Adaptive and high-resolution estimation of specific differential phase for polarimetric X-band weather radars

Ricardo Reinoso-Rondinel*, Christine Unal, Herman Russchenberg

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)
45 Downloads (Pure)


One of the most beneficial polarimetric variables may be the specific differential phase KDP because of its independence from power attenuation and radar miscalibration. However, conventional KDP estimation requires a substantial amount of range smoothing as a result of the noisy characteristic of the measured differential phase ψDP. In addition, the backscatter differential phase δhv component of ψDP, significant at C- and X-band frequency, may lead to inaccurate KDP estimates. In this work, an adaptive approach is proposed to obtain accurate KDP estimates in rain from noisy ψDP, whose δhv is of significance, at range resolution scales. This approach uses existing relations between polarimetric variables in rain to filter δhv from ψDP while maintaining its spatial variability. In addition, the standard deviation of the proposed KDP estimator is mathematically formulated for quality control. The adaptive approach is assessed using four storm events, associated with light and heavy rain, observed by a polarimetric X-band weather radar in the Netherlands. It is shown that this approach is able to retain the spatial variability of the storms at scales of the range resolution. Moreover, the performance of the proposed approach is compared with two different methods. The results of this comparison show that the proposed approach outperforms the other two methods in terms of the correlation between KDP and reflectivity, and KDP standard deviation reduction.

Original languageEnglish
Pages (from-to)555-573
Number of pages19
JournalJournal of Atmospheric and Oceanic Technology
Issue number3
Publication statusPublished - 1 Mar 2018


  • Data processing
  • Filtering techniques
  • Radars/Radar observations
  • Remote sensing
  • Weather radar signal processing


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