Tropical Cyclone Rain Retrievals from FY-3B MWRI Brightness Temperatures Using the Goddard Profiling Algorithm (GPROF)

Ruanyu Zhang Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China

Search for other papers by Ruanyu Zhang in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0003-2184-1098
,
Christian D. Kummerow Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

Search for other papers by Christian D. Kummerow in
Current site
Google Scholar
PubMed
Close
,
David L. Randel Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

Search for other papers by David L. Randel in
Current site
Google Scholar
PubMed
Close
,
Paula J. Brown Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

Search for other papers by Paula J. Brown in
Current site
Google Scholar
PubMed
Close
,
Wesley Berg Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

Search for other papers by Wesley Berg in
Current site
Google Scholar
PubMed
Close
, and
Zhenzhan Wang Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing, China

Search for other papers by Zhenzhan Wang in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

This study focuses on the tropical cyclone rainfall retrieval using FY-3B Microwave Radiation Imager (MWRI) brightness temperatures (Tbs). The GPROF, a fully parametric approach based on the Bayesian scheme, is adapted for use by the MWRI sensor. The MWRI GPROF algorithm is an ocean-only scheme used to estimate rain rates and hydrometeor vertical profiles. An a priori database is constructed from MWRI simulated Tbs, the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) combined data, and ancillary data resulting in about 100 000 rainfall profiles. The performance of MWRI retrievals is consistent with DPR observations, even though MWRI retrievals slightly overestimate low rain rates and underestimate high rain rates. The total bias of MWRI retrievals is less than 13% of the mean rain rate of DPR precipitation. Statistical comparisons over GMI GPROF, GMI Hurricane GPROF (HGPROF), and MWRI GPROF retrievals show MWRI GPROF retrievals are consistent in terms of spatial distribution and rain estimates for TCs compared with the other two estimates. In terms of the global precipitation, the mean rain rates at different distances from best track locations for five TC categories are used to identify substantial differences between mean MWRI and GMI GPROF retrievals. After correcting the biases between MWRI and GMI retrievals, the performance of MWRI retrievals shows slight overestimate for light rain rates while underestimating rain rates near the eyewall for category 4 and 5 only.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ruanyu Zhang, ruanyu_zhang@163.com

Abstract

This study focuses on the tropical cyclone rainfall retrieval using FY-3B Microwave Radiation Imager (MWRI) brightness temperatures (Tbs). The GPROF, a fully parametric approach based on the Bayesian scheme, is adapted for use by the MWRI sensor. The MWRI GPROF algorithm is an ocean-only scheme used to estimate rain rates and hydrometeor vertical profiles. An a priori database is constructed from MWRI simulated Tbs, the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) combined data, and ancillary data resulting in about 100 000 rainfall profiles. The performance of MWRI retrievals is consistent with DPR observations, even though MWRI retrievals slightly overestimate low rain rates and underestimate high rain rates. The total bias of MWRI retrievals is less than 13% of the mean rain rate of DPR precipitation. Statistical comparisons over GMI GPROF, GMI Hurricane GPROF (HGPROF), and MWRI GPROF retrievals show MWRI GPROF retrievals are consistent in terms of spatial distribution and rain estimates for TCs compared with the other two estimates. In terms of the global precipitation, the mean rain rates at different distances from best track locations for five TC categories are used to identify substantial differences between mean MWRI and GMI GPROF retrievals. After correcting the biases between MWRI and GMI retrievals, the performance of MWRI retrievals shows slight overestimate for light rain rates while underestimating rain rates near the eyewall for category 4 and 5 only.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ruanyu Zhang, ruanyu_zhang@163.com
Save
  • Aires, F., C. Prigent, F. Bernardo, C. Jiménez, R. Saunders, and P. Brunel, 2011: A Tool to Estimate Land-Surface Emissivities at Microwave frequencies (TELSEM) for use in numerical weather prediction. Quart. J. Roy. Meteor. Soc., 137, 690699, https://doi.org/10.1002/qj.803.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barrett, E. C., 1970: The estimation of monthly rainfall from satellite. Mon. Wea. Rev., 98, 322327, https://doi.org/10.1175/1520-0493(1970)098<0322:TEOMRF>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berg, W., T. L’ Ecuyer, and C. Kummerow, 2006: Rainfall climate regimes: The relationship of regional TRMM rainfall biases to the environment. J. Appl. Meteor. Climatol., 45, 434454, https://doi.org/10.1175/JAM2331.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berg, W., and Coauthors, 2016: Intercalibration of the GPM microwave radiometer constellation. J. Atmos. Oceanic Technol., 33, 26392654, https://doi.org/10.1175/JTECH-D-16-0100.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, P. J., C. D. Kummerow, and D. L. Randel, 2016: Hurricane GPROF: An optimized ocean microwave rainfall retrieval for tropical cyclones. J. Atmos. Oceanic Technol., 33, 15391556, https://doi.org/10.1175/JTECH-D-15-0234.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Büttner, K. J. K., 1963: Regenortung vom Wettersatelliten mit Hilfe von Zentimeterwellen (Rain localization from a weather satellite via centimeter waves). Naturwissenschaften, 50, 591592, https://doi.org/10.1007/BF00632686.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chu, J., C. R. Sampson, A. S. Levine, and E. Fukada, 2002: The joint typhoon warning center tropical cyclone best-tracks, 1945–2000. Doc. NRL/MR/7540-02-16, Joint Typhoon Warning Center, Naval Research Laboratory, http://www.metoc.navy.mil/jtwc/products/best-tracks/tc-bt-report.html.

