Theoretical and Applied Climatology

, Volume 112, Issue 1–2, pp 317–338 | Cite as

Fuzzy logic based melting layer recognition from 3 GHz dual polarization radar: appraisal with NWP model and radio sounding observations

  • Tanvir Islam
  • Miguel A. Rico-Ramirez
  • Dawei Han
  • Michaela Bray
  • Prashant K. Srivastava
Original Paper


The advent of polarimetry makes it possible to categorize hydrometeor inferences more accurately by providing detailed information of the scattering properties. In light of this, the authors have developed a fuzzy logic based system for the recognition of melting layer in the atmosphere. The fuzzy system is based on characterizing melting layer scatterers from non-melting scatterers using five crisp inputs, namely, horizontal reflectivity (Z H), differential reflectivity (Z DR), co-polar correlation coefficient (ρ HV), linear depolarization ratio (LDR) and height of radar measurements (H). For the implementation of melting layer recognition, the study employs the dual polarized signatures from the 3 GHz Chilbolton Advanced Meteorological Radar (CAMRA). Furthermore, a simple but effective averaging procedure for melting level estimation from a volume RHI scan is proposed. The proposed scheme has been evaluated with Weather Research and Forecasting (WRF) model simulated and radio soundings retrieved melting level height over a total of 84 RHI scan-based bright band cases. The results confirm that the estimated melting level heights from the proposed method are in good agreement with the WRF model and radio sounding observations. The 3 GHz radar melting level height estimates correspond with the R 2 and RMSE values of 0.92 and 0.24 km, respectively, when compared to the radio soundings, and 0.93 and 0.21 km, respectively, when compared to the WRF model results. Moreover, the related R 2 and RMSE values are reported as 0.93 and 0.22 km respectively between the WRF and radio soundings retrievals. This implies that the downscaled WRF modelled melting level height may also be used for operational or research needs.



The authors would like to thank the British Atmospheric Data Centre and the Radio Communications Research Unit at the STFC Rutherford Appleton Laboratory for providing the radar and radio soundings data. The FNL data for this study are from the Research Data Archive (RDA) which is maintained by the Computational and Information Systems Laboratory (CISL) at the National Center for Atmospheric Research (NCAR). NCAR is sponsored by the National Science Foundation (NSF). The original data are available from the RDA ( in dataset number ds083.2.


