Multisensor Precipitation Reanalysis

Brian R. Nelson NOAA/NESDIS/NCDC, Asheville, North Carolina

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D-J. Seo NOAA/NWS/OHD, Silver Spring, Maryland, and University Corporation for Atmospheric Research, Boulder, Colorado

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Dongsoo Kim NOAA/NESDIS/NCDC, Asheville, North Carolina

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Abstract

Temporally consistent high-quality, high-resolution multisensor precipitation reanalysis (MPR) products are needed for a wide range of quantitative climatological and hydroclimatological applications. Therefore, the authors have reengineered the multisensor precipitation estimator (MPE) algorithms of the NWS into the MPR package. Owing to the retrospective nature of the analysis, MPR allows for the utilization of additional rain gauge data, more rigorous automatic quality control, and post factum correction of radar quantitative precipitation estimation (QPE) and optimization of key parameters in multisensor estimation. To evaluate and demonstrate the value of MPR, the authors designed and carried out a set of cross-validation experiments in the pilot domain of North Carolina and South Carolina. The rain gauge data are from the reprocessed Hydrometeorological Automated Data System (HADS) and the daily Cooperative Observer Program (COOP). The radar QPE data are the operationally produced Weather Surveillance Radar-1988 Doppler digital precipitation array (DPA) products. To screen out bad rain gauge data, quality control steps were taken that use rain gauge and radar data. The resulting MPR products are compared with the stage IV product on a daily scale at the withheld COOP gauge locations. This paper describes the data, the MPR procedure, and the validation experiments, and it summarizes the findings.

Corresponding author address: Brian R. Nelson, NOAA/NESDIS/NCDC, 151 Patton Ave., Asheville, NC 28801. Email: brian.nelson@noaa.gov

Abstract

Temporally consistent high-quality, high-resolution multisensor precipitation reanalysis (MPR) products are needed for a wide range of quantitative climatological and hydroclimatological applications. Therefore, the authors have reengineered the multisensor precipitation estimator (MPE) algorithms of the NWS into the MPR package. Owing to the retrospective nature of the analysis, MPR allows for the utilization of additional rain gauge data, more rigorous automatic quality control, and post factum correction of radar quantitative precipitation estimation (QPE) and optimization of key parameters in multisensor estimation. To evaluate and demonstrate the value of MPR, the authors designed and carried out a set of cross-validation experiments in the pilot domain of North Carolina and South Carolina. The rain gauge data are from the reprocessed Hydrometeorological Automated Data System (HADS) and the daily Cooperative Observer Program (COOP). The radar QPE data are the operationally produced Weather Surveillance Radar-1988 Doppler digital precipitation array (DPA) products. To screen out bad rain gauge data, quality control steps were taken that use rain gauge and radar data. The resulting MPR products are compared with the stage IV product on a daily scale at the withheld COOP gauge locations. This paper describes the data, the MPR procedure, and the validation experiments, and it summarizes the findings.

Corresponding author address: Brian R. Nelson, NOAA/NESDIS/NCDC, 151 Patton Ave., Asheville, NC 28801. Email: brian.nelson@noaa.gov

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  • Anagnostou, E. N., Morales C. A. , and Dinku T. , 2001: The use of TRMM precipitation radar observations in determining ground radar calibration biases. J. Atmos. Oceanic Technol., 18 , 616628.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Atlas, D., Rosenfeld D. , and Wolf D. B. , 1990: Climatologically tuned reflectivity–rainfall rate relations and links to area–time integrals. J. Appl. Meteor., 29 , 11201135.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baeck, M. L., and Smith J. A. , 1998: Estimation of heavy rainfall by the WSR-88D. Wea. Forecasting, 13 , 416436.

  • Breidenbach, J. P., Seo D-J. , and Fulton R. A. , 1998: Stage II and III post processing of NEXRAD precipitation estimates in the modernized Weather Service. Preprints, 14th Conf. on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Phoenix, AZ, Amer. Meteor. Soc., 263–266.

    • Search Google Scholar
    • Export Citation
  • Breidenbach, J. P., Seo D-J. , Tilles P. , and Roy K. , 1999: Accounting for radar beam blockage patterns in radar-derived precipitation mosaics for River Forecast Centers. Preprints, 15th Int. Conf. on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Dallas, TX, Amer. Meteor. Soc., 5.22. [Available online at http://ams.confex.com/ams/older/99annual/abstracts/1699.htm].

    • Search Google Scholar
    • Export Citation
  • Breidenbach, J. P., Fortune M. A. , Seo D-J. , and Tilles P. , cited. 2001a: Multi-sensor precipitation estimation for use by River Forecast Centers during heavy rainfall events. [Available online at http://ams.confex.com/ams/annual2001/techprogram/paper_18658.htm].

    • Search Google Scholar
    • Export Citation
  • Breidenbach, J. P., Seo D-J. , Tilles P. , and Pham C. , cited. 2001b: Seasonal variation in multi-radar coverage for WSR-88D precipitation estimation in a mountainous region. [Available online at http://ams.confex.com/ams/annual2001/techprogram/paper_18668.htm].

    • Search Google Scholar
    • Export Citation
  • Breidenbach, J. P., Seo D-J. , Tilles P. , and Fortune M. , 2002: Multisensor precipitation estimation for use by the National Weather Service River Forecast Centers. Preprints, 16th Conf. on Hydrology, Orlando, FL, Amer. Meteor. Soc., 2.5. [Available online at http://ams.confex.com/ams/annual2002/techprogram/paper_26937.htm].

    • Search Google Scholar
    • Export Citation
  • Fuller, W. A., 1987: Measurement Error Models. John Wiley and Sons, 440 pp.

