Editorial Type:
Article
Article Type:
Research Article

The Influence of Surface and Precipitation Characteristics on TRMM Microwave Imager Rainfall Retrieval Uncertainty

N. Carr * School of Meteorology, University of Oklahoma, Norman, Oklahoma
+Advanced Radar Research Center, National Weather Center, Norman, Oklahoma

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P.-E. Kirstetter +Advanced Radar Research Center, National Weather Center, Norman, Oklahoma
#NOAA/National Severe Storms Laboratory, Norman, Oklahoma

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Y. Hong @School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, Oklahoma

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J. J. Gourley #NOAA/National Severe Storms Laboratory, Norman, Oklahoma

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M. Schwaller &NASA Goddard Space Flight Center, Greenbelt, Maryland

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W. Petersen ** NASA Wallops Flight Facility, Wallops Island, Virginia

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Nai-Yu Wang ++I.M. Systems Group, College Park, Maryland

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Ralph R. Ferraro ##NOAA/NESDIS, College Park, Maryland

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Xianwu Xue @School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, Oklahoma

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Print Publication:
01 Aug 2015
DOI:
https://doi.org/10.1175/JHM-D-14-0194.1
Page(s):
1596–1614
Restricted access

Abstract

Characterization of the error associated with quantitative precipitation estimates (QPEs) from spaceborne passive microwave (PMW) sensors is important for a variety of applications ranging from flood forecasting to climate monitoring. This study evaluates the joint influence of precipitation and surface characteristics on the error structure of NASA’s Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) surface QPE product (2A12). TMI precipitation products are compared with high-resolution reference precipitation products obtained from the NOAA/NSSL ground radar–based Multi-Radar Multi-Sensor (MRMS) system. Surface characteristics were represented via a surface classification dataset derived from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). This study assesses the ability of 2A12 to detect, classify, and quantify precipitation at its native resolution for the 2011 warm season (March–September) over the southern continental United States. Decreased algorithm performance is apparent over dry and sparsely vegetated regions, a probable result of the surface radiation signal mimicking the scattering signature associated with frozen hydrometeors. Algorithm performance is also shown to be positively correlated with precipitation coverage over the sensor footprint. The algorithm also performs better in pure stratiform and convective precipitation events, compared to events containing a mixture of stratiform and convective precipitation within the footprint. This possibly results from the high spatial gradients of precipitation associated with these events and an underrepresentation of such cases in the retrieval database. The methodology and framework developed herein apply more generally to precipitation estimates from other passive microwave sensors on board low-Earth-orbiting satellites and specifically could be used to evaluate PMW sensors associated with the recently launched Global Precipitation Measurement (GPM) mission.

Corresponding author address: Nick Carr, Advanced Radar Research Center, National Weather Center, 120 David L. Boren Blvd., Rm. 4630, Norman, OK 73072-7303. E-mail: n.carr2@ou.edu

Abstract

Characterization of the error associated with quantitative precipitation estimates (QPEs) from spaceborne passive microwave (PMW) sensors is important for a variety of applications ranging from flood forecasting to climate monitoring. This study evaluates the joint influence of precipitation and surface characteristics on the error structure of NASA’s Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) surface QPE product (2A12). TMI precipitation products are compared with high-resolution reference precipitation products obtained from the NOAA/NSSL ground radar–based Multi-Radar Multi-Sensor (MRMS) system. Surface characteristics were represented via a surface classification dataset derived from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). This study assesses the ability of 2A12 to detect, classify, and quantify precipitation at its native resolution for the 2011 warm season (March–September) over the southern continental United States. Decreased algorithm performance is apparent over dry and sparsely vegetated regions, a probable result of the surface radiation signal mimicking the scattering signature associated with frozen hydrometeors. Algorithm performance is also shown to be positively correlated with precipitation coverage over the sensor footprint. The algorithm also performs better in pure stratiform and convective precipitation events, compared to events containing a mixture of stratiform and convective precipitation within the footprint. This possibly results from the high spatial gradients of precipitation associated with these events and an underrepresentation of such cases in the retrieval database. The methodology and framework developed herein apply more generally to precipitation estimates from other passive microwave sensors on board low-Earth-orbiting satellites and specifically could be used to evaluate PMW sensors associated with the recently launched Global Precipitation Measurement (GPM) mission.

