Neural network algorithm for precipitation estimation from atms radiometer data

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Abstract

The 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 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 was an array of measurements simulated using the fast radiation transfer model RTTOV in the bands of ATMS instrument and the corresponding precipitation rates were taken from ECMWF ERA5 reanalysis data. Validation of the obtained precipitation estimates was 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 showed a high accuracy level consistent with many others works in this research field. The validation was carried out for land and water surface separately. The comparison with MIRS algorithm showed the correlation coefficient was more 0.9, and the RMSE error was approximately 0.78 mm/h for water and 0.84 mm/h for land surface. The same metrics for GPROF algorithm showed the correlation coefficient was ~0.8, and the RMSE error was approximately 1.27 mm/h and 0.9 for water and land surface, respectively. When compared with ground-based NIMROD radar data, the correlation and the RMSE were 0.47 and 1.37 mm/h, respectively. The results of the validation confirm the performance of the presented 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.

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About the authors

A. A. Filei

Far-Eastern Center of State Research Center for Space Hydrometeorology “Planeta”

Author for correspondence.
Email: andreyvm-61@mail.ru
Russian Federation, Khabarovsk

A. I. Andreev

Far-Eastern Center of State Research Center for Space Hydrometeorology “Planeta”

Email: andreyvm-61@mail.ru
Russian Federation, Khabarovsk

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Supplementary files

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2. Fig. 1. Weighting functions of the spectral channels of the ATMS radiometer.

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3. Fig. 2. General diagram of the process of modeling measurements in the channels of the ATMS device.

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4. Fig. 3. Distribution of precipitation intensity in the training sample.

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5. Fig. 4. Distribution of precipitation intensity in the training sample by precipitation intensity classes: a ‒ light; b ‒ moderate; c ‒ heavy; d ‒ very heavy.

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6. Fig. 5. Scatter plots of precipitation intensity values ​​from MIRS and APNA data for water (a) and for land (b).

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7. Fig. 6. Precipitation intensity according to MIRS (a) and APNA (b) data.

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8. Fig. 7. Scatter plots of precipitation intensity values ​​from GPROF and APNA data for water (a) and for land (b).

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9. Fig. 8. Precipitation intensity according to GPROF (a) and APNA (b) data.

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10. Fig. 9. Scatter plot of precipitation intensity values ​​from NIMROD and APNA data.

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11. Fig. 10. Precipitation intensity according to NIMROD (a) and APNA (b) data.

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