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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amtd-8-11429-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amtd-8-11429-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Submitted as: research article 03 Nov 2015

Submitted as: research article | 03 Nov 2015

Review status
This discussion paper is a preprint. It has been under review for the journal Atmospheric Measurement Techniques (AMT). The revised manuscript was not accepted.

Application of bias correction methods to improve the accuracy of quantitative radar rainfall in Korea

J.-K. Lee1, J.-H. Kim2, and M.-K. Suk2 J.-K. Lee et al.
  • 1Innovation Center for Engineering Education, Daejin University, Pocheon-si, Gyeonggi-do, Korea
  • 2Weather Radar Center, Korea Meteorological Administration, Seoul, Korea

Abstract. There are many potential sources of the biases in the radar rainfall estimation process. This study classified the biases from the rainfall estimation process into the reflectivity measurement bias and the rainfall estimation bias by the Quantitative Precipitation Estimation (QPE) model and also conducted the bias correction methods to improve the accuracy of the Radar-AWS Rainrate (RAR) calculation system operated by the Korea Meteorological Administration (KMA). In the Z bias correction for the reflectivity biases occurred by measuring the rainfalls, this study utilized the bias correction algorithm. The concept of this algorithm is that the reflectivity of the target single-pol radars is corrected based on the reference dual-pol radar corrected in the hardware and software bias. This study, and then, dealt with two post-process methods, the Mean Field Bias Correction (MFBC) method and the Local Gauge Correction method (LGC), to correct the rainfall estimation bias by the QPE model. The Z bias and rainfall estimation bias correction methods were applied to the RAR system. The accuracy of the RAR system was improved after correcting Z bias. For the rainfall types, although the accuracy of the Changma front and the local torrential cases was slightly improved without the Z bias correction the accuracy of the typhoon cases got worse than the existing results in particular. As a result of the rainfall estimation bias correction, the Z bias_LGC was especially superior to the MFBC method because the different rainfall biases were applied to each grid rainfall amount in the LGC method. For the rainfall types, the results of the Z bias_LGC showed that the rainfall estimates for all types was more accurate than only the Z bias and, especially, the outcomes in the typhoon cases was vastly superior to the others.

J.-K. Lee et al.
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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J.-K. Lee et al.
J.-K. Lee et al.
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Short summary
We focus on the improvement of radar rainfall estimates using the various bias correction methods. This is significant because we introduced the various bias correction methods for the weather radar and we combined the QPE model and bias correction methods. The paper should be of interest to readers in the areas of weather radars. Many authors who deal with weather radars and research radar data submit their studies to this journal.
We focus on the improvement of radar rainfall estimates using the various bias correction...
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