  1. Austin PM, Bemis AC (1950) A quantitative study of the bright band in radar precipitation echoes. J Meteorol 7(2):145–151CrossRefGoogle Scholar
  2. Baldini L, Gorgucci E (2006) Identification of the melting layer through dual-polarization radar measurements at vertical incidence. J Atmos Ocean Technol 23(6):829–839CrossRefGoogle Scholar
  3. Bellon A, Lee G, Zawadzki I (2005) Error statistics of VPR corrections in stratiform precipitation. J Appl Meteorol 44(7):998–1015CrossRefGoogle Scholar
  4. Beswick KM, Gallagher MW, Webb AR, Norton EG, Perry F (2008) Application of the Aventech AIMMS20AQ airborne probe for turbulence measurements during the Convective Storm Initiation Project. Atmos Chem Phys 8(17):5449–5463CrossRefGoogle Scholar
  5. Boodoo S, Hudak D, Donaldson N, Leduc M (2010) Application of dual-polarization radar melting-layer detection algorithm. J Appl Meteorol Climatol 49(8):1779–1793. doi: 10.1175/2010jamc2421.1 CrossRefGoogle Scholar
  6. Brandes EA, Ikeda K (2004) Freezing-level estimation with polarimetric radar. J Appl Meteorol 43(11):1541–1553CrossRefGoogle Scholar
  7. Bray M, Han DW, Xuan YQ, Bates P, Williams M (2011) Rainfall uncertainty for extreme events in NWP downscaling model. Hydrol Process 25(9):1397–1406. doi: 10.1002/hyp. 7905 CrossRefGoogle Scholar
  8. Bringi V, Chandrasekar V (2001) Polarimetric Doppler weather radar: principles and applications. Cambridge University Press, NYGoogle Scholar
  9. Cifelli R, Chandrasekar V, Lim S, Kennedy PC, Wang Y, Rutledge SA (2011) A new dual-polarization radar rainfall algorithm: application in Colorado precipitation events. J Atmos Ocean Technol 28(3):352–364. doi: 10.1175/2010jtecha1488.1 CrossRefGoogle Scholar
  10. Defer E, Prigent C, Aires F, Pardo JR, Walden CJ, Zanife OZ, Chaboureau JP, Pinty JP (2008) Development of precipitation retrievals at millimeter and sub-millimeter wavelengths for geostationary satellites. J Geophys Res-Atmos 113(D8):doi: D08111 CrossRefGoogle Scholar
  11. Dudhia J (1993) A nonhydrostatic version of the penn state ncar mesoscale model - validation tests and simulation of an atlantic cyclone and cold-front. Mon Weather Rev 121(5):1493–1513. doi: 10.1175/1520-0493(1993) 121<1493:anvotp>;2 CrossRefGoogle Scholar
  12. El-Magd A, Chandrasekar V, Bringi VN, Strapp W (2000) Multiparameter radar and in situ aircraft observation of graupel and hail. IEEE Trans Geosci Remote Sens 38(1):570–578. doi: 10.1109/36.823951 CrossRefGoogle Scholar
  13. Evaristo R, Scialom G, Viltard N, Lemaitre Y (2010) Polarimetric signatures and hydrometeor classification of West African squall lines. Q J R Meteorol Soc 136:272–288. doi: 10.1002/qj.561 CrossRefGoogle Scholar
  14. Fabry F, Zawadzki T (1995) Long-term radar observations of the melting layer of precipitation and their interpretation. J Atmos Sci 52(7):838–851CrossRefGoogle Scholar
  15. Germann U, Joss J (2002) Mesobeta profiles to extrapolate radar precipitation measurements above the Alps to the ground level. J Appl Meteorol 41(5):542–557CrossRefGoogle Scholar
  16. Giangrande SE, Krause JM, Ryzhkov AV (2008) Automatic designation of the melting layer with a polarimetric prototype of the WSR-88D radar. J Appl Meteorol Climatol 47(5):1354–1364. doi: 10.1175/2007jamc1634.1 CrossRefGoogle Scholar
  17. Goddard JWF, Eastment JD, Thurai M (1994) The chilbolton advanced meteorological radar—a tool for multidisciplinary atmospheric research. Electron Commun Eng J 6(2):77–86. doi: 10.1049/ecej:19940205 CrossRefGoogle Scholar
  18. Gourley JJ, Calvert CM (2003) Automated detection of the bright band using WSR-88D data. Weather Forecast 18(4):585–599. doi: 10.1175/1520-0434(2003) 018<0585:adotbb>;2 CrossRefGoogle Scholar
  19. Gray WR, Uddstrom MJ, Larsen HR (2002) Radar surface rainfall estimates using a typical shape function approach to correct for the variations in the vertical profile of reflectivity. Int J Remote Sens 23(12):2489–2504. doi: 10.1080/01431160110070834 CrossRefGoogle Scholar