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Defforge, C. L., and T. M. Merlis, 2017: Evaluating the evidence of a global sea surface temperature threshold for tropical cyclone genesis. J. Climate, 30, 91339145, https://doi.org/10.1175/JCLI-D-16-0737.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Farfán, L. M., and I. Fogel, 2007: Influence of tropical cyclones on humidity patterns over southern Baja California, Mexico. Mon. Wea. Rev., 135, 12081224, https://doi.org/10.1175/MWR3356.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gray, W. M., 1968: Global view of the origins of tropical disturbances and storms. Mon. Wea. Rev., 96, 669700, https://doi.org/10.1175/1520-0493(1968)096<0669:GVOTOO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grecu, M., W. S. Olson, S. J. Munchak, S. Ringerud, L. Liao, Z. Haddad, B. L. Kelley, and S. F. McLaughlin, 2016: The GPM combined algorithm. J. Atmos. Oceanic Technol., 33, 22252245, https://doi.org/10.1175/JTECH-D-16-0019.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hilburn, K. A., and F. J. Wentz, 2008: Intercalibrated passive microwave rain products from the Unified Microwave Ocean Retrieval Algorithm (UMORA). J. Appl. Meteor. Climatol., 47, 778794, https://doi.org/10.1175/2007JAMC1635.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., and Coauthors, 2014: The global precipitation measurement mission. Bull. Amer. Meteor. Soc., 95, 701722, https://doi.org/10.1175/BAMS-D-13-00164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hristova-Veleva, S., and Coauthors, 2013: Revealing the winds under the rain. Part I: Passive microwave rain retrievals using a new observation-based parameterization of subsatellite rain variability and intensity—Algorithm description. J. Appl. Meteor. Climatol., 52, 28282848, https://doi.org/10.1175/JAMC-D-12-0237.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, C., C. Zhang, S. Yang, D. Chen, and S. He, 2018: Perspective on the northwestward shift of autumn tropical cyclogenesis locations over the western North Pacific from shifting ENSO. Climate Dyn., 51, 24552465, https://doi.org/10.1007/s00382-017-4022-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kidd, C., 1998: On rainfall retrieval using polarization-corrected temperatures. Int. J. Remote Sens., 19, 981996, https://doi.org/10.1080/014311698215829.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kidd, C., and V. Levizzani, 2011: Status of satellite precipitation retrievals. Hydrol. Earth Syst. Sci., 15, 11091116, https://doi.org/10.5194/hess-15-1109-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kidd, C., T. Matsui, J. Chern, K. Mohr, C. D. Kummerow, and D. L. Randel, 2016: Global precipitation estimates from cross-track passive microwave observations using a physically based retrieval scheme. J. Hydrometeor., 17, 383400, https://doi.org/10.1175/JHM-D-15-0051.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kidd, C., R. Roca, and E. Stocker, 2018: Development of the SAPHIR Precipitation Retrieval and Profiling Scheme (PRPS). Geophysical Research Abstracts, Vol. 20, Abstract EGU2018-9356, https://meetingorganizer.copernicus.org/EGU2018/EGU2018-9356.pdf.

