Quality control of weather radar data using polarimetric variables

Journal of Atmospheric and Oceanic Technology

Volumn 31, Issue 6, 2014, Pages 1234-1249

  Lakshmanan, Valliappa ^a   Karstens, Christopher ^a   Krause, John ^a   Tang, Lin ^a  

^a UNIVERSITY OF OKLAHOMA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

POLARIMETERS; PROBABILITY; QUALITY CONTROL; REFLECTION;

CORRELATION COEFFICIENT; DIFFERENTIAL REFLECTIVITY; HISTORICAL DATA; OPERATIONAL DEPLOYMENTS; PATTERN VECTORS; POLARIMETRIC DATA; POLARIMETRIC VARIABLE; WEATHER RADAR DATA;

METEOROLOGICAL RADAR;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; DATA QUALITY; DATA SET; DOPPLER RADAR; PROBABILITY; QUALITY CONTROL;

UNITED STATES;

EID: 84902138531     PISSN: 07390572     EISSN: 15200426     Source Type: Journal    
DOI: 10.1175/JTECH-D-13-00073.1     Document Type: Article

References (39)

1. Berenguer, M., D. Sempere-Torres, C. Corral, and R. Sanchez-Diezma, 2006: A fuzzy logic technique for identifying nonprecipitating echoes in radar scans. J. Atmos. Oceanic Technol., 23, 1157-1180, doi:10.1175/JTECH1914.1.
2. Bishop, C. M., 1995: Neural Networks for Pattern Recognition. Oxford University Press, 512 pp.
3. Chandrasekar, V., R. Kernen, S. Lin, and D. Moisseev, 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.
4. Chanthavong, V., J. Holmes, R. Keranen, D. Paris, J. Selzler, A. Siggia, and T. Stordell, 2010: Mitigation of sea clutter and other nonstationary echoes based on general purpose polarimetric echo identification. Proc. Sixth European Conf. on Radar in Meteorology and Hydrology, Sibiu, Romania, National Meteorological Administration, P3.7. [Available online at http://www.erad2010.org/pdf/POSTER/03_Quality/ 07_ERAD2010_0307.pdf.]
5. Charalampidis, D., T. Kasparis, and W. Jones, 2002: Removal of nonprecipitation echoes in weather radar using multifractals and intensity. IEEE Trans. Geosci. Remote Sens., 40, 1121-1132, doi:10.1109/TGRS.2002.1010899.
6. da Silveira, R. B., and A. R. Holt, 2001: An automatic identification of clutter and anomalous propagation in polarization-diversity weather radar data using neural networks. IEEE Trans. Geosci. Remote Sens., 39, 1777-1788, doi:10.1109/36.942556.
7. Efron, B., 1979: Bootstrap methods: Another look at the jackknife. Ann. Stat., 7, 1-26, doi:10.1214/aos/1176344552.
8. Figueras i Ventura, J., and P. Tabary, 2013: The new French operational polarimetric radar rainfall rate product. J. Appl.Meteor. Climatol., 52, 1817-1835, doi:10.1175/JAMC-D-12-0179.1.
9. Friedrich, K., M. Hagen, and T. Einfalt, 2006: A quality control concept for radar reflectivity, polarimetric parameters, and Doppler velocity. J. Atmos. Oceanic Technol., 23, 865-887, doi:10.1175/JTECH1920.1.
10. Fulton, R., D. Breidenback, D. Miller, and T. O'Bannon, 1998: The WSR-88D rainfall algorithm. Wea. Forecasting, 13, 377-395, doi:10.1175/1520-0434(1998)013,0377:TWRA.2.0.CO;2.
11. Giuli, D., M. Gherardelli, A. Freni, T. A. Seliga, and K. Aydin, 1991: Rainfall and clutter discrimination by means of duallinear polarization radar measurements. J. Atmos. Oceanic Technol., 8, 777-789, doi:10.1175/1520-0426(1991)008,0777:RACDBM.2.0.CO;2.
