Adjustment of Precipitation Restoration Algorithm According to MTVZA-GYa No. 2-2 Measurements

Cover Page

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

This paper presents an adjusted algorithm for restoring precipitation intensity over the ocean surface based on MTVZA-GYa No. 2-2 data. Based on the studies carried out on georeferencing data and convergence of the beams of the MTVZA-GYa antenna system, the weighting coefficients of the approximating functions for the scattering index and precipitation intensity were recalculated. A qualitative analysis of data for 2020 showed that precipitation intensity is restored adequately and correlate with measurements from other satellite instruments. Quantitative analysis showed that precipitation according to MTVZA-GYa data can be reconstructed over the entire range, however, only in the range up to 25 mm/h can reliable data be obtained with an accuracy of ~50%. In the precipitation range of more than 25 mm/h, there is not enough data for comparison and the statistics are unreliable. Based on the results of the qualitative and statistical comparison presented in the work, we can conclude that the accuracy of the precipitation intensity restoring based on the MTVZA-GYa instrument data is comparable to the accuracies for the AMSR-2 and SSMIS instruments.

Full Text

Restricted Access

About the authors

D. S. Sazonov

Space Research Institute of the Russian Academy of Sciences

Author for correspondence.
Email: sazonov_33m7@mail.ru
Russian Federation, Moscow

References

  1. Boldyrev V.V., Gorobets N.N., Il'gasov P.A., Nikitin O.V., Pantsov V.Yu., Prokhorov Yu.N., Strel'nikov N.I., Strel'tsov A.M., Chernyi I.V., Chernyavskii G.M., Yakovlev V.V. Satellite microwave scanner/sounder MTVZA-GY, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2008. Vol. 1. No 5. Pp. 243–248. (In Russian).
  2. Chernyavskii G.M., Mitnik L.M., Kuleshov V.P., Mitnik M.L., Chernyi I.V. Microwave sensing of the ocean, atmosphere and land surface from Meteor-M No. 2 data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2018. Vol. 15. No. 4. Pp. 78–100.
  3. Chinnawat Surussavadee, David H. Staelin, NPOESS Precipitation Retrievals Using the ATMS Passive Microwave Spectrometer, IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2010, VOL. 7, NO. 3, pp. 440–444. doi: 10.1109/LGRS.2009.2038614
  4. Ferraro R.R. Special sensor microwave imager derived global rainfall estimates for climatological applications // J. Geophys. Res. 1997. Vol. 102. NO. D14. Pp. 16,715–16,735.
  5. Huffman, G.J., E.F. Stocker, D.T. Bolvin, E.J. Nelkin, Jackson Tan (2019), GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [30.04.2022]. doi: 10.5067/GPM/IMERG/3B-HH/06
  6. Kummerow C.D., Randel D.L., Kulie M., Wang N.Y., Ferraro R., Munchak S.J., Petkovic V. The Evolution of the Goddard Profiling Algorithm to a Fully Parametric Scheme. JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY, 2015, Vol. 32, NO 12, Pp. 2265–2280. DOI: https://doi.org/10.1175/JTECH-D-15-0039.1
  7. Sazonov D.S. Algorithm for reconstructing ocean surface temperature, near-surface wind speed and integral vapor content from MTVZA-GY data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2022. Vol. 19. No 1. Pp. 50–64. doi: 10.21046/2070-7401-2022-19-1-50-64 (In Russian)
  8. Sazonov D.S. Study the possibility of precipitation intensity recovery from MTVZA-GY measurements, Issled. Zemli iz kosmosa. 2023. No. 5. Pp. 23–35. doi: 10.31857/S020596142305007X, EDN: XQPADE (in Russian)
  9. Sazonov D.S., Sadovskii I.N. Geographical reference adjustment of MTVZA-GY frequency channels, Issled. Zemli iz kosmosa. 2024 (in print)
  10. Zabolotskikh E. and Chapron B. Validation of the New Algorithm for Rain Rate Retrieval from AMSR2 Data Using TMI Rain Rate Product. Advances in Meteorology Volume 2015, Article ID 492603, 12 pages http://dx.doi.org/10.1155/2015/492603
  11. Zhang R., Wang Z., Hilburn K.A. Tropical Cyclone Rainfall Estimates from FY-3B MWRI Brightness Temperatures Using the WS Algorithm. Remote Sens. 2018, 10, 1770. doi: 10.3390/rs10111770

Supplementary files

Supplementary Files
Action
1. JATS XML
2. Fig. 1. SI scattering index (K) for ascending turns. Data for 06/20/20 (a) Before the correction of high-frequency channels, (b) after the correction.

Download (181KB)
3. Fig. 2. Relationship of the precipitation intensity obtained from the GPM IMERG reanalysis data with the SI scattering index according to the MTG data for ascending and descending windings. Statistics for all regions accumulated over 2020 in the time range of ± 1 minute. The scale indicates the number of measurements that fall within the range of ΔSI = 0.5 K and ΔI = 0.5 mm/h.

Download (325KB)
4. Fig. 3. (a) The dependence of precipitation intensity on the SI scattering index and its approximation by the function (3). (b) The standard deviation of the reconstructed precipitation intensity from the reanalysis.

Download (294KB)
5. Fig. 4. Restored precipitation intensity according to the data of MTVZA-GYa No. 2-2. Data for July 21, 2020

Download (610KB)
6. Fig. 5. Comparison of precipitation intensity, restored according to the MTVZA-GYa data, with precipitation according to AMSR-2 and SSMIS. Data for July 21, 2020

Download (472KB)

Copyright (c) 2024 Russian Academy of Sciences