Editorial Type:
Article
Article Type:
Research Article

NEXRAD NWS Polarimetric Precipitation Product Evaluation for IFloodS

Luciana K. Cunha Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, and Willis Research Network, London, United Kingdom

Search for other papers by Luciana K. Cunha in
Current site
Google Scholar
PubMed Close
,
James A. Smith Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey

Search for other papers by James A. Smith in
Current site
Google Scholar
PubMed Close
,
Witold F. Krajewski IIHR–Hydroscience and Engineering, The University of Iowa, Iowa City, Iowa

Search for other papers by Witold F. Krajewski in
Current site
Google Scholar
PubMed Close
,
Mary Lynn Baeck Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey

Search for other papers by Mary Lynn Baeck in
Current site
Google Scholar
PubMed Close
, and
Bong-Chul Seo IIHR–Hydroscience and Engineering, The University of Iowa, Iowa City, Iowa

Search for other papers by Bong-Chul Seo in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Aug 2015
Collections:
IFloodS 2013: A Field Campaign to Support the NASA-JAXA Global Precipitation Measurement Mission
DOI:
https://doi.org/10.1175/JHM-D-14-0148.1
Page(s):
1676–1699
Restricted access

Abstract

The NEXRAD program has recently upgraded the WSR-88D network observational capability with dual polarization (DP). In this study, DP quantitative precipitation estimates (QPEs) provided by the current version of the NWS system are evaluated using a dense rain gauge network and two other single-polarization (SP) rainfall products. The analyses are performed for the period and spatial domain of the Iowa Flood Studies (IFloodS) campaign. It is demonstrated that the current version (2014) of QPE from DP is not superior to that from SP mainly because DP QPE equations introduce larger bias than the conventional rainfall–reflectivity [i.e., R(Z)] relationship for some hydrometeor types. Moreover, since the QPE algorithm is based on hydrometeor type, abrupt transitions in the phase of hydrometeors introduce errors in QPE with surprising variation in space that cannot be easily corrected using rain gauge data. In addition, the propagation of QPE uncertainties across multiple hydrological scales is investigated using a diagnostic framework. The proposed method allows us to quantify QPE uncertainties at hydrologically relevant scales and provides information for the evaluation of hydrological studies forced by these rainfall datasets.

Denotes Open Access content.

Corresponding author address: Luciana Cunha, Department of Civil and Environmental Engineering, Princeton University, Engineering Quad, Princeton, NJ 08544-0001. E-mail: lcunha@princeton.edu

Abstract

The NEXRAD program has recently upgraded the WSR-88D network observational capability with dual polarization (DP). In this study, DP quantitative precipitation estimates (QPEs) provided by the current version of the NWS system are evaluated using a dense rain gauge network and two other single-polarization (SP) rainfall products. The analyses are performed for the period and spatial domain of the Iowa Flood Studies (IFloodS) campaign. It is demonstrated that the current version (2014) of QPE from DP is not superior to that from SP mainly because DP QPE equations introduce larger bias than the conventional rainfall–reflectivity [i.e., R(Z)] relationship for some hydrometeor types. Moreover, since the QPE algorithm is based on hydrometeor type, abrupt transitions in the phase of hydrometeors introduce errors in QPE with surprising variation in space that cannot be easily corrected using rain gauge data. In addition, the propagation of QPE uncertainties across multiple hydrological scales is investigated using a diagnostic framework. The proposed method allows us to quantify QPE uncertainties at hydrologically relevant scales and provides information for the evaluation of hydrological studies forced by these rainfall datasets.

Denotes Open Access content.

