06 Jun 2023
 | 06 Jun 2023
Status: this preprint is currently under review for the journal AMT.

Study on The Error Structure of Radar Reflectivity Using The Symmetric Rainrate Predictor

Lidou Huyan, Yudong Gao, Zheng Wu, and Bojun Liu

Abstract. Given that the Gaussianity of observation error distribution is the fundamental principle of most current modern data assimilation methods, the error structure of radar reflectivity becomes increasingly important with the development of reflectivity assimilation in convection-allowing numerical weather prediction. This study examines the error distribution of radar reflectivity and discusses what give rise to the non-Gaussian error distribution by using 6 month reflectivity departures between observations and simulations in the Southwest China. By following the symmetric error model in all-sky satellite radiance assimilation, we unveil the error structure of radar reflectivity as a function of symmetric rainrates, which is the average of observed and simulated rainrates. Unlike satellite radiance, the reflectivity error shows a sharper slope in light precipitations than moderate precipitations. Thus, a three-piecewise fitting function is more suitable for radar reflectivity than a two-piecewise fitting function. The probability distribution functions of reflectivity departures normalized by symmetric rainrates become more Gaussian in comparison with the raw probability distribution function. Moreover, the possibility of using third-party predictor to construct the symmetric error model are also discussed in this study. According to the Jensen-Shannon divergence, a more linear predictor, the logarithmic transformation of rainrate, can provide the most Gaussian error distribution.

Lidou Huyan et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2023-72', Anonymous Referee #1, 25 Jun 2023
    • AC1: 'Reply on RC1', Yudong Gao, 28 Jun 2023
    • AC3: 'Reply on RC1', Yudong Gao, 24 Aug 2023
  • RC2: 'Comment on amt-2023-72', Anonymous Referee #2, 06 Aug 2023
    • AC2: 'Reply on RC2', Yudong Gao, 21 Aug 2023
  • CC1: 'Comment on amt-2023-72', Shizhang Wang, 23 Aug 2023
    • AC4: 'Reply on CC1', Yudong Gao, 24 Aug 2023
  • RC3: 'Comment on amt-2023-72', Anonymous Referee #3, 24 Aug 2023

Lidou Huyan et al.

Lidou Huyan et al.


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Short summary
In order to deal with this non-Gaussian problem, we describe the variation of reflectivity standard deviation as a function of the symmetric rainrate, which is the average of observed and simulated rainrates. The reflectivity error distribution normalized by the rainrate-dependent function becomes more Gaussian. The effects of the accuracy and linearization of symmetric rainrate on the reflectivity error structure are also discussed.