  20. Heinselman PL, Ryzhkov AV (2006) Validation of polarimetric hail detection. Weather Forecast 21(5):839–850. doi: 10.1175/waf956.1 CrossRefGoogle Scholar
  21. Hong SY, Dudhia J, Chen SH (2004) A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon Weather Rev 132(1):103–120. doi: 10.1175/1520-0493(2004) 132<0103:aratim>;2 CrossRefGoogle Scholar
  22. Hong SY, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev 134(9):2318–2341. doi: 10.1175/mwr3199.1 CrossRefGoogle Scholar
  23. Ikeda K, Brandes EA, Rasmussen RM (2005) Polarimetric radar observation of multiple freezing levels. J Atmos Sci 62(10):3624–3636. doi: 10.1175/jas3556.1 CrossRefGoogle Scholar
  24. Ishak AM, Bray M, Remesan R, Han DW (2010) Estimating reference evapotranspiration using numerical weather modelling. Hydrol Process 24(24):3490–3509. doi: 10.1002/hyp. 7770 CrossRefGoogle Scholar
  25. Islam T, Rico-Ramirez MA, Han D (2012a) Tree-based genetic programming approach to infer microphysical parameters of the DSDS from the polarization diversity measurements. Computers & Geosciences. doi: 10.1016/j.cageo.2012.05.028
  26. Islam T, Rico-Ramirez MA, Han D, Srivastava PK (2012b) Artificial intelligence techniques for clutter identification with polarimetric radar signatures. Atmos Res 109–110:95–113. doi: 10.1016/j.atmosres.2012.02.007 CrossRefGoogle Scholar
  27. Islam T, Rico-Ramirez MA, Han D, Srivastava PK (2012c) A Joss–Waldvogel disdrometer derived rainfall estimation study by collocated tipping bucket and rapid response rain gauges. Atmos Sci Lett 13(2):139–150. doi: 10.1002/asl.376 CrossRefGoogle Scholar
  28. Islam T, Rico-Ramirez MA, Han D, Srivastava PK, Ishak AM (2012d) Performance evaluation of the TRMM precipitation estimation using ground-based radars from the GPM validation network. J Atmos Solar-Terr Phys 77:194–208. doi: 10.1016/j.jastp. 2012.01.001 CrossRefGoogle Scholar
  29. Islam T, Rico-Ramirez MA, Thurai M, Han D (2012e) Characteristics of raindrop spectra as normalized gamma distribution from a Joss–Waldvogel disdrometer. Atmos Res 108:57–73. doi: 10.1016/j.atmosres.2012.01.013 CrossRefGoogle Scholar
  30. Joss J, Waldvogel A (1990) Precipitation measurement and hydrology. Radar in meteorology(A 90-39376 17-47). American Meteorological Society, Boston, pp 577–606Google Scholar
  31. Kain JS (2004) The Kain-Fritsch convective parameterization: an update. J Appl Meteorol 43(1):170–181. doi: 10.1175/1520-0450(2004) 043<0170:tkcpau>;2 CrossRefGoogle Scholar
  32. Kitchen M, Brown R, Davies AG (1994) Real-time correction of weather radar data for the effects of bright band, range and orographic growth in widespread precipitation. Q J R Meteorol Soc 120(519):1231–1254. doi: 10.1256/smsqj.51905 CrossRefGoogle Scholar
  33. Liguori S, Rico-Ramirez MA, Schellart ANA, Saul AJ (2012) Using probabilistic radar rainfall nowcasts and NWP forecasts for flow prediction in urban catchments. Atmos Res 103:80–95. doi: 10.1016/j.atmosres.2011.05.004 CrossRefGoogle Scholar
  34. Lim S, Chandrasekar V, Bringi VN (2005) Hydrometeor classification system using dual-polarization radar measurements: model improvements and in situ verification. IEEE Trans Geosci Remote Sens 43(4):792–801. doi: 10.1109/tgrs.2004.843077 CrossRefGoogle Scholar
  35. Liu HP, 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 Ocean Technol 17(2):140–164CrossRefGoogle Scholar
  36. Liu J, Bray M, Han D (2011) Sensitivity of the Weather Research & Forecasting (WRF) model to downscaling ratios and storm types in rainfall simulation. Hydrol Process. doi: 10.1002/hyp. 8247
  37. Marzano FS, Scaranari D, Montopoli M, Vulpiani G (2008) Supervised classification and estimation of hydrometeors from C-band dual-polarized radars: a Bayesian approach. IEEE Trans Geosci Remote Sens 46(1):85–98. doi: 10.1109/tgrs.2007.906476 CrossRefGoogle Scholar
  38. Matrosov SY (2004) Depolarization estimates from linear H and V measurements with weather radars operating in simultaneous transmission-simultaneous receiving mode. J Atmos Ocean Technol 21(4):574–583. doi:10.1175/1520-0426(2004) 021<0574:deflha>;2CrossRefGoogle Scholar