Corresponding author address: Luciana Cunha, Department of Civil and Environmental Engineering, Princeton University, Engineering Quad, Princeton, NJ 08544-0001. E-mail: lcunha@princeton.edu
Save
Cover Journal of Hydrometeorology
Journal of Hydrometeorology

  • Austin, P. M., 1987: Relation between measured radar reflectivity and surface rainfall. Mon. Wea. Rev., 115, 1053–1070, doi:10.1175/1520-0493(1987)115<1053:RBMRRA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Ayalew, T. B., Krajewski W. F. , and Mantilla R. , 2014: Connecting the power-law scaling structure of peak-discharges to spatially variable rainfall and catchment physical properties. Adv. Water Resour., 71, 32–43, doi:10.1016/j.advwatres.2014.05.009.

    • Search Google Scholar
    • Export Citation

  • Baeck, M. L., and Smith J. A. , 1998: Estimation of heavy rainfall by the WSR-88D. Wea. Forecasting, 13, 416–436, doi:10.1175/1520-0434(1998)013<0416:REBTWF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Berne, A., and Krajewski W. F. , 2013: Radar for hydrology: Unfulfilled promise or unrecognized potential? Adv. Water Resour., 51, 357–366, doi:10.1016/j.advwatres.2012.05.005.

    • Search Google Scholar
    • Export Citation

  • Borga, M., 2002: Accuracy of radar rainfall estimates for streamflow simulation. J. Hydrol., 267, 26–39, doi:10.1016/S0022-1694(02)00137-3.

    • Search Google Scholar
    • Export Citation

  • Breidenbach, J., and Bradberry J. , 2001: Multisensor precipitation estimates produced by National Weather Service forecast centers for hydrologic applications. Proc. 2001 Georgia Water Resources Conf., Athens, GA, University of Georgia, 179–182. [Available online at http://hdl.handle.net/1853/43760.]

  • Breidenbach, J., Seo D.-J. , Tilles P. , and Roy K. , 1999: Accounting for radar beam blockage patterns in radar-derived precipitation mosaics for River Forecast Centers. Proc. 15th Conf. on Interactive Information Processing Systems, Dallas, TX, Amer. Meteor. Soc., 5.22. [Available online at https://ams.confex.com/ams/99annual/abstracts/1699.htm.]

  • Brun, J., and Barros A. P. , 2014: Mapping the role of tropical cyclones on the hydroclimate of the southeast United States: 2002–2011. Int. J. Climatol., 34, 494–517, doi:10.1002/joc.3703.

    • Search Google Scholar
    • Export Citation

  • Carpenter, T. M., and Georgakakos K. P. , 2004: Impacts of parametric and radar rainfall uncertainty on the ensemble streamflow simulations of a distributed hydrologic model. J. Hydrol., 298, 202–221, doi:10.1016/j.jhydrol.2004.03.036.

    • Search Google Scholar
    • Export Citation

  • Chandrasekar, V., Bringi V. N. , Balakrishnan N. , and Zrnić D. S. , 1990: Error structure of multiparameter radar and surface measurements of rainfall. Part III: Specific differential phase. J. Atmos. Oceanic Technol., 7, 621–629, doi:10.1175/1520-0426(1990)007<0621:ESOMRA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Chandrasekar, V., Bringi V. N. , Rutledge S. A. , Hou A. , Smith E. , Jackson G. S. , Gorgucci E. , and Petersen W. A. , 2008: Potential role of dual-polarization radar in the validation of satellite precipitation measurements: Rationale and opportunities. Bull. Amer. Meteor. Soc., 89, 1127–1145, doi:10.1175/2008BAMS2177.1.

    • Search Google Scholar
    • Export Citation

  • Chandrasekar, V., Keranen R. , Lim S. , and Moisseev D. , 2013: Recent advances in classification of observations from dual polarization weather radars. Atmos. Res., 119, 97–111, doi:10.1016/j.atmosres.2011.08.014.

    • Search Google Scholar
    • Export Citation

  • Ciach, G. J., 2003: Local random errors in tipping-bucket rain gauge measurements. J. Atmos. Oceanic Technol., 20, 752–759, doi:10.1175/1520-0426(2003)20<752:LREITB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Ciach, G. J., Krajewski W. F. , and Villarini G. , 2007: Product-error-driven uncertainty model for probabilistic quantitative precipitation estimation with NEXRAD data. J. Hydrometeor., 8, 1325–1347, doi:10.1175/2007JHM814.1.