Corresponding author address: Nick Carr, Advanced Radar Research Center, National Weather Center, 120 David L. Boren Blvd., Rm. 4630, Norman, OK 73072-7303. E-mail: n.carr2@ou.edu
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  • Aires, F., Prigent C. , Bernardo F. , Jiménez C. , Saunders R. , and Brunel P. , 2011: A tool to estimate land-surface emissivities at microwave frequencies (TELSEM) for use in numerical weather prediction. Quart. J. Roy. Meteor. Soc., 137, 690–699, doi:10.1002/qj.803.

    • Search Google Scholar
    • Export Citation

  • Bakker, M. H. N., 2009: Transboundary river floods and institutional capacity. J. Amer. Water Resour. Assoc.,45, 553–566, doi:10.1111/j.1752-1688.2009.00325.x.

  • Bauer, P., Mahfouf J.-F. , Olson W. S. , Marzano F. S. , Michele S. D. , Tassa A. , and Mugnai A. , 2002: Error analysis of TMI rainfall estimates over ocean for variational data assimilation. Quart. J. Roy. Meteor. Soc., 128, 2129–2144, doi:10.1256/003590002320603575.

    • Search Google Scholar
    • Export Citation

  • Brier, G. W., and Allen R. A. , 1952: Verification of weather forecasts. Compendium of Meteorology, T. F. Malone, Ed., Amer. Meteor. Soc., 841–848.

  • Ferraro, R., and Coauthors, 2013: An evaluation of microwave land surface emissivities over the continental United States to benefit GPM-era precipitation algorithms. IEEE Trans. Geosci. Remote Sens., 51, 378–398, doi:10.1109/TGRS.2012.2199121.

    • Search Google Scholar
    • Export Citation

  • Friedl, M. A., Sulla-Menashe D. , Tan B. , Schneider A. , Ramankutty N. , Sibley A. , and Huang X. , 2010: MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets, 2001–2012. Remote Sens. Environ., 114, 168–182, doi:10.1016/j.rse.2009.08.016.

    • Search Google Scholar
    • Export Citation

  • Gopalan, K., Wang N. Y. , Ferraro R. , and Liu C. , 2010: Status of the TRMM 2A12 land precipitation algorithm. J. Atmos. Oceanic Technol., 27, 1343–1354, doi:10.1175/2010JTECHA1454.1.

    • Search Google Scholar
    • Export Citation

  • Grecu, M., and Anagnostou E. , 2001: Overland precipitation estimation from TRMM passive microwave observations. J. Appl. Meteor., 40, 1367–1380, doi:10.1175/1520-0450(2001)040<1367:OPEFTP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hou, A. Y., Zhang S. Q. , da Silva A. M. , Olson W. S. , Kummerow C. D. , and Simpson J. , 2001: Improving global analysis and short-range forecast using rainfall and moisture observations derived from TRMM and SSM/I passive microwave sensors. Bull. Amer. Meteor. Soc., 82, 659–680, doi:10.1175/1520-0477(2001)082<0659:IGAASF>2.3.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kirstetter, P. E., and Coauthors, 2012: Toward a framework for systematic error modeling of NASA spaceborne radar with NOAA/NSSL ground radar–based National Mosaic QPE. J. Hydrometeor., 13, 1285–1300, doi:10.1175/JHM-D-11-0139.1.

    • Search Google Scholar
    • Export Citation

  • Kirstetter, P. E., Viltard N. , and Gosset M. , 2013: An error model for instantaneous satellite rainfall estimates: Evaluation of BRAIN-TMI over West Africa. Quart. J. Roy. Meteor. Soc., 139, 894–911, doi:10.1002/qj.1964.

    • Search Google Scholar
    • Export Citation

  • Kirstetter, P. E., Hong Y. , Gourley J. J. , Cao Q. , Schwaller M. , and Petersen W. , 2014: Research framework to bridge from the Global Precipitation Measurement mission core satellite to the constellation sensors using ground-radar-based National Mosaic QPE. Remote Sensing of the Terrestrial Water Cycle, Geophys. Monogr., Vol. 206, Amer. Geophys. Union, 61–80, doi:10.1002/9781118872086.ch4.