  39. Matrosov SY, Clark KA, Kingsmill DE (2007) A polarimetric radar approach to identify rain, melting-layer, and snow regions for applying corrections to vertical profiles of reflectivity. J Appl Meteorol Climatol 46(2):154–166. doi: 10.1175/jam2508.1 CrossRefGoogle Scholar
  40. Mittermaier MP, Illingworth AJ (2003) Comparison of model-derived and radar-observed freezing-level heights: implications for vertical reflectivity profile-correction schemes. Q J R Meteorol Soc 129(587):83–95. doi: 10.1256/qj.02.19 CrossRefGoogle Scholar
  41. Park H, Ryzhkov AV, Zrnic DS, Kim KE (2009) The hydrometeor classification algorithm for the polarimetric WSR-88D: description and application to an MCS. Weather Forecast 24(3):730–748. doi: 10.1175/2008waf2222205.1 CrossRefGoogle Scholar
  42. Rico-Ramirez MA, Cluckie ID (2007) Bright-band detection from radar vertical reflectivity profiles. Int J Remote Sens 28(18):4013–4025CrossRefGoogle Scholar
  43. Rico-Ramirez MA, Cluckie ID (2008) Classification of ground clutter and anomalous propagation using dual-polarization weather radar. IEEE Trans Geosci Remote Sens 46(7):1892–1904. doi: 10.1109/tgrs.2008.916979 CrossRefGoogle Scholar
  44. Rico-Ramirez MA, Cluckie ID, Han D (2005) Correction of the bright band using dual-polarisation radar. Atmos Sci Lett 6(1):40–46. doi: 10.1002/asl.89 CrossRefGoogle Scholar
  45. Rico-Ramirez MA, Cluckie ID, Shepherd G, Pallot A (2007) A high-resolution radar experiment on the island of Jersey. Meteorol Appl 14(2):117–129. doi: 10.1002/met.13 CrossRefGoogle Scholar
  46. Ryzhkov AV, Zrnic DS (1998) Discrimination between rain and snow with a polarimetric radar. J Appl Meteorol 37(10):1228–1240CrossRefGoogle Scholar
  47. Sanchez-Diezma R, Zawadzki I, Sempere-Torres D (2000) Identification of the bright band through the analysis of volumetric radar data. J Geophys Res-Atmos 105(D2):2225–2236CrossRefGoogle Scholar
  48. Skamarock WC, Klemp JB (1992) The stability of time-split numerical-methods for the hydrostatic and the nonhydrostatic elastic equations. Mon Weather Rev 120(9):2109–2127. doi: 10.1175/1520-0493(1992) 120<2109:tsotsn>;2 CrossRefGoogle Scholar
  49. Szyrmer W, Zawadzki I (1999) Modeling of the melting layer. Part I: dynamics and microphysics. J Atmos Sci 56(20):3573–3592CrossRefGoogle Scholar
  50. Testud J, Le Bouar E, Obligis E, Ali-Mehenni M (2000) The rain profiling algorithm applied to polarimetric weather radar. J Atmos Ocean Technol 17(3):332–356. doi: 10.1175/1520-0426(2000) 017<0332:trpaat>;2 CrossRefGoogle Scholar
  51. Vignal B, Krajewski WF (2001) Large-sample evaluation of two methods to correct range-dependent error for WSR-88D rainfall estimates. J Hydrometeorol 2(5):490–504CrossRefGoogle Scholar
  52. Vivekanandan J, Zrnic DS, Ellis SM, Oye R, Ryzhkov AV, Straka J (1999) Cloud microphysics retrieval using S-band dual-polarization radar measurements. Bull Amer Meteorol Soc 80(3):381–388. doi: 10.1175/1520-0477(1999) 080<0381:cmrusb>;2 CrossRefGoogle Scholar
  53. Wexler R, Atlas D (1956) Factors influencing radar-echo intensities in the melting layer. Q J R Meteorol Soc 82(353):349–351CrossRefGoogle Scholar
  54. White AB, Gottas DJ, Strem ET, Ralph FM, Neiman PJ (2002) An automated brightband height detection algorithm for use with Doppler radar spectral moments. J Atmos Ocean Technol 19(5):687–697CrossRefGoogle Scholar
  55. Zhang J, Langston C, Howard K (2008) Brightband identification based on vertical profiles of reflectivity from the WSR-88D. J Atmos Ocean Technol 25(10):1859–1872. doi: 10.1175/2008jtecha1039.1 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Tanvir Islam
    • 1
  • Miguel A. Rico-Ramirez
    • 1
  • Dawei Han
    • 1
  • Michaela Bray
    • 1
    • 2
  • Prashant K. Srivastava
    • 1
  1. 1.Department of Civil EngineeringUniversity of BristolBristolUK
  2. 2.Hydro-Environmental Research Centre, Cardiff School of EngineeringCardiff UniversityCardiffUK

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