    • Search Google Scholar
    • Export Citation

  • Cifelli, R., Chandrasekar V. , Lim S. , Kennedy P. C. , Wang Y. , and Rutledge S. A. , 2011: A new dual-polarization radar rainfall algorithm: Application in Colorado precipitation events. J. Atmos. Oceanic Technol., 28, 352–364, doi:10.1175/2010JTECHA1488.1.

    • Search Google Scholar
    • Export Citation

  • Crosson, W. L., Duchon C. E. , Raghavan R. , and Goodman S. J. , 1996: Assessment of rainfall estimates using a standard Z–R relationship and the probability matching method applied to composite radar data in central Florida. J. Appl. Meteor., 35, 1203–1219, doi:10.1175/1520-0450(1996)035<1203:AOREUA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Cunha, L. K., Krajewski W. F. , Mantilla R. , and Cunha L. , 2011: A framework for flood risk assessment under nonstationary conditions or in the absence of historical data. J. Flood Risk Manage., 4, 3–22, doi:10.1111/j.1753-318X.2010.01085.x.

    • Search Google Scholar
    • Export Citation

  • Cunha, L. K., Mandapaka P. V. , Krajewski W. F. , Mantilla R. , and Bradley A. A. , 2012: Impact of radar-rainfall error structure on estimated flood magnitude across scales: An investigation based on a parsimonious distributed hydrological model. Water Resour. Res., 48, W10515, doi:10.1029/2012WR012138.

    • Search Google Scholar
    • Export Citation

  • Cunha, L. K., Smith J. A. , Baeck M. L. , and Krajewski W. F. , 2013: An early performance evaluation of the NEXRAD dual-polarization radar rainfall estimates for urban flood applications. Wea. Forecasting, 28, 1478–1497, doi:10.1175/WAF-D-13-00046.1.

    • Search Google Scholar
    • Export Citation

  • D’Adderio, L. P., Porcù F. , and Tokay A. , 2015: Identification and analysis of collisional break-up in natural rain. J. Atmos. Sci., doi:10.1175/JAS-D-14-0304.1, in press.

    • Search Google Scholar
    • Export Citation

  • Frasson, R. P. de M., da Cunha L. K. , and Krajewski W. F. , 2011: Assessment of the Thies optical disdrometer performance. Atmos. Res., 101, 237–255, doi:10.1016/j.atmosres.2011.02.014.

    • Search Google Scholar
    • Export Citation

  • Fulker, D., Bates S. , and Jacobs C. , 1997: Unidata: A virtual community sharing resources via technological infrastructure. Bull. Amer. Meteor. Soc., 78, 457–468, doi:10.1175/1520-0477(1997)078<0457:UAVCSR>2.0.CO;2.

    • 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, doi:10.1175/1520-0434(1998)013<0377:TWRA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Fulton, R. A., Ding F. , and Miller D. , 2003: Truncation errors in historical WSR-88D rainfall products. Preprints, 31st Conf. on Radar Meteorology, Amer. Meteor. Soc., Seattle, WA, P2B.7. [Available online at https://ams.confex.com/ams/32BC31R5C/techprogram/paper_63261.htm.]

  • Giangrande, S. E., and Ryzhkov A. V. , 2008: Estimation of rainfall based on the results of polarimetric echo classification. J. Appl. Meteor., 47, 2445–2460, doi:10.1175/2008JAMC1753.1.

    • Search Google Scholar
    • Export Citation

  • Giangrande, S. E., Krause J. M. , and Ryzhkov A. , 2008: Automatic designation of the melting layer with a polarimetric prototype of the WSR-88D radar. J. Appl. Meteor. Climatol., 47, 1354–1364, doi:10.1175/2007JAMC1634.1.