  • Kirstetter, P. E., Hong Y. , Gourley J. J. , Schwaller M. , Petersen W. , and Cao Q. , 2015: Impact of sub-pixel rainfall variability on spaceborne precipitation estimation: Evaluating the TRMM 2A25 product. Quart. J. Roy. Meteor. Soc., 141, 953–966, doi:10.1002/qj.2416.

    • Search Google Scholar
    • Export Citation

  • Kitzmiller, K., and Coauthors, 2011: Evolving multisensor precipitation estimation methods: Their impacts on flow prediction using a distributed hydrologic model. J. Hydrometeor., 12, 1414–1431, doi:10.1175/JHM-D-10-05038.1.

    • Search Google Scholar
    • Export Citation

  • Kummerow, C., Barnes W. , Kozu T. , Shiue J. , and Simpson J. , 1998: The Tropical Rainfall Measuring Mission (TRMM) sensor package. J. Atmos. Oceanic Technol., 15, 809–817, doi:10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kummerow, C., and Coauthors, 2001: The evolution of the Goddard profiling algorithm (GPROF) for rainfall estimation from passive microwave sensors. J. Appl. Meteor., 40, 1801–1820, doi:10.1175/1520-0450(2001)040<1801:TEOTGP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kummerow, C., Ringerud S. , Crook J. , Randel D. , and Berg W. , 2011: An observationally generated a priori database for microwave rainfall retrievals. J. Atmos. Oceanic Technol., 28, 113–130, doi:10.1175/2010JTECHA1468.1.

    • Search Google Scholar
    • Export Citation

  • Lakshmanan, V., Fritz A. , Smith T. , Hondl K. , and Stumpf G. , 2007: An automated technique to quality control radar reflectivity data. J. Appl. Meteor. Climatol., 46, 288–305, doi:10.1175/JAM2460.1.

    • Search Google Scholar
    • Export Citation

  • Lin, X., and Hou A. Y. , 2008: Evaluation of coincident passive microwave rainfall estimates using TRMM PR and ground measurements as references. J. Appl. Meteor. Climatol., 47, 3170–3187, doi:10.1175/2008JAMC1893.1.

    • Search Google Scholar
    • Export Citation

  • Liu, C., and Zipser E. J. , 2009: “Warm rain” in the tropics: Seasonal and regional distribution based on 9 years of TRMM data. J. Climate, 22, 767–779, doi:10.1175/2008JCLI2641.1.

    • Search Google Scholar
    • Export Citation

  • McCollum, J., and Ferraro R. , 2003: Next generation of NOAA/NESDIS TMI, SSM/I, and AMSR-E microwave land rainfall algorithms. J. Geophys. Res., 108, 8382, doi:10.1029/2001JD001512.

    • Search Google Scholar
    • Export Citation

  • McCollum, J., and Ferraro R. , 2005: Microwave rainfall estimation over coasts. J. Atmos. Oceanic Technol., 22, 497–512, doi:10.1175/JTECH1732.1.

    • Search Google Scholar
    • Export Citation

  • Moisseev, D., Saltikoff E. , and Leskinen M. , 2009: Using dual-polarization weather radar observations to improve quantitative precipitation estimation in snowfall. Proceedings of the 8th International Symposium on Troposheric Profiling, A. Apituley, H. W. J. Russchenberg, and W. A. A. Monna, Eds., Royal Netherlands Meteorological Institute, S11-O04.

  • Olson, W. S., Hong Y. , Kummerow C. D. , and Turk J. , 2001: A texture polarization method for estimating convective–stratiform precipitation area coverage from passive microwave radiometer data. J. Appl. Meteor., 40, 1577–1591, doi:10.1175/1520-0450(2001)040<1577:ATPMFE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Petty, G. W., 1994: Physical Retrievals of over-ocean rain rate from multichannel microwave imagery. Part II: Algorithm Implementation. Meteor. Atmos. Phys., 54, 101–124, doi:10.1007/BF01030054.

    • Search Google Scholar
    • Export Citation

  • Prabhakara, C., Icovazzi R. Jr., Weinman J. A. , and Dalu G. , 2000: A TRMM microwave radiometer rain rate estimation method with convective and stratiform discrimination. J. Meteor. Soc. Japan, 78, 241–258.

