Editorial Type:
Article
Article Type:
Research Article

Evaluation and Uncertainty Estimation of NOAA/NSSL Next-Generation National Mosaic Quantitative Precipitation Estimation Product (Q2) over the Continental United States

Sheng Chen * Civil Engineering and Environmental Science, and Hydrometeorology and Remote Sensing Laboratory, and Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma

Search for other papers by Sheng Chen in
Current site
Google Scholar
PubMed Close
,
Jonathan J. Gourley NOAA/National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Jonathan J. Gourley in
Current site
Google Scholar
PubMed Close
,
Yang Hong * Civil Engineering and Environmental Science, and Hydrometeorology and Remote Sensing Laboratory, and Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma

Search for other papers by Yang Hong in
Current site
Google Scholar
PubMed Close
,
P. E. Kirstetter * Civil Engineering and Environmental Science, and Hydrometeorology and Remote Sensing Laboratory, and Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma

Search for other papers by P. E. Kirstetter in
Current site
Google Scholar
PubMed Close
,
Jian Zhang NOAA/National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Jian Zhang in
Current site
Google Scholar
PubMed Close
,
Kenneth Howard NOAA/National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Kenneth Howard in
Current site
Google Scholar
PubMed Close
,
Zachary L. Flamig Civil Engineering and Environmental Science, University of Oklahoma, and NOAA/National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Zachary L. Flamig in
Current site
Google Scholar
PubMed Close
,
Junjun Hu Hydrometeorology and Remote Sensing Laboratory, and Advanced Radar Research Center, and School of Computer Science, University of Oklahoma, Norman, Oklahoma

Search for other papers by Junjun Hu in
Current site
Google Scholar
PubMed Close
, and
Youcun Qi ** Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Youcun Qi in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Aug 2013
DOI:
https://doi.org/10.1175/JHM-D-12-0150.1
Page(s):
1308–1322
Restricted access

Abstract

Quantitative precipitation estimation (QPE) products from the next-generation National Mosaic and QPE system (Q2) are cross-compared to the operational, radar-only product of the National Weather Service (Stage II) using the gauge-adjusted and manual quality-controlled product (Stage IV) as a reference. The evaluation takes place over the entire conterminous United States (CONUS) from December 2009 to November 2010. The annual comparison of daily Stage II precipitation to the radar-only Q2Rad product indicates that both have small systematic biases (absolute values > 8%), but the random errors with Stage II are much greater, as noted with a root-mean-squared difference of 4.5 mm day−1 compared to 1.1 mm day−1 with Q2Rad and a lower correlation coefficient (0.20 compared to 0.73). The Q2 logic of identifying precipitation types as being convective, stratiform, or tropical at each grid point and applying differential Z–R equations has been successful in removing regional biases (i.e., overestimated rainfall from Stage II east of the Appalachians) and greatly diminishes seasonal bias patterns that were found with Stage II. Biases and radar artifacts along the coastal mountain and intermountain chains were not mitigated with rain gauge adjustment and thus require new approaches by the community. The evaluation identifies a wet bias by Q2Rad in the central plains and the South and then introduces intermediate products to explain it. Finally, this study provides estimates of uncertainty using the radar quality index product for both Q2Rad and the gauge-corrected Q2RadGC daily precipitation products. This error quantification should be useful to the satellite QPE community who use Q2 products as a reference.

Corresponding author address: Jonathan J. Gourley, National Weather Center, 120 David L. Boren Blvd., Norman, OK 73072-7303. E-mail: jj.gourley@noaa.gov

Abstract

Quantitative precipitation estimation (QPE) products from the next-generation National Mosaic and QPE system (Q2) are cross-compared to the operational, radar-only product of the National Weather Service (Stage II) using the gauge-adjusted and manual quality-controlled product (Stage IV) as a reference. The evaluation takes place over the entire conterminous United States (CONUS) from December 2009 to November 2010. The annual comparison of daily Stage II precipitation to the radar-only Q2Rad product indicates that both have small systematic biases (absolute values > 8%), but the random errors with Stage II are much greater, as noted with a root-mean-squared difference of 4.5 mm day−1 compared to 1.1 mm day−1 with Q2Rad and a lower correlation coefficient (0.20 compared to 0.73). The Q2 logic of identifying precipitation types as being convective, stratiform, or tropical at each grid point and applying differential Z–R equations has been successful in removing regional biases (i.e., overestimated rainfall from Stage II east of the Appalachians) and greatly diminishes seasonal bias patterns that were found with Stage II. Biases and radar artifacts along the coastal mountain and intermountain chains were not mitigated with rain gauge adjustment and thus require new approaches by the community. The evaluation identifies a wet bias by Q2Rad in the central plains and the South and then introduces intermediate products to explain it. Finally, this study provides estimates of uncertainty using the radar quality index product for both Q2Rad and the gauge-corrected Q2RadGC daily precipitation products. This error quantification should be useful to the satellite QPE community who use Q2 products as a reference.