    • Search Google Scholar
    • Export Citation
  • Klotzbach, P. J., and E. C. J. Oliver, 2015: Variations in global tropical cyclone activity and the Madden-Julian Oscillation since the midtwentieth century. Geophys. Res. Lett., 42, 41994207, https://doi.org/10.1002/2015GL063966.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C. D., 1993: On the accuracy of the Eddington approximation for radiative transfer in the microwave frequencies. J. Geophys. Res., 98, 27572765, https://doi.org/10.1029/92JD02472.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C. D., and L. Giglio, 1994: A passive microwave technique for estimating rainfall and vertical structure information from space. Part I: Algorithm description. J. Appl. Meteor., 33, 318, https://doi.org/10.1175/1520-0450(1994)033<0003:APMTFE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C. D., W. S. Olson, and L. Giglio, 1996: A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors. IEEE Trans. Geosci. Remote Sens., 34, 12131232, https://doi.org/10.1109/36.536538.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C. D., and Coauthors, 2001: The evolution of the Goddard Profiling Algorithm (GPROF) for rainfall estimation from passive microwave sensors. J. Appl. Meteor., 40, 18011820, https://doi.org/10.1175/1520-0450(2001)040<1801:TEOTGP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C. D., S. Ringerud, J. Crook, D. L. Randel, and W. Berg, 2011: An observationally generated a priori database for microwave rainfall retrievals. J. Atmos. Oceanic Technol., 28, 113130, https://doi.org/10.1175/2010JTECHA1468.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C. D., D. L. Randel, M. Kulie, N. Wang, R. Ferraro, S. J. Munchak, and V. Petković, 2015: The evolution of the Goddard profiling algorithm to a fully parametric scheme. J. Atmos. Oceanic Technol., 32, 22652280, https://doi.org/10.1175/JTECH-D-15-0039.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kunkee, D. B., G. A. Poe, D. J. Boucher, S. D. Swadley, Y. Hong, J. E. Wessel, and E. A. Uliana, 2008: Design and evaluation of the first special sensor microwave imager/sounder. IEEE Trans. Geosci. Remote Sens., 46, 863883, https://doi.org/10.1109/TGRS.2008.917980.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Landsea, C. W., and J. L. Franklin, 2013: Atlantic hurricane database uncertainty and presentation of a new database format. Mon. Wea. Rev., 141, 35763592, https://doi.org/10.1175/MWR-D-12-00254.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, X., F. Zhao, Y. Qiao, H. Yang, and R. You, 2012: Comparison of three rainfall products from Microwave Imagers during development of Typhoon Ma-on. Adv. Mater. Res., 442, 167171, https://doi.org/10.4028/www.scientific.net/AMR.442.167.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marino, D. R., 2012: Statistical analysis of ensemble forecasts of tropical cyclone tracks over the Northwest Pacific Ocean. Ph.D. thesis, Naval Postgraduate School, 84 pp., http://hdl.handle.net/10945/17412.

  • Muth, C., P. S. Lee, J. C. Shiue, and W. A. Webb, 2004: Advanced technology microwave sounder on NPOESS and NPP. Proc. IEEE Int. Conf. on Geoscience and Remote Sensing Symp. 2004, Anchorage, AK, Institute of Electrical and Electronics Engineers, 2454–2458, https://doi.org/10.1109/IGARSS.2004.1369789.

    • Crossref
    • Export Citation
  • NOAA, 2005: Saffir–Simpson hurricane wind scale. NOAA/National Hurricane Center, https://www.nhc.noaa.gov/aboutsshws.php.

  • Olson, W. S., C. D. Kummerow, Y. Hong, and W.-K. Tao, 1999: Atmospheric latent heating distributions in the tropics derived from satellite passive microwave radiometer measurements. J. Appl. Meteor., 38, 633664, https://doi.org/10.1175/1520-0450(1999)038<0633:ALHDIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Palmén, E., 1948: On the formation and structure of tropical hurricanes. Geophysica, 3 (1), 2638.