    • Search Google Scholar
    • Export Citation

  • Shige, S., Kida S. , Ashiwake H. , Kubota T. , and Aonashi K. , 2013: Improvement of TMI rain retrievals in mountainous areas. J. Appl. Meteor. Climatol., 52, 242–254, doi:10.1175/JAMC-D-12-074.1.

    • Search Google Scholar
    • Export Citation

  • Steiner, M., Houze R. A. , and Yuter S. E. , 1995: Climatological characterization of three-dimensional storm structure from operational radar and rain gauge data. J. Appl. Meteor., 34, 1978–2007, doi:10.1175/1520-0450(1995)034<1978:CCOTDS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Stephens, G. L., and Kummerow C. D. , 2007: The remote sensing of clouds and precipitation from space: A review. J. Atmos. Sci., 64, 3742–3765, doi:10.1175/2006JAS2375.1.

    • Search Google Scholar
    • Export Citation

  • Tian, Y., and Peters-Lidard C. D. , 2007: Systematic anomalies over inland water bodies in satellite-based precipitation estimates. Geophys. Res. Lett., 34, L14403, doi:10.1029/2007GL030787.

    • Search Google Scholar
    • Export Citation

  • Varma, A. K., Liu G. , and Noh Y.-J. , 2004: Subpixel-scale variability of rainfall and its application to mitigate the beam-filling problem. J. Geophys. Res., 109, D18210, doi:10.1029/2004JD004968.

    • Search Google Scholar
    • Export Citation

  • Vasiloff, S., and Coauthors, 2007: Improving QPE and very short term QPF: An initiative for a community-wide integrated approach. Bull. Amer. Meteor. Soc., 88, 1899–1911, doi:10.1175/BAMS-88-12-1899.

    • Search Google Scholar
    • Export Citation

  • Wang, N. Y., Liu C. , Ferraro R. , Wolff D. , Zipser E. , and Kummerow C. , 2009: The TRMM 2A12 land precipitation product—Status and future plans. J. Meteor. Soc. Japan,87A, 237–253, doi:10.2151/jmsj.87A.237.

  • Weng, F., Zhu T. , and Yan B. , 2007: Satellite data assimilation in numerical weather prediction models. Part II: Uses of rain-affected radiances from microwave observations for hurricane vortex analysis. J. Atmos. Sci., 64, 3910–3925, doi:10.1175/2006JAS2051.1.

    • Search Google Scholar
    • Export Citation

  • Wilheit, T., Kummerow C. D. , and Ferraro R. , 2003: Rainfall algorithms for AMSR-E. IEEE Trans. Geosci. Remote Sens., 41, 204–214, doi:10.1109/TGRS.2002.808312.

    • Search Google Scholar
    • Export Citation

  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. Elsevier, 676 pp.

  • Wolff, D. B., and Fisher B. L. , 2008: Comparisons of instantaneous TRMM ground validation and satellite rain-rate estimates at different spatial scales. J. Appl. Meteor. Climatol., 47, 2215–2237, doi:10.1175/2008JAMC1875.1.

    • Search Google Scholar
    • Export Citation

  • Wolff, D. B., and Fisher B. L. , 2009: Assessing the relative performance of microwave-based satellite rain-rate retrievals using TRMM ground validation data. J. Appl. Meteor. Climatol., 48, 1069–1099, doi:10.1175/2008JAMC2127.1.

    • Search Google Scholar
    • Export Citation

  • Zhang, J., Howard K. , and Gourley J. J. , 2005: Constructing three-dimensional multiple radar reflectivity mosaics: Examples of convective storms and stratiform rain echoes. J. Atmos. Oceanic Technol., 22, 30–42, doi:10.1175/JTECH-1689.1.

    • Search Google Scholar
    • Export Citation

  • Zhang, J., and Coauthors, 2011a: National Mosaic and Multi-Sensor QPE (NMQ) system: Description, results and future plans. Bull. Amer. Meteor. Soc., 92, 1321–1338, doi:10.1175/2011BAMS-D-11-00047.1.

    • Search Google Scholar
    • Export Citation

  • Zhang, J., Qi Y. , Howard K. , Langston C. , and Kaney B. , 2011b: Radar Quality Index (RQI)—A combined measure of beam blockage and VPR effects in a national network. IAHS Publ.,351, 388–393.

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