Corresponding author address: Jonathan J. Gourley, National Weather Center, 120 David L. Boren Blvd., Norman, OK 73072-7303. E-mail: jj.gourley@noaa.gov
Save
Cover Journal of Hydrometeorology
Journal of Hydrometeorology

  • Amitai, E., Llort X. , and Sempere-Torres D. , 2009: Comparison of TRMM radar rainfall estimates with NOAA next-generation QPE. J. Meteor. Soc. Japan, 87, 109–118.

    • Search Google Scholar
    • Export Citation

  • Amitai, E., Petersen W. , Llort X. , and Vasiloff S. , 2012: Multiplatform comparisons of rain intensity for extreme precipitation events. IEEE Trans. Geosci. Remote Sens., 50, 675–686.

    • Search Google Scholar
    • Export Citation

  • Benjamin, S. G., and Coauthors, 2004: An hourly assimilation–forecast cycle: The RUC. Mon. Wea. Rev., 132, 495–518.

  • Daly, C., Neilson R. P. , and Phillips D. L. , 1994: A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140–158.

    • Search Google Scholar
    • Export Citation

  • Fulton, R. A., Breidenbach J. P. , Seo D. J. , Miller D. A. , and O'Bannon T. , 1998: The WSR-88D rainfall algorithm. Wea. Forecasting, 13, 377–395.

    • Search Google Scholar
    • Export Citation

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

    • Search Google Scholar
    • Export Citation

  • Krajewski, W. F., Villarini G. , and Smith J. A. , 2010: Radar-rainfall uncertainties. Bull. Amer. Meteor. Soc., 91, 87–94.

  • Lakshmanan, V., Smith T. , Stumpf G. , and Hondl K. , 2007: The warning decision support system–integrated information. Wea. Forecasting, 22, 596–612.

    • Search Google Scholar
    • Export Citation

  • Maddox, R. A., Zhang J. , Gourley J. J. , and Howard K. W. , 2002: Weather radar coverage over the contiguous United States. Wea. Forecasting, 17, 927–934.

    • Search Google Scholar
    • Export Citation

  • Rosenfeld, D., and Ulbrich C. W. , 2003: Cloud microphysical properties, processes, and rainfall estimation opportunities. Radar and Atmospheric Science: A Collection of Essays in Honor of David Atlas, Meteor. Monogr., No. 52, Amer. Meteor. Soc., 237–258.

  • Scofield, R. A., and Kuligowski R. J. , 2003: Status and outlook of operational satellite precipitation algorithms for extreme-precipitation events. Wea. Forecasting, 18, 1037–1051.

    • Search Google Scholar
    • Export Citation

  • Vasiloff, S. V., and Coauthors, 2007: Improving QPE and Very Short Term QPF. Bull. Amer. Meteor. Soc., 88, 1899–1911.

  • Vicente, G. A., Scofield R. A. , and Menzel W. P. , 1998: The operational GOES infrared rainfall estimation technique. Bull. Amer. Meteor. Soc., 79, 1883–1898.

    • Search Google Scholar
    • Export Citation

  • Wu, W., Kitzmiller D. , and Wu S. , 2012: Evaluation of radar precipitation estimates from the National Mosaic and Multisensor Quantitative Precipitation Estimation System and the WSR-88D Precipitation Processing System over the conterminous United States. J. Hydrometeor., 13, 1080–1093.

    • Search Google Scholar
    • Export Citation

  • Xu, X., Howard K. , and Zhang J. , 2008: An automated radar technique for the identification of tropical precipitation. J. Hydrometeor., 9, 885–902.

    • Search Google Scholar
    • Export Citation

  • Zawadzki, I., 1975: On radar-raingage comparison. J. Appl. Meteor., 14, 1430–1436.

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

    • Search Google Scholar
    • Export Citation

  • Zhang, J., Langston C. , and Howard K. , 2008: Brightband identification based on vertical profiles of reflectivity from the WSR-88D. J. Atmos. Oceanic Technol., 25, 1859–1872.

    • Search Google Scholar
    • Export Citation

  • Zhang, J., and Coauthors, 2011: National Mosaic and Multi-Sensor QPE (NMQ) system: Description, results, and future plans. Bull. Amer. Meteor. Soc., 92, 1321–1338.

    • Search Google Scholar
    • Export Citation

  • Zhang, J., Qin Y. , Howard K. , Langston C. , and Kaney B. , 2012: Radar Quality Index (RQI)—A combined measure of beam blockage and VPR effects in a national network.IAHS Publ., 351, 388–393.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 178 0 0
Full Text Views 271 160 6
PDF Downloads 152 78 2