HydroClass: A Synthesis of Public Methods

The HydroClass library is a collection of public echo identification methods resulting from major developments in the weather radar community. The main sources of origin are:

  • Meteo

    HydroClass results of fuzzy classification for the S- and C-band polarimetric weather radar echoes, evolved and field tested by the Colorado State University (CSU), Fort Collins, USA, and by their research partners. 1 2 3

  • Precip

    HydroClass results of echo and hydrometeor classifiers for the polarimetric version of the S-band WSR-88D, developed by the National Severe Storms Laboratory (NSSL), and evaluated for operational use in the Joint Polarimetric Experiment (JPOLE) and subsequent transition of the NEXRAD radar network into polarimetry. 4 5 6

  • Cell

    HydroClass results of analysis of the vertical structure precipitation echo, historical developments for single polarization radars. 7; originally the method intended for detecting hail events, here applied to detecting general cases of convective precipitation.

The implementation HydroClass is a synthesis that respects the integrity of each algorithm. Their best aspects are materialized through their sequential arrangement with:

  • Preclassifier (adapted from JPOLE)

    An echo classifier algorithm serves as a prior quality control to:

    • MeteoClassifiers (of CSU origin) and PrecipClassifier (of NSSL origin)

      Alternative methods of gate-to-gate hydrometeor classification

    • CellClassifier

      A weather pattern classifier of convection and stratiform rain used as a further attribute to the previous classifications

HydroClass software supports full tunability of the algorithms through a comprehensive set of parameters. The default parameter settings reflect the original algorithms, with variant settings for polarimetric radars operating at C-band and S-band. Users may customize the settings based on local knowledge, experience, and application needs.

For more information, see HydroClass Classifiers.

1 Bringi, V. N., and V. Chandrasekar, 2001: Polarimetric Doppler Weather Radar: Principles and Applications. Cambridge UP, 636.
2 Lim, S., V. Chandrasekar, and V.N. Bringi, 2005: Hydrometeor Classification System Using Dual Polarization Radar Measurements: Model Improvements and In Situ Verification. IEEE Transactions on Geoscience and Remote Sensing, 43.4, 792-801.
3 Liu, H., and V. Chandrasekar, 2000: Classification of Hydrometeors Based on Polarimetric Radar Measurements: Development of Fuzzy Logic and Neuro-Fuzzy Systems, and In Situ Verification. J. of Atmospheric and Oceanic Technology, 17.2, 140-164.
4 Park, H., A.V. Ryzhkov, D.S. Zrnic, and K.-E. Kim, 2008: The Hydrometeor Classification Algorithm for the Polarimetric WSR- 88D: Description and Application to an MCS. Weather and Forecasting, 24, 730-748.
5 Schuur,T. et al., 2003: Observations and Classifiction of Echoes with Polarimetric WSR-88D Radar. Rep. NOAA, Norman, OK, University of Oklahoma. Web: http://www.cimms.ou.edu/~schuur/jpole/ JPOLE_HCA_report_pdf.pdf.
6 Straka, J.M., D.S. Zrnić, and A.V. Ryzhkov, 2000: Bulk Hydrometeor Classification and Quantification Using Polarimetric Radar Data: Synthesis of Relations. J. of Applied Meteorology, 39.8, 1341-1372.
7 Waldvogel, A., B. Federer, and P. Grimm, 1979: Criteria for the Detectionof Hail Cells. J.of Applied Meteorology, 18.12, 1521-1525. See also Foote, G.B. at al ibid.