Retrieval of Total Precipitable Water from Meteor-M No. 2-2 MTVZA-GYa Data Using a Neural Network Algorithm.
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| Title: | Retrieval of Total Precipitable Water from Meteor-M No. 2-2 MTVZA-GYa Data Using a Neural Network Algorithm. |
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| Authors: | Filei, A. A.1 (AUTHOR) andreyvm-61@mail.ru, Andreev, A. I.1 (AUTHOR), Kuchma, M. O.1 (AUTHOR), Uspensky, A. B.2 (AUTHOR) |
| Source: | Russian Meteorology & Hydrology. Apr2022, Vol. 47 Issue 4, p272-280. 9p. |
| Subject Terms: | *Precipitable water, *Numerical weather forecasting, *Meteors, *Artificial neural networks, *Microwave radiometers, *Brightness temperature, *Algorithms |
| Abstract: | The paper presents the application of the artificial neural network algorithm for the retrieval of total precipitable water in the atmosphere over water and land from the measurements of MTVZA-GYa microwave radiometer on board the Meteor-M No. 2-2 satellite. Satellite-based estimates of total precipitable water were compared with radiosonde and AERONET data, as well as with the ECMWF numerical weather prediction model output. According to the comparison, the root-mean-square error (RMSE) does not exceed 4.5 mm for radiosonde data and is less than 4 mm for the ECMWF and AERONET data. The best accuracy is provided over water with the RMSE not exceeding 3 mm. The total precipitable water estimates retrieved from MTVZA-GYa and NOAA-20/ATMS radiometer data are consistent over water, while the MTVZA-GYa based estimates are more accurate over land. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 158179104 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Retrieval of Total Precipitable Water from Meteor-M No. 2-2 MTVZA-GYa Data Using a Neural Network Algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Filei%2C+A%2E+A%2E%22">Filei, A. A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> andreyvm-61@mail.ru</i><br /><searchLink fieldCode="AR" term="%22Andreev%2C+A%2E+I%2E%22">Andreev, A. I.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kuchma%2C+M%2E+O%2E%22">Kuchma, M. O.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Uspensky%2C+A%2E+B%2E%22">Uspensky, A. B.</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Russian+Meteorology+%26+Hydrology%22">Russian Meteorology & Hydrology</searchLink>. Apr2022, Vol. 47 Issue 4, p272-280. 9p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Precipitable+water%22">Precipitable water</searchLink><br />*<searchLink fieldCode="DE" term="%22Numerical+weather+forecasting%22">Numerical weather forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Meteors%22">Meteors</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Microwave+radiometers%22">Microwave radiometers</searchLink><br />*<searchLink fieldCode="DE" term="%22Brightness+temperature%22">Brightness temperature</searchLink><br />*<searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The paper presents the application of the artificial neural network algorithm for the retrieval of total precipitable water in the atmosphere over water and land from the measurements of MTVZA-GYa microwave radiometer on board the Meteor-M No. 2-2 satellite. Satellite-based estimates of total precipitable water were compared with radiosonde and AERONET data, as well as with the ECMWF numerical weather prediction model output. According to the comparison, the root-mean-square error (RMSE) does not exceed 4.5 mm for radiosonde data and is less than 4 mm for the ECMWF and AERONET data. The best accuracy is provided over water with the RMSE not exceeding 3 mm. The total precipitable water estimates retrieved from MTVZA-GYa and NOAA-20/ATMS radiometer data are consistent over water, while the MTVZA-GYa based estimates are more accurate over land. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=158179104 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3103/S1068373922040033 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 272 Subjects: – SubjectFull: Precipitable water Type: general – SubjectFull: Numerical weather forecasting Type: general – SubjectFull: Meteors Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Microwave radiometers Type: general – SubjectFull: Brightness temperature Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: Retrieval of Total Precipitable Water from Meteor-M No. 2-2 MTVZA-GYa Data Using a Neural Network Algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Filei, A. A. – PersonEntity: Name: NameFull: Andreev, A. I. – PersonEntity: Name: NameFull: Kuchma, M. O. – PersonEntity: Name: NameFull: Uspensky, A. B. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 10683739 Numbering: – Type: volume Value: 47 – Type: issue Value: 4 Titles: – TitleFull: Russian Meteorology & Hydrology Type: main |
| ResultId | 1 |