  • 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 , 377395.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hudlow, M. D., 1988: Technological developments in real-time operational hydrologic forecasting in the United States. J. Hydrol., 102 , 6992.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hunter, S., and Holroyd E. W. III, 2002: Demonstration of improved operational water resources management through use of better snow water equivalent information. U.S. Bureau of Reclamation Rep. R-02-02, 75 pp.

    • Search Google Scholar
    • Export Citation
  • Kim, D., Nelson B. R. , and Seo D-J. , 2009: Characteristics of reprocessed Hydrometeorological Automated Data System (HADS) hourly precipitation data. Wea. Forecasting, 24 , 12871296.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kottegoda, N. T., and Rosso R. , 1997: Statistics, Probability, and Reliability for Civil and Environmental Engineers. McGraw-Hill, 735 pp.

    • Search Google Scholar
    • Export Citation
  • Klazura, G. E., and Imy D. A. , 1993: A description of the initial set of analysis products available from the NEXRAD WSR-88D system. Bull. Amer. Meteor. Soc., 74 , 12931311.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krajewski, W. F., and Vignal B. , 2001: Evaluation of anomalous propagation echo detection in WSR-88D data: A large sample case study. J. Atmos. Oceanic Technol., 18 , 807814.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murphy, A. H., and Winkler R. L. , 1987: A general framework for forecast verification. Mon. Wea. Rev., 115 , 13301338.

  • NCDC, 2009: Data documentation for data set 3200 (DSI-3200): Surface land daily cooperative summary of the day. NCDC, 19 pp. [Available online at http://www1.ncdc.noaa.gov/pub/data/documentlibrary/tddoc/td3200.pdf].

    • Search Google Scholar
    • Export Citation
  • NWS, cited. 2009: The National Weather Service River Forecast System user manual documentation. NOAA/NWS/Office of Hydrologic Development Rep. [Available online at http://www.nws.noaa.gov/oh/hrl/nwsrfs/users_manual/htm/formats.php].

    • Search Google Scholar
    • Export Citation
  • Press, W. H., Teukolsky S. A. , Vetterling W. T. , and Flannery B. P. , 1992: Numerical Recipes in C: The Art of Scientific Computing. 2nd ed. Cambridge University Press, 994 pp.

    • Search Google Scholar
    • Export Citation
  • Schaake, J., Henkel A. , and Cong S. , 2004: Application of PRISM climatologies for hydrologic modeling and forecasting in the western United States. Preprints, 18th Conf. on Hydrology, Seattle, WA, Amer. Meteor. Soc., 5.3. [Available online at http://ams.confex.com/ams/84Annual/techprogram/paper_72159.htm].

    • Search Google Scholar
    • Export Citation
  • Seo, D-J., 1996: Nonlinear estimation of spatial distribution of rainfall—An indicator cokriging approach. Stochastic Hydrol. Hydraul., 10 , 127150.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, D-J., 1998: Real-time estimation of rainfall fields using rain gage data under fractional coverage conditions. J. Hydrol., 208 , 2536.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, D-J., and Breidenbach J. P. , 2002: Real-time correction of spatially nonuniform bias in radar rainfall data using rain gauge measurements. J. Hydrometeor., 3 , 93111.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, D-J., Breidenbach J. P. , and Johnson E. R. , 1999: Real-time estimation of mean field bias in radar rainfall data. J. Hydrol., 223 , 131147.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, D-J., Breidenbach J. P. , Fulton R. A. , Miller D. A. , and O’Bannon T. , 2000: Real-time adjustment of range-dependent bias in WSR-88D rainfall data due to nonuniform vertical profile of reflectivity. J. Hydrometeor., 1 , 222240.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, D-J., Herr H. D. , and Schaake J. C. , 2006: A statistical post processor for accounting of hydrologic uncertainty in short range ensemble streamflow prediction. Hydrol. Earth Syst. Sci. Discuss., 3 , 19872035.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, J. A., and Krajewski W. F. , 1991: Estimation of the mean field bias of radar rainfall estimates. J. Appl. Meteor., 30 , 397412.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, J. A., Seo D. J. , Baeck M. L. , and Hudlow M. D. , 1996: An intercomparison study of NEXRAD precipitation estimates. Water Resour. Res., 32 , 20352045.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Steiner, M., 1996: Uncertainty of estimates of monthly areal rainfall for temporally sparse remote observations. Water Resour. Res., 32 , 373388.

  • Ulbrich, C. W., and Lee L. G. , 1999: Rainfall measurement error by WSR-88D radars due to variations in ZR law parameters and the radar constant. J. Atmos. Oceanic Technol., 16 , 10171024.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vasiloff, S. V., and Coauthors, 2007: Improving QPE and very short term QPF: An initiative for a community-wide integrated approach. Bull. Amer. Meteor. Soc., 88 , 18991911.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, J. W., and Brandes E. A. , 1979: Radar measurement of rainfall—A summary. Bull. Amer. Meteor. Soc., 60 , 10481058.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, C. B., Nelson B. R. , Bradley A. A. , Smith J. A. , Peters-Lidard C. D. , Kruger A. , and Baeck M. L. , 1999: An evaluation of NEXRAD precipitation estimates in complex terrain. J. Geophys. Res., 104 , (D16). 1969119703.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, C. B., Bradley A. A. , Krajewski W. F. , Kruger A. , and Morrissey M. L. , 2000: Evaluating NEXRD multisensor precipitation estimates for operational hydrologic forecasting. J. Hydrometeor., 1 , 241254.

    • Crossref
    • Search Google Scholar
    • Export Citation
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