    • Search Google Scholar
    • Export Citation

  • Glaudemans, M., Lawrence B. , and Tilles P. , 2009: Interactive quality control and operational product generation of hourly multi-sensor precipitation estimates in the NWS. 25th Conf. on Int. Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Amer. Meteor. Soc., New Orleans, LA, 5B.3. [Available online at https://ams.confex.com/ams/89annual/techprogram/paper_143861.htm.]

  • Hou, A. Y., and Coauthors, 2014: The Global Precipitation Measurement (GPM) mission. Bull. Amer. Meteor. Soc., 95, 701–722, doi:10.1175/BAMS-D-13-00164.1.

    • Search Google Scholar
    • Export Citation

  • Illingworth, A. J., Goddard J. W. F. , and Cherry S. M. , 1986: Detection of hail by dual-polarization radar. Nature, 320, 431–433, doi:10.1038/320431a0.

    • Search Google Scholar
    • Export Citation

  • Istok, M. J., Fresch M. , Jing Z. , and Smith S. , 2009: WSR-88D dual polarization initial operational capabilities. 25th Conf. on Int. Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Amer. Meteor. Soc., New Orleans, LA, 15.5. [Available online at https://ams.confex.com/ams/89annual/techprogram/paper_148927.htm.]

  • Kim, D., Nelson B. , and Seo D.-J. , 2009: Characteristics of Reprocessed Hydrometeorological Automated Data System (HADS) hourly precipitation data. Wea. Forecasting, 24, 1287–1296, doi:10.1175/2009WAF2222227.1.

    • Search Google Scholar
    • Export Citation

  • Kitzmiller, D., Miller D. , Fulton R. , and Ding F. , 2013: Radar and multisensor precipitation estimation techniques in National Weather Service hydrologic operations. J. Hydrol. Eng., 18, 133–142, doi:10.1061/(ASCE)HE.1943-5584.0000523.

    • Search Google Scholar
    • Export Citation

  • Krajewski, W. F., and Smith J. A. , 2002: Radar hydrology: Rainfall estimation. Adv. Water Resour., 25, 1387–1394, doi:10.1016/S0309-1708(02)00062-3.

    • Search Google Scholar
    • Export Citation

  • Krajewski, W. F., Kruger A. , Smith J. A. , Lawrence R. , and Gunyon C. , 2011: Towards better utilization of NEXRAD data in hydrology: An overview of Hydro-NEXRAD. J. Hydroinf., 13, 255–266, doi:10.2166/hydro.2010.056.

    • Search Google Scholar
    • Export Citation

  • Krajewski, W. F., Kruger A. , Singh S. , Seo B.-C. , and Smith J. A. , 2013: Hydro-NEXRAD-2: Real-time access to customized radar-rainfall for hydrologic applications. J. Hydroinf., 15, 580–590, doi:10.2166/hydro.2012.227.

    • Search Google Scholar
    • Export Citation

  • Kruger, A., Krajewski W. F. , Domaszczynski P. , and Smith J. A. , 2011: Hydro-NEXRAD: Metadata computation and use. J. Hydroinf., 13, 267–276, doi:10.2166/hydro.2010.057.

    • Search Google Scholar
    • Export Citation

  • Lawrence, B., Shebsovich M. , Glaudeman M. , and Tilles P. , 2003: Enhancing precipitation estimation capabilities at National Weather Service field offices using multisensor precipitation data mosaics. 19th Conf. on Interactive Information Processing Systems, Long Beach, VA, Amer. Meteor. Soc., 15.1. [Available online at https://ams.confex.com/ams/annual2003/techprogram/paper_54867.htm.]

  • Lin, Y., and Mitchell K. E. , 2005: The NCEP Stage II/IV hourly precipitation analyses: Development and applications. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2. [Available online at https://ams.confex.com/ams/Annual2005/techprogram/paper_83847.htm.]

