A Neural Network Method for Precipitation Estimation from Elektro-L No. 4/MSU-GS Spectroradiometer Measurements.
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| Title: | A Neural Network Method for Precipitation Estimation from Elektro-L No. 4/MSU-GS Spectroradiometer Measurements. |
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| Authors: | Andreev, A. I.1,2 (AUTHOR) a.andreev@dvrcpod.ru, Filei, A. A.1,2 (AUTHOR), Kuchma, M. O.1,2 (AUTHOR), Malkovsky, S. I.1 (AUTHOR) |
| Source: | Russian Meteorology & Hydrology. Oct2024, Vol. 49 Issue 10, p848-856. 9p. |
| Subject Terms: | *Artificial neural networks, *Precipitation (Chemistry), *Reference sources, *Information resources, *Spectroradiometer |
| Abstract: | The paper presents an algorithm for the precipitation intensity estimation from the data of the MSU-GS spectroradiometer on board the Elektro-L No. 4 satellite. The algorithm is based on two neural networks with transformer and convolutional architectures and aimed at detection of precipitation-forming clouds and quantification of precipitation, respectively. The Global Precipitation Measurements (GPM) Integrated Multi-SatellitE Retrievals (IMERG) product is used as a reference source of information about precipitation intensity for training and testing the neural network models. The presented algorithm takes into account spectral, textural, and microphysical properties of clouds and allows obtaining precipitation maps for both daytime and nighttime conditions. The test results have shown an RMSE value of 1.27 mm/hour and F1 score of about 0.59 for the warm and cold seasons. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 181924748 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Neural Network Method for Precipitation Estimation from Elektro-L No. 4/MSU-GS Spectroradiometer Measurements. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Andreev%2C+A%2E+I%2E%22">Andreev, A. I.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> a.andreev@dvrcpod.ru</i><br /><searchLink fieldCode="AR" term="%22Filei%2C+A%2E+A%2E%22">Filei, A. A.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kuchma%2C+M%2E+O%2E%22">Kuchma, M. O.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Malkovsky%2C+S%2E+I%2E%22">Malkovsky, S. I.</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Russian+Meteorology+%26+Hydrology%22">Russian Meteorology & Hydrology</searchLink>. Oct2024, Vol. 49 Issue 10, p848-856. 9p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Precipitation+%28Chemistry%29%22">Precipitation (Chemistry)</searchLink><br />*<searchLink fieldCode="DE" term="%22Reference+sources%22">Reference sources</searchLink><br />*<searchLink fieldCode="DE" term="%22Information+resources%22">Information resources</searchLink><br />*<searchLink fieldCode="DE" term="%22Spectroradiometer%22">Spectroradiometer</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The paper presents an algorithm for the precipitation intensity estimation from the data of the MSU-GS spectroradiometer on board the Elektro-L No. 4 satellite. The algorithm is based on two neural networks with transformer and convolutional architectures and aimed at detection of precipitation-forming clouds and quantification of precipitation, respectively. The Global Precipitation Measurements (GPM) Integrated Multi-SatellitE Retrievals (IMERG) product is used as a reference source of information about precipitation intensity for training and testing the neural network models. The presented algorithm takes into account spectral, textural, and microphysical properties of clouds and allows obtaining precipitation maps for both daytime and nighttime conditions. The test results have shown an RMSE value of 1.27 mm/hour and F1 score of about 0.59 for the warm and cold seasons. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=181924748 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3103/S1068373924100029 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 848 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Precipitation (Chemistry) Type: general – SubjectFull: Reference sources Type: general – SubjectFull: Information resources Type: general – SubjectFull: Spectroradiometer Type: general Titles: – TitleFull: A Neural Network Method for Precipitation Estimation from Elektro-L No. 4/MSU-GS Spectroradiometer Measurements. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Andreev, A. I. – PersonEntity: Name: NameFull: Filei, A. A. – PersonEntity: Name: NameFull: Kuchma, M. O. – PersonEntity: Name: NameFull: Malkovsky, S. I. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 10683739 Numbering: – Type: volume Value: 49 – Type: issue Value: 10 Titles: – TitleFull: Russian Meteorology & Hydrology Type: main |
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