Preprints
https://doi.org/10.5194/amt-2020-290
https://doi.org/10.5194/amt-2020-290

  18 Aug 2020

18 Aug 2020

Review status: a revised version of this preprint was accepted for the journal AMT and is expected to appear here in due course.

Evaluation of micro rain radar-based precipitation classification algorithms to discriminate between stratiform and convective precipitation

Andreas Foth1, Janek Zimmer2, Felix Lauermann1,a, and Heike Kalesse1 Andreas Foth et al.
  • 1Leipzig Institute for Meteorology, University of Leipzig, Leipzig, Germany
  • 2Meteologix AG, Sattel, Switzerland
  • anow at: Deutscher Wetterdienst, Meteorologisches Observatorium Lindenberg/Richard–Aßmann–Observatorium, Tauche, Germany

Abstract. In this paper, we present two micro rain radar-based approaches to discriminate between stratiform and convective precipitation. One is based on probability density functions (PDFs) in combination with a confidence function and the other one is an artificial neural network (ANN) classification. Both methods use the maximum radar reflectivity per profile, the maximum of the observed mean Doppler velocity per profile and the maximum of the temporal standard deviation (±15 min) of the observed 5 mean Doppler velocity per profile from a micro rain radar (MRR). Training and testing of the algorithms were performed using a two year data set from the Jülich Observatory for Cloud Evolution (JOYCE). Both methods agree well giving similar results. However, the results of the artificial neural network are more reasonable since it is also able to distinguish into an inconclusive class, in turn making the stratiform and convective classes more reliable.

Andreas Foth et al.

 
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Status: closed
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Andreas Foth et al.

Andreas Foth et al.

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Short summary
In this paper, we present two micro rain radar-based approaches to discriminate between stratiform and convective precipitation. One is based on probability density functions and the other one is an artificial neural network classification. Both methods agree well giving similar results. However, the results of the artificial neural network are more reasonable since it is also able to distinguish into an inconclusive class, in turn making the stratiform and convective classes more reliable.