Izvestiya, Atmospheric and Oceanic Physics

, Volume 52, Issue 1, pp 82–88 | Cite as

Neural network-based method for the estimation of the rain rate over oceans by measurements of the satellite radiometer AMSR2

Article

Abstract

The rain rate (RR) retrieval method for the RR estimation over ice-free areas of the ocean is presented. Measurements of the Japanese Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the satellite GCOM-W1 are used. The method is based on the results of the numerical modeling of brightness temperatures of the outgoing microwave radiation of the ocean–atmosphere system and their subsequent conversion into the RR using neural networks. A simplified form of the transfer equation is used. Its errors for the considered wavelengths do not exceed 1 K at an RR of less than 20 mm/h. The method is verified by comparison with the Tropical Rainfall Measuring Mission’s (TRMM) Microwave Instrument (TMI) RR product. As a result of the comparison, the rain rate retrieval error within the range of 20 mm/h is found to be 1 mm/h.

Keywords

rain rate numerical modeling brightness temperature AMSR2 TMI 

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References

  1. 1.
    A. Behrangi, M. Lersock, S. Wong, and B. Lambrigtsen, “On the quantification of oceanic rainfall using spaceborne sensors,” J. Geophys. Res. 117 (D20) (2012). doi 10.1029/2012.TD017979Google Scholar
  2. 2.
    T. T. Wilheit, “Some comments on passive microwave measurement of rain,” Bull. Am. Meteorol. Soc. 67 (10), 1226–1232 (1986).CrossRefGoogle Scholar
  3. 3.
    G. W. Petty and K. Li, “Improved passive microwave retrievals of rain rate over land and ocean. Part I: Algorithm description,” J. Atmos. Ocean. Technol. 30 (11), 2493–2508 (2013).CrossRefGoogle Scholar
  4. 4.
    R. J. Kuligowski, “A self-calibrating real-time GOES rainfall algorithm for short-term rainfall estimates,” J. Hydrometeorol. 3 (2), 112–130 (2002).CrossRefGoogle Scholar
  5. 5.
    C. Kummerow, W. Barnes, T. Kozu, J. Shine, et al., “The tropical rainfall measuring mission (TRMM) sensor package,” J. Atmos. Ocean. Technol. 15 (3), 809–817 (1998).CrossRefGoogle Scholar
  6. 6.
    A. Behrangi, G. Stephens, R. F. Adler, et al., “An update on the oceanic precipitation rate and its zonal distribution in light of advanced observations from space,” J. Clim. 2 (11), 3957–3965 (2014).CrossRefGoogle Scholar
  7. 7.
    R. J. Joyce, J. E. Janowiak, P. A. Arkin, and P. Xie, “CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution,” J. Hydrometeorol. 5 (3), 487–503 (2004).CrossRefGoogle Scholar
  8. 8.
    A. AghaKouchak, A. Mehran, H. Norouzi, and A. Behrangi, “Systematic and random error components in satellite precipitation data sets,” Geophys. Res. Lett. 39 (9) (2012). doi 10.1029/2012G.L051592Google Scholar
  9. 9.
    A. Y. Hou and R. K. Kakar, S. Neeck, et al., “The Global Precipitation Measurement (GPM) Mission,” Bull. Am. Meteorol. Soc. 95 (5), 711–722 (2014).Google Scholar
  10. 10.
    K. A. Hilburn and F. J. Wentz, “Intercalibrated passive microwave rain products from the unified microwave ocean retrieval algorithm (UMORA),” J. Appl. Meteorol. Climatol. 47 (3), 778–794 (2008).CrossRefGoogle Scholar
  11. 11.
    F. J. Turk, Z. S. Haddad, and Y. You, “Principal components of multifrequency microwave land surface emissivities. Part I: Estimation under clear and precipitating conditions,” J. Hydrometeorol. 15 (1), 3–19 (2014).CrossRefGoogle Scholar