  • Liu, H., and Chandrasekar V. , 2000: Classification of hydrometeors based on polarimetric radar measurements: Development of fuzzy logic and neuro-fuzzy systems, and in situ verification. J. Atmos. Oceanic Technol., 17, 140–164, doi:10.1175/1520-0426(2000)017<0140:COHBOP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Mandapaka, P. V., Krajewski W. F. , Mantilla R. , and Gupta V. K. , 2009: Dissecting the effect of rainfall variability on the statistical structure of peak flows. Adv. Water Resour., 32, 1508–1525, doi:10.1016/j.advwatres.2009.07.005.

    • Search Google Scholar
    • Export Citation

  • Mantilla, R., and Gupta V. K. , 2005: A GIS numerical framework to study the process basis of scaling statistics in river networks. IEEE Geosci. Remote Sens. Lett., 2, 404–408, doi:10.1109/LGRS.2005.853571.

    • Search Google Scholar
    • Export Citation

  • McKee, T. B., Doesken N. J. , Davey C. A. , and Pielke R. A. Sr., 2000: Climate data continuity with ASOS: Report for period April 1996 through June 2000. Climatology Rep. 00-3, Colorado State University, Fort Collins, CO, 82 pp. [Available online at http://climate.atmos.colostate.edu/pdfs/climatologyreport-00-3.pdf.]

  • Milewska, E., and Hogg W. D. , 2002: Continuity of climatological observations with automation-temperature and precipitation amounts from AWOS (Automated Weather Observing System). Atmos.–Ocean,40, 333–359, doi:10.3137/ao.400304.

  • Miller, D. A., Wu S. , and Kitzmiller D. , 2013: Spatial and temporal resolution considerations in evaluating and utilizing radar quantitative precipitation estimates. J. Oper. Meteor., 1, 168–184, doi:10.15191/nwajom.2013.0115.

    • Search Google Scholar
    • Export Citation

  • Nelson, B., Seo D.-J. , and Kim D. , 2010: Multisensor precipitation reanalysis. J. Hydrometeor., 11, 666–682, doi:10.1175/2010JHM1210.1.

    • Search Google Scholar
    • Export Citation

  • O’Bannon, T., and Ding F. , 2003: Continuing enhancement of the WSR-88D precipitation pre-processing subsystem. Preprints, 31st Int. Conf. on Radar Meteorology, Seattle, WA, Amer. Meteor. Soc., P3C.10. [Available online at https://ams.confex.com/ams/32BC31R5C/techprogram/paper_63158.htm.]

  • Park, H. S., Ryzhkov A. V. , and Zrnić D. S. , 2009: The hydrometeor classification algorithm for the polarimetric WSR-88D: Description and application to an MCS. Wea. Forecasting, 24, 730–748, doi:10.1175/2008WAF2222205.1.

    • Search Google Scholar
    • Export Citation

  • Pereira Fo., A. J., Crawford K. C. , and Hartzell C. L. , 1998: Improving WSR-88D hourly rainfall estimates. Wea. Forecasting, 13, 1016–1028, doi:10.1175/1520-0434(1998)013<1016:IWHRE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Petersen, W. A., and Krajewski W. , 2013: Status update on the GPM Ground Validation Iowa Flood Studies (IFloodS) field experiment. Geophysical Research Abstracts, Vol. 15, Abstract 13345. [Available online at http://meetingorganizer.copernicus.org/EGU2013/EGU2013-13345.pdf.]

    • Search Google Scholar
    • Export Citation

  • Price, K., Purucker S. T. , and Kraemer S. R. , 2014: Comparison of radar and gauge precipitation data in watershed models across varying spatial and temporal scales. Hydrol. Processes, 28, 3505–3520, doi:10.1002/hyp.9890.