12. Gourley, J. J., P. Tabary, and J. P. du Chatelet, 2007: A fuzzy logic algorithm for the separation of precipitating from nonprecipitating echoes using polarimetric radar observations. J. Atmos. Oceanic Technol., 24, 1439-1451, doi:10.1175/JTECH2035.1.
13. Grecu,M., andW. Krajewski, 2000: An efficient methodology for detection of anomalous propagation echoes in radar reflectivity data using neural networks. J. Atmos. Oceanic Technol., 17, 121-129, doi:10.1175/1520-0426(2000)017,0121:AEMFDO.2.0.CO;2.
14. Heidke, P., 1926: Berechnung des erfolges und der gute der windstarkvorhersagen im sturmwarnungsdienst. Geogr. Ann., 8, 301-349.
15. Holt, A., R. McGuinness, E. Torlaschi, and D. Bebbington, 1992:On the use of polarization-diversity to aid recognition of ground clutter and anomalous propagation in radar echoes of storms. Proc. Open Symp. on Wave Propagation and Remote Sensing, Ravenscar, North Yorkshire, United Kingdom, URSI, 2.4.1-2.4.5.
16. Islam, T., M. Rico-Ramirez, D. Han, and P. Srivastava, 2012: Artificial intelligence techniques for clutter identification with polarimetric radar signatures. Atmos. Res., 109-110, 95-113, doi:10.1016/j.atmosres.2012.02.007.
17. Istok, M. J., and Coauthors, 2009: WSR-88D dual polarization initial operational capabilities. Preprints, 25th Conf. on Int. Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Phoenix, AZ, Amer. Meteor. Soc., 15.5. [Available online at https://ams. confex.com/ams/89annual/techprogram/paper_148927.htm.]
18. Kessinger, C., S. Ellis, and J. Van Andel, 2003: The radar echo classifier:Afuzzy logic algorithm for the WSR-88D. Preprints, Third Conf. on Artificial Intelligence Applications to the Environmental Science, Long Beach, CA, Amer.Meteor. Soc., P1.6. [Available online at https://ams.confex.com/ams/annual2003/ techprogram/paper_54946.htm.]
19. Lakshmanan, V., 2012: Automating the Analysis of Spatial Grids: A Practical Guide to Data Mining Geospatial Images for Human and Environmental Applications. Geotechnologies and the Environment, Vol. 6, Springer, 320 pp.
20. Lakshmanan, V., A. Fritz, T. Smith, K. Hondl, and G. J. Stumpf, 2007a: An automated technique to quality control radar reflectivity data. J. Appl. Meteor. Climatol., 46, 288-305, doi:10.1175/JAM2460.1.
21. Lakshmanan, V., T. Smith, G. J. Stumpf, and K. Hondl, 2007b: The Warning Decision Support System-Integrated Information. Wea. Forecasting, 22, 596-612, doi:10.1175/WAF1009.1.
22. Lakshmanan, V., J. Zhang, and K. Howard, 2010: A technique to censor biological echoes in radar reflectivity data. J. Appl. Meteor. Climatol., 49, 435-462, doi:10.1175/2009JAMC2255.1.
23. Lakshmanan, V., J. Zhang, K. Hondl, and C. Langston, 2012:Astatistical approach to mitigating persistent clutter in radar reflectivity data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 5, 652-662, doi:10.1109/JSTARS.2011.2181828.
24. Liu, H., and V. Chandrasekar, 1998: An adaptive neural network scheme for precipitation estimation from radar observations. IGARSS '98: Sensing and Managing the Environment; 1998 IEEE International Geoscience and Remote Sensing Symposium Proceedings, T. I. Stein, Ed., Vol. 4, IEEE, 1895-1897, doi:10.1109/IGARSS.1998.703687.
25. Lucas, D. J., L. S. Goodman, and F. J. Fabozzi, 2006: Collateralized Debt Obligations: Structures and Analysis. Frank J. Fabozzi Series, Wiley Finance, 528 pp.
