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Neural Network Algorithm for Precipitation Estimation from ATMS Radiometer Data.

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Title: Neural Network Algorithm for Precipitation Estimation from ATMS Radiometer Data.
Authors: Filei, A. A.1 (AUTHOR) andreyvm-61@mail.ru, Andreev, A. I.1 (AUTHOR)
Source: Izvestiya, Atmospheric & Oceanic Physics. Dec2024, Vol. 60 Issue 12, p1443-1457. 15p.
Subject Terms: *Precipitation (Chemistry), *Artificial intelligence, *Microwave measurements, *Image processing, *Reference sources
Abstract: Abstract—This paper presents a neural network method for precipitation estimation using microwave measurements from ATMS radiometer on board Suomi NPP and NOAA-20/21 satellites. The algorithms are based on two fully connected neural networks: the first one is used to detect precipitation clouds and the other one is used to quantify precipitation rate. When training the neural networks, the reference source of information is an array of measurements simulated using the fast radiation transfer model RTTOV in the bands of ATMS instrument and the corresponding precipitation rates taken from ECMWF ERA5 reanalysis data. The validation of the precipitation estimates is carried out using the results of the MIRS and GPROF algorithms for satellite radiometer ATMS, as well as ground-based radar observations from NIMROD. The results of the validation show a high accuracy level consistent with many others works in this research field. Validation is carried out for land and water surface separately. The comparison with the MIRS algorithm shows that the correlation coefficient is more than 0.9, and the RMSE error is approximately 0.78 mm/h for water and 0.84 mm/h for land surface. The same metrics for the GPROF algorithm show that the correlation coefficient is ~0.8 and the RMSE error is approximately 1.27 and 0.9 mm/h for water and land surface, respectively. When compared with ground-based NIMROD radar data, the correlation and the RMSE are 0.47 and 1.37 mm/h, respectively. The results of the validation confirm the performance of the neural network method for precipitation estimation. In addition, further minor refinement of the presented algorithm will make it possible to apply it to measurements of other microwave satellite instruments, including Russian ones, such as MTVZA-GY, installed on Meteor-M satellites. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
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An: 185940782
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Neural Network Algorithm for Precipitation Estimation from ATMS Radiometer Data.
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  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)
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  Data: <searchLink fieldCode="JN" term="%22Izvestiya%2C+Atmospheric+%26+Oceanic+Physics%22">Izvestiya, Atmospheric & Oceanic Physics</searchLink>. Dec2024, Vol. 60 Issue 12, p1443-1457. 15p.
– Name: Subject
  Label: Subject Terms
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  Data: *<searchLink fieldCode="DE" term="%22Precipitation+%28Chemistry%29%22">Precipitation (Chemistry)</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Microwave+measurements%22">Microwave measurements</searchLink><br />*<searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br />*<searchLink fieldCode="DE" term="%22Reference+sources%22">Reference sources</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Abstract—This paper presents a neural network method for precipitation estimation using microwave measurements from ATMS radiometer on board Suomi NPP and NOAA-20/21 satellites. The algorithms are based on two fully connected neural networks: the first one is used to detect precipitation clouds and the other one is used to quantify precipitation rate. When training the neural networks, the reference source of information is an array of measurements simulated using the fast radiation transfer model RTTOV in the bands of ATMS instrument and the corresponding precipitation rates taken from ECMWF ERA5 reanalysis data. The validation of the precipitation estimates is carried out using the results of the MIRS and GPROF algorithms for satellite radiometer ATMS, as well as ground-based radar observations from NIMROD. The results of the validation show a high accuracy level consistent with many others works in this research field. Validation is carried out for land and water surface separately. The comparison with the MIRS algorithm shows that the correlation coefficient is more than 0.9, and the RMSE error is approximately 0.78 mm/h for water and 0.84 mm/h for land surface. The same metrics for the GPROF algorithm show that the correlation coefficient is ~0.8 and the RMSE error is approximately 1.27 and 0.9 mm/h for water and land surface, respectively. When compared with ground-based NIMROD radar data, the correlation and the RMSE are 0.47 and 1.37 mm/h, respectively. The results of the validation confirm the performance of the neural network method for precipitation estimation. In addition, further minor refinement of the presented algorithm will make it possible to apply it to measurements of other microwave satellite instruments, including Russian ones, such as MTVZA-GY, installed on Meteor-M satellites. [ABSTRACT FROM AUTHOR]
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      – Type: doi
        Value: 10.1134/S0001433825700112
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 15
        StartPage: 1443
    Subjects:
      – SubjectFull: Precipitation (Chemistry)
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Microwave measurements
        Type: general
      – SubjectFull: Image processing
        Type: general
      – SubjectFull: Reference sources
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      – TitleFull: Neural Network Algorithm for Precipitation Estimation from ATMS Radiometer Data.
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              M: 12
              Text: Dec2024
              Type: published
              Y: 2024
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