithm software implementation.

AB - The paper considers the development of a model for precipitation field nowcasting using the data obtained from the Himawari-8 satellite and a GFS numerical forecast model. The nowcasting method employs a convolutional and recurrent neural network architecture. A peculiarity of the developed model is a possibility to make a forecast using no ground-based meteorological radars data. The authors present preliminary research results as exemplified by the precipitation field nowcasting for a 30-minute period and the 60-minute forecast of the cloud cover optical depth distribution. Finally, the paper outlines the areas for further research with the account to the identified drawbacks of the existing forecasting algorithm software implementation.

UR - http://www.scopus.com/inward/record.url?scp=85098722708&partnerID=8YFLogxK

U2 - 10.21046/2070-7401-2020-17-6-18-22

DO - 10.21046/2070-7401-2020-17-6-18-22

M3 - статья

AN - SCOPUS:85098722708

VL - 17

SP - 18

EP - 22

JO - СОВРЕМЕННЫЕ ПРОБЛЕМЫ ДИСТАНЦИОННОГО ЗОНДИРОВАНИЯ ЗЕМЛИ ИЗ КОСМОСА

JF - СОВРЕМЕННЫЕ ПРОБЛЕМЫ ДИСТАНЦИОННОГО ЗОНДИРОВАНИЯ ЗЕМЛИ ИЗ КОСМОСА

SN - 2070-7401

IS - 6

ER -

ID: 85146072