    • Search Google Scholar
    • Export Citation

  • Reed, S. M., and Maidment D. R. , 1999: Coordinate transformations for using NEXRAD data in GIS-based hydrologic modeling. J. Hydrol. Eng., 4, 174–182, doi:10.1061/(ASCE)1084-0699(1999)4:2(174).

    • Search Google Scholar
    • Export Citation

  • Ryzhkov, A. V., and Zrnić D. S. , 1996: Polarimetric method for ice water content determination. Proc. 1996 Int. Geoscience and Remove Sensing Symp., Vol. 1, Lincoln, NE, IEEE, 557–559, doi:10.1109/IGARSS.1996.516402.

  • Ryzhkov, A. V., Giangrande S. E. , Melnikov V. M. , and Schuur T. J. , 2005a: Calibration issues of dual-polarization radar measurements. J. Atmos. Oceanic Technol., 22, 1138–1155, doi:10.1175/JTECH1772.1.

    • Search Google Scholar
    • Export Citation

  • Ryzhkov, A. V., Giangrande S. E. , and Schuur T. J. , 2005b: Rainfall estimation with a polarimetric prototype of WSR-88D. J. Appl. Meteor., 44, 502–515, doi:10.1175/JAM2213.1.

    • Search Google Scholar
    • Export Citation

  • Ryzhkov, A. V., Schuur T. J. , Burgess D. W. , Heinselman P. L. , Giangrande S. E. , and Zrnić D. S. , 2005c: The Joint Polarization Experiment: Polarimetric rainfall measurements and hydrometeor classification. Bull. Amer. Meteor. Soc., 86, 809–824, doi:10.1175/BAMS-86-6-809.

    • Search Google Scholar
    • Export Citation

  • Ryzhkov, A. V., Diederich M. , Zhang P. , and Simmer C. , 2014: Potential utilization of specific attenuation for rainfall estimation, mitigation of partial beam blockage, and radar networking. J. Atmos. Oceanic Technol., 31, 599–619, doi:10.1175/JTECH-D-13-00038.1.

    • Search Google Scholar
    • Export Citation

  • Sachidananda, M., and Zrnić D. S. , 1987: Rain rate estimates from differential polarization measurements. J. Atmos. Oceanic Technol., 4, 588–598, doi:10.1175/1520-0426(1987)004<0588:RREFDP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Seliga, T. A., and Bringi V. N. , 1976: Potential use of radar differential reflectivity measurements at orthogonal polarizations for measuring precipitation. J. Appl. Meteor., 15, 69–76, doi:10.1175/1520-0450(1976)015<0069:PUORDR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Seo, B.-C., and Krajewski W. F. , 2010: Scale dependence of radar rainfall uncertainty: Initial evaluation of NEXRAD’s new super-resolution data for hydrologic applications. J. Hydrometeor., 11, 1191–1198, doi:10.1175/2010JHM1265.1.

    • Search Google Scholar
    • Export Citation

  • Seo, B.-C., Krajewski W. F. , Kruger A. , Domaszczynski P. , Smith J. A. , and Steiner M. , 2011: Radar-rainfall estimation algorithms of Hydro-NEXRAD. J. Hydroinf., 13, 277–291, doi:10.2166/hydro.2010.003.

    • Search Google Scholar
    • Export Citation

  • Seo, B.-C., Cunha L. K. , and Krajewski W. F. , 2013: Uncertainty in radar-rainfall composite and its impact on hydrologic prediction for the eastern Iowa flood of 2008. Water Resour. Res., 49, 2747–2764, doi:10.1002/wrcr.20244.

    • Search Google Scholar
    • Export Citation

  • Seo, B.-C., Krajewski W. F. , Cunha L. K. , Dolan B. , Smith J. A. , Rutledge S. , and Petersen W. , 2014: Comprehensive evaluation of the IFloodS precipitation datasets for hydrologic applications. Ninth Int. Symp. on Weather Radar and Hydrology, Washington, D.C., Environmental and Water Resources Institute, 1.49.