  • Panegrossi, G., and Coauthors, 1998: Use of cloud model microphysics for passive microwave-based precipitation retrieval: Significance of consistency between model and measurement manifolds. J. Atmos. Sci., 55, 16441673, https://doi.org/10.1175/1520-0469(1998)055<1644:UOCMMF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petković, V., C. D. Kummerow, D. L. Randel, J. R. Pierce, and J. K. Kodros, 2018: Improving the quality of heavy precipitation estimates from satellite passive microwave rainfall retrievals. J. Hydrometeor., 19, 6985, https://doi.org/10.1175/JHM-D-17-0069.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Precipitation Research Group, 2017: NASA Global Precipitation Measurement Mission: GPROF2017. Algorithm Theoretical Basis Doc., Version 1, 63 pp., http://rain.atmos.colostate.edu/ATBD/ATBD_GPM_June1_2017.pdf.

  • Shimoda, H., 2005: GCOM missions. Proc. IEEE Int. Conf. on Geoscience and Remote Sensing Symp. 2005, Seoul, South Korea, Institute of Electrical and Electronics Engineers, 4201–4204, https://doi.org/10.1109/IGARSS.2005.1525844.

    • Crossref
    • Export Citation
  • Spencer, R. W., H. M. Goodman, and R. E. Hood, 1989: Precipitation retrieval over land and ocean with the SSM/I: Identification and characteristics of the scattering signal. J. Atmos. Oceanic Technol., 6, 254273, https://doi.org/10.1175/1520-0426(1989)006<0254:PROLAO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tao, W.-K., J. Simpson, S. Lang, M. McCumber, R. Adler, and R. Penc, 1990: An algorithm to estimate the heating budget from vertical hydrometeor profiles. J. Appl. Meteor., 29, 12321244, https://doi.org/10.1175/1520-0450(1990)029<1232:AATETH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Villarini, G., D. A. Lavers, E. Scoccimarro, M. Zhao, M. F. Wehner, G. A. Vecchi, T. R. Knutson, and K. A. Reed, 2014: Sensitivity of tropical cyclone rainfall to idealized global-scale forcings. J. Climate, 27, 46224641, https://doi.org/10.1175/JCLI-D-13-00780.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Viltard, N., C. Burlaud, and C. D. Kummerow, 2006: Rain retrieval from TMI brightness temperature measurements using a TRMM PR-based database. J. Appl. Meteor. Climatol., 45, 455466, https://doi.org/10.1175/JAM2346.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., Y. Xu, and B. Bi, 2007: Forecasting and warning of tropical cyclones in China. Data Sci. J., 6, S723S737, https://doi.org/10.2481/dsj.6.S723.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wentz, F. J., and R. W. Spencer, 1998: SSM/I rain retrievals within a unified all-weather ocean algorithm. J. Atmos. Sci., 55, 16131627, https://doi.org/10.1175/1520-0469(1998)055<1613:SIRRWA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilheit, T. T., A. T. C. Chang, M. S. V. Rao, E. B. Rodgers, and J. S. Theon, 1977: A satellite technique for quantitatively mapping rainfall rates over the oceans. J. Appl. Meteor., 16, 551560, https://doi.org/10.1175/1520-0450(1977)016<0551:ASTFQM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, H., X. Zou, X. Li, and R. You, 2012: Environmental data records from FengYun-3B microwave radiation imager. IEEE Trans. Geosci. Remote Sens., 50, 49864993, https://doi.org/10.1109/TGRS.2012.2197003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Q., L. Wu, and Q. Liu, 2009: Tropical cyclone damages in China 1983–2006. Bull. Amer. Meteor. Soc., 90, 489496, https://doi.org/10.1175/2008BAMS2631.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, R., Z. Wang, L. Zhang, and Y. Li, 2017: Rainfall retrieval of tropical cyclones using FY-3B microwave radiation imager (MWRI). Proc. IEEE Int. Conf. on Geoscience and Remote Sensing Symp. 2017, Fort Worth, TX, USA, Institute of Electrical and Electronics Engineers, 550–553, https://doi.org/10.1109/IGARSS.2017.8127012.

    • Crossref
    • Export Citation
  • Zick, S. E., and C. J. Matyas, 2016: A shape metric methodology for studying the evolving geometries of synoptic-scale precipitation patterns in tropical cyclones. Ann. Amer. Assoc. Geogr., 106, 12171235, https://doi.org/10.1080/24694452.2016.1206460.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 530 222 22
PDF Downloads 261 56 5