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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/amt-2019-324
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-324
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 18 Dec 2019

Submitted as: research article | 18 Dec 2019

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A revised version of this preprint is currently under review for the journal AMT.

Separation of Convective and Stratiform Precipitation Using Polarimetric Radar Data with A Support Vector Machine Method

Yadong Wang1, Lin Tang2, Pao-Liang Chang3, and Yu-Shuang Tang3 Yadong Wang et al.
  • 1Electrical and Computer Engineering Department, Southern Illinois University Edwardsville, Illinois, USA
  • 2Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, NOAA/OAR/National Severe StormsLaboratory, Norman, Oklahoma, USA
  • 3Central Weather Bureau, Taipei, Taiwan

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.

Yadong Wang et al.

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Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

Yadong Wang et al.

Yadong Wang et al.

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Latest update: 13 Jul 2020
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Short summary
The motivation of this work is developing a precipitation separation approach that can be implemented on those radars with fast scanning schemes. In these schemes, the higher tilts radar data is not available, which pose a challenges for the traditional approaches. This approach use an artificial intelligence approach which integrate polarimetric radar variables. The quantitative precipitation estimation will benefit from the outputs of this algorithm.
The motivation of this work is developing a precipitation separation approach that can be...
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