26. Lynn, R., and V. Lakshmanan, 2002: Virtual radar volumes: Creation, algorithm access, and visualization. Preprints, 21st Conf. on Severe Local Storms, San Antonio, TX, Amer. Meteor. Soc., P5.3. [Available online at https://ams.confex.com/ams/SLS_WAF_NWP/techprogram/paper_47546.htm.]
27. Ortega, K., T. Smith, K. Manross, K. Scharfenberg, A. Witt, A. Kolodziej, and J. Gourley, 2009: The severe hazards analysis and verification experiment. Bull. Amer. Meteor. Soc., 90, 1519-1530, doi:10.1175/2009BAMS2815.1.
28. Otsu, N., 1979: A threshold selection method from gray-level histograms. IEEE Trans. Syst.Man Cybern., 9, 62-66, doi:10.1109/TSMC.1979.4310076.
29. Park, H., A. Ryzhkov, D. Zrnić, and K. Kim, 2009: The hydrometeor classification algorithm for the polarimetric WSR-88D: Description and application to aMCS. Wea. Forecasting, 24, 730-748, doi:10.1175/2008WAF2222205.1.
30. Rico-Ramirez, M. A., and I. D. Cluckie, 2008: Classification of ground clutter and anomalous propagation using dualpolarization weather radar. IEEE Trans. Geosci. Remote Sens., 46, 1892-1904, doi:10.1109/TGRS.2008.916979.
31. Ryzhkov, A. V., 2007: The impact of beam broadening on the quality of radar polarimetric data. J. Atmos. Oceanic Technol., 24, 729-744, doi:10.1175/JTECH2003.1.
32. Ryzhkov, A. V., S. E. Giangrande, V. M. Melnikov, and T. J. Schuur, 2005: Calibration issues of dual-polarization radar measurements. J. Atmos. Oceanic Technol., 22, 1138-1155, doi:10.1175/JTECH1772.1.
33. Steiner, M., and J. Smith, 2002: Use of three-dimensional reflectivity structure for automated detection and removal of nonprecipitating echoes in radar data. J. Atmos. Oceanic Technol., 19, 673-686, doi:10.1175/1520-0426(2002)019,0673:UOTDRS.2.0.CO;2.
34. Stumpf, G., A. Witt, E. D. Mitchell, P. L. Spencer, J. T. Johnson, M. D. Eilts, K. W. Thomas, and D. W. Burgess, 1998: The National Severe Storms Laboratory mesocyclone detection algorithm for the WSR-88D. Wea. Forecasting, 13, 304-326, doi:10.1175/1520-0434(1998)013,0304:TNSSLM.2.0.CO;2.
35. Sun, J., and J. W. Wilson, 2003: The assimilation of radar data for weather prediction. Radar and Atmospheric Science: A Collection of Essays in Honor of David Atlas, Meteor. Monogr., No. 52, Amer. Meteor. Soc., 175-198, doi:10.1175/ 0065-9401(2003)030,0175:TAORDF.2.0.CO;2.
36. Zhang, J., S.Wang, andB. Clarke, 2004: WSR-88Dreflectivity quality control using horizontal and vertical reflectivity structure. Preprints, 11th Conf. on Aviation, Range, and Aerospace, Hyannis, MA, Amer. Meteor. Soc., P5.4. [Available online at https://ams. confex.com/ams/11aram22sls/techprogram/paper_81858.htm.]
37. Zrnić, D., and A. Ryzhkov, 1998: Observations of insects and birds with a polarimetric radar. IEEE Trans. Geosci. Remote Sens., 36, 661-668, doi:10.1109/36.662746.
38. Zrnić, D., and A. Ryzhkov, 1999: Polarimetry for weather surveillance radars. Bull. Amer. Meteor. Soc., 80, 389-406, doi:10.1175/ 1520-0477(1999)080,0389:PFWSR.2.0.CO;2.
39. Zrnić, D., and A. Ryzhkov, J. Straka, Y. Liu, and J. Vivekanandan, 2001: Testing a procedure for automatic classification of hydrometeor types. J. Atmos. Oceanic Technol., 18, 892-913, doi:10.1175/ 1520-0426(2001)018,0892:TAPFAC.2.0.CO;2.