    • Search Google Scholar
    • Export Citation

  • Seo, D.-J., Seed A. , and Delrieu G. , 2010: Radar and multisensor rainfall estimation for hydrologic applications. Rainfall: State of the Science, Geophys. Monogr., Vol. 191, Amer. Geophys. Union, 79–104, doi:110.1029/2010GM000952.

    • Search Google Scholar
    • Export Citation

  • Sherretz, L. A., and Fulker D. W. , 1988: Unidata: Enabling universities to acquire and analyze scientific data. Bull. Amer. Meteor. Soc., 69, 373–376, doi:10.1175/1520-0477(1988)069<0373:UEUTAA>2.0.CO;2.

    • 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, 2035–2045, doi:10.1029/96WR00270.

    • Search Google Scholar
    • Export Citation

  • Super, A., and Holroyd E. , 1997: Snow accumulation algorithm for the WSR-88D radar. Proc. 28th Conf. on Radar Meteorology, Vail, CO, Amer. Meteor. Soc., 324–325.

  • Tabary, P., Boumahmoud A.-A. , Andrieu H. , Thompson R. J. , Illingworth A. J. , Bouar E. L. , and Testud J. , 2011: Evaluation of two “integrated” polarimetric Quantitative Precipitation Estimation (QPE) algorithms at C-band. J. Hydrol., 405, 248–260, doi:10.1016/j.jhydrol.2011.05.021.

    • Search Google Scholar
    • Export Citation

  • Tapiador, F. J., and Coauthors, 2012: Global precipitation measurement: Methods, datasets and applications. Atmos. Res., 104–105, 70–97, doi:10.1016/j.atmosres.2011.10.021.

    • Search Google Scholar
    • Export Citation

  • Torres, S. M., and Curtis C. D. , 2007: Initial implementation of super-resolution data on the NEXRAD network. 23rd Conf. on Int. Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, San Antonio, TX, Amer. Meteor. Soc., 5B.10. [Available online at http://ams.confex.com/ams/87ANNUAL/techprogram/paper_116240.htm.]

  • Villarini, G., and Krajewski W. F. , 2010: Review of the different sources of uncertainty in single polarization radar-based estimates of rainfall. Surv. Geophys., 31, 107–129, doi:10.1007/s10712-009-9079-x.

    • Search Google Scholar
    • Export Citation

  • Vulpiani, G., and Giangrande S. , 2009: Rainfall estimation from polarimetric S-band radar measurements: Validation of a neural network approach. J. Appl. Meteor. Climatol., 48, 2022–2036, doi:10.1175/2009JAMC2172.1.

    • Search Google Scholar
    • Export Citation

  • Wilson, J. W., and Brandes E. A. , 1979: Radar measurement of rainfall—A summary. Bull. Amer. Meteor. Soc., 60, 1048–1058, doi:10.1175/1520-0477(1979)060<1048:RMORS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wu, W., Kitzmiller D. , and Wu S. , 2012: Evaluation of radar precipitation estimates from the National Mosaic and Quantitative Precipitation Estimation System and the WSR-88D precipitation processing system over the conterminous United States. J. Hydrometeor., 13, 1080–1093, doi:10.1175/JHM-D-11-064.1.

    • 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, Y., Reed S. , and Kitzmiller D. , 2011: Effects of retrospective gauge-based readjustment of multi-sensor precipitation estimates on hydrologic simulations. J. Hydrometeor., 12, 429–443, doi:10.1175/2010JHM1200.1.

    • Search Google Scholar
    • Export Citation

  • Zrnić, D., Doviak R. , Zhang G. , and Ryzhkov A. , 2010: Bias in differential reflectivity due to cross coupling through the radiation patterns of polarimetric weather radars. J. Atmos. Oceanic Technol., 27, 1624–1637, doi:10.1175/2010JTECHA1350.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 3186 2677 28
PDF Downloads 296 78 5