A multi-sensor physically based weather / non-weather radar echo classifier using polarimetric and environmental data in a real-time national system

Polarimetric radar observations provide information regarding the hydrometeor shape, size, and phase as well as an improved skill in differentiating radar echoes of hydrometeors from those of non-hydrometeors. In this work, a multi-sensor physically based algorithm is designed to classify weather/non-weather radar echoes. The algorithm uses reflectivity, correlation coefficient, and temperature sounding data to quality control the reflectivity data by applying a set of explicit meteorological rules. The proposed methodology are tested with a large number of real data cases across different geographical regions and seasons and showed a high accuracy (Heidke Skill Score of 0.83) in segregating weather and nonweather echoes. Comparing to other quality control methodologies using all polarimetric variables, this algorithm’s advantage is in its simplicity, effectiveness and computational efficiency. The current methodology is also demonstrated in a real-time multi-radar and multi-sensor national mosaic system.

[1]  David B. Wolff,et al.  General Probability-matched Relations between Radar Reflectivity and Rain Rate , 1993 .

[2]  Jonathan J. Gourley,et al.  A Fuzzy Logic Algorithm for the Separation of Precipitating from Nonprecipitating Echoes Using Polarimetric Radar Observations , 2007 .

[3]  J. Marshall,et al.  THE DISTRIBUTION OF RAINDROPS WITH SIZE , 1948 .

[4]  W. Gray,et al.  Radar rainfall estimation in the New Zealand context , 2005 .

[6]  Hervé Andrieu,et al.  Identification of Vertical Profiles of Radar Reflectivity for Hydrological Applications Using an Inverse Method. Part II: Formulation. , 1995 .

[7]  Jian Zhang,et al.  National mosaic and multi-sensor QPE (NMQ) system description, results, and future plans , 2011 .

[8]  David Bebbington,et al.  Modelling of weather radar echoes from anomalous propagation using a hybrid parabolic equation method and NWP model data , 2007 .

[9]  Witold F. Krajewski,et al.  Evaluation of Anomalous Propagation Echo Detection in WSR-88D Data: A Large Sample Case Study , 2001 .

[10]  R. Carbone,et al.  The Evolution of Raindrop Spectra in Warm-Based Convective Storms as Observed and Numerically Modeled , 1978 .

[11]  Valliappa Lakshmanan,et al.  Quality Control of Weather Radar Data Using Polarimetric Variables , 2014 .

[12]  Alexander V. Ryzhkov,et al.  The Hydrometeor Classification Algorithm for the Polarimetric WSR-88D: Description and Application to an MCS , 2009 .

[13]  V. Chandrasekar,et al.  Hydrometeor classification system using dual-polarization radar measurements: model improvements and in situ verification , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Travis M. Smith,et al.  An Automated Technique to Quality Control Radar Reflectivity Data , 2007 .

[15]  M. Kitchen,et al.  Real-time correction of weather radar data for the effects of bright band, range and orographic growth in widespread precipitation , 1994 .

[16]  V. Chandrasekar,et al.  Classification of Hydrometeors Based on Polarimetric Radar Measurements: Development of Fuzzy Logic and Neuro-Fuzzy Systems, and In Situ Verification , 2000 .

[17]  Urs Germann,et al.  Mesobeta Profiles to Extrapolate Radar Precipitation Measurements above the Alps to the Ground Level , 2002 .

[18]  Valery M. Melnikov,et al.  Autocorrelation and Cross-Correlation Estimators of Polarimetric Variables , 2007 .

[19]  Jerry M. Straka,et al.  Testing a Procedure for Automatic Classification of Hydrometeor Types , 2001 .

[20]  Frank S. Marzano,et al.  Supervised Classification and Estimation of Hydrometeors From C-Band Dual-Polarized Radars: A Bayesian Approach , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Jian Zhang,et al.  WSR-88D reflectivity quality control using horizontal and vertical reflectivity structure , 2004 .

[22]  F. Martin Ralph,et al.  Coastal Orographic Rainfall Processes Observed by Radar during the California Land-Falling Jets Experiment , 2003 .

[23]  Timothy J. Smyth,et al.  Radar estimates of rainfall rates at the ground in bright band and non‐bright band events , 1998 .