Abstract
Abstract. A precipitation separation approach using a support vector machine method was developed and tested on a C-band polarimetric weather radar located in Taiwan (RCMK). Different from those methods requiring whole-volume scan data, the proposed approach utilizes polarimetric radar data from the lowest unblocked tilt to classify precipitation echoes into either stratiform or convective types. In this algorithm, inputs of radar reflectivity, differential reflectivity, and the separation index are integrated through a support vector machine. The weight vector and bias in the support vector machine were optimized using well-classified data from two precipitation events. The proposed approach was tested with three precipitation events, including a widespread mixed stratiform and convective event, a tropical typhoon precipitation event, and a stratiform-precipitation event. Results from the multi-radar–multi-sensor (MRMS) precipitation classification algorithm were used as the ground truth in the performance evaluation. The performance of the proposed approach was also compared with the approach using the separation index only. It was found that the proposed method can accurately classify the convective and stratiform precipitation and produce better results than the approach using the separation index only.
Highlights
Convective and stratiform precipitation exhibit a significant difference in precipitation growth mechanisms, thermodynamic structures, and precipitation intensities (e.g., Houghton, 1968; Houze, 1993, 1997)
A C-band polarimetric radar precipitation separation approach was developed by Bringi et al (2009), which classifies the precipitation into stratiform, convective, and transition regions based on retrieved drop size distribution (DSD) characteristics
A support-vector-machine-(SVM)-based classification method was developed and tested on a C-band polarimetric radar located in Taiwan
Summary
Convective and stratiform precipitation exhibit a significant difference in precipitation growth mechanisms, thermodynamic structures, and precipitation intensities (e.g., Houghton, 1968; Houze, 1993, 1997). SHY95 proposed a separation approach that utilizes the texture features derived from the radar reflectivity field In this approach, a grid point is identified as the convective center if its Z value is larger than 40 dBZ or exceeds the average intensity taken over the surrounding background. Such an alternative scanning scheme enables the WSR-88D radars to promptly capture the storm development and enhance the weather forecast capability These new schemes include the automated volume scan evaluation and termination (AVSET), supplemental adaptive intra-volume low-level scan (SAILS), the multiple elevation scan option for SAILS, and the mid-volume rescan of low-level elevations (MRLEs). Polarimetric measurements may reveal more precipitation microphysical and dynamic properties Inspired by these features, a C-band polarimetric radar precipitation separation approach was developed by Bringi et al (2009) (hereafter BAL), which classifies the precipitation into stratiform, convective, and transition regions based on retrieved drop size distribution (DSD) characteristics.
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