Bibliographic Details
| Title: |
Retrieval of Total Precipitable Water from Meteor-M No. 2-2 MTVZA-GYa Data Using a Neural Network Algorithm. |
| 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 |
