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.
Abstract. A precipitation separation approach using support vector machine method was developed and tested on a C-band polarimetric radar located in Taiwan (RCMK). Different from some existing separation methods that require a whole volume radar data, the proposed approach utilizes the polarimetric radar data from the lowest tilt to classify precipitation echoes into either stratiform or convective type. Through a support vector machine method, the inputs of radar reflectivity, differential reflectivity, and the separation index are utilized in the classification. The feature vector and weight vector in the support vector machine were optimized using well-classified training data. The proposed approach was tested with multiple precipitation events including two widespread mixture of stratiform and convective events, a tropical typhoon precipitation event, and a stratiform precipitation event. In the evaluation, the results from the multi-radar-multi-sensor (MRMS) precipitation classification approach were used as the ground truth, and the performances from proposed approach were further compared with the approach using separation index only with different thresholds. It was found that the proposed method can accurately identify the convective cells from stratiform storms with the radar data only from the lowest scanning tilt. It can produce better results than using the separation index only.
We do appreciate the reviewer provide so much important comments help us improving our manuscript. We'd like to address these comments as following. Line 2: "that require a whole volume radar data" should read as.. .."that require a whole volume of radar data" Response: the manuscript was modified following the reviewer's comment. Line 7: "with multiple precipitation events including two widespread mixture of stratiform and convective events" May read better as.. ...with multiple precipitation events C1
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