  12. 12.
    C. Kummerow and L. Giglio, “A passive microwave technique for estimating rainfall and vertical structure information from space. Part I: Algorithm description,” J. Appl. Meteorol. 33 (1), 3–18 (1994).CrossRefGoogle Scholar
  13. 13.
    R. R. Ferraro, “Special sensor microwave imager derived global rainfall estimates for climatological applications,” J. Geophys. Res. 102 (D14), 16715–16735 (1997).CrossRefGoogle Scholar
  14. 14.
    P. Bauer and P. Schluessel, “Rainfall, total water, ice water, and water vapor over sea from polarized microwave simulations and Special Sensor Microwave/Imager data,” J. Geophys. Res. 98 (D11), 20737–20759 (1993).CrossRefGoogle Scholar
  15. 15.
    C. Kummerow and R. Ferraro, Algorithm Theoretical Basis Document: EOS/AMSR-E Level-2 Rainfall. Colorado State Univ. Rep., 2007.Google Scholar
  16. 16.
    B. Lin and W. B. Rossow, “Precipitation water path and rainfall rate estimates over oceans using special sensor microwave imager and International Satellite Cloud Climatology Project data,” J. Geophys. Res. 102 (D8), 9359–9374 (1997).CrossRefGoogle Scholar
  17. 17.
    S. Chandrasekhar, Radiative Transfer (Dover Publications, New York, 1960).Google Scholar
  18. 18.
    K. Imaoka, M. Kachi, M. Kasahara, et al., “Instrument performance and calibration of AMSR-E and AMSR2,” Int. Arch. Photogramm. Remote Sens. Spec. Inf. Sci 38 (8), 13–18 (2010).Google Scholar
  19. 19.
    H. J. Liebe and D. H. Layton, Millimeter-wave properties of the atmosphere: Laboratory studies and propagation modeling, NTIA Rep. 87–24. Nat. Tech. Inf. Service Boulder, Colorado, 1987.Google Scholar
  20. 20.
    S. Y. Matrosov and E. M. Shulgina, “Scattering and attenuation of microwave radiation by precipitation,” MGO Trans. 448, 85–94 (1982).Google Scholar
  21. 21.
    D. L. Wu, J. H. Jiang, and C. P. Davis, “EOS MLS cloud ice measurements and cloudy-sky radiative transfer model,” IEEE Trans. Geosci. Remote Sens. 44 (5), 1156–1165 (2006).CrossRefGoogle Scholar
  22. 22.
    S. Yu. Matrosov, “Microwave radiation transfer in precipitation,” Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, No. 478, 50–61 (1983).Google Scholar
  23. 23.
    D. D. Turner, M. P. Cadeddu, U. Lohnert, et al., “Modifications to the water vapor continuum in the microwave suggested by ground-based 150-GHz observations,” IEEE Trans. Geosci. Remote Sens. Lett. 47 (10), 3326–3337 (2009).CrossRefGoogle Scholar
  24. 24.
    T. Meissner and F. J. Wentz, “The complex dielectric constant of pure and sea water from microwave satellite observations,” IEEE Trans. Geosci. Remote Sens. 42 (9), 1836–1849 (2004).CrossRefGoogle Scholar
  25. 25.
    B. Chapron, A. Bingham, F. Collard, et al., “Ocean remote sensing data integration-examples and outlook,” in Proc. Ocean. Sustain. Ocean Obs. Inf. Soc., WPP-306 (ESA, Venice, Italy, 2010). doi 10.5270/OceanObs09Google Scholar
  26. 26.
    K. Hilburn, D. Smith, and T. Meissner, “Assessment of remote sensing systems version-7 rain rates,” EGU Gen. Assem. Conf. Abstr. 15, 6120 (2013).Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2016

Authors and Affiliations

  1. 1.Russian State Hydrometeorological UniversitySt. PetersburgRussia
  2. 2.French Research Institute for Exploitation of the SeaPluzaneFrance

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