Abstract
Data from weather radars are commonly used in meteorology and hydrology, but they are burdened with serious disturbances, especially due to the appearance of numerous non-meteorological echoes. For this reason, these data are subject to advanced quality control algorithms. The paper presents a significant improvement of the RADVOL-QC system made necessary by the appearance of an increasing number of various disturbances. New algorithms are mainly addressed to the occurrence of clutter caused by wind turbines (DP.TURBINE algorithm) and other terrain obstacles (DP.NMET algorithm), as well as various forms of echoes caused by the interaction of a radar beam with RLAN signals (set of SPIKE algorithms). The individual algorithms are based on the employment of polarimetric data as well as on the geometric analysis of echo patterns. In the paper the algorithms are described along with examples of their performance and an assessment of their effectiveness, and finally examples of the performance of the whole system are discussed.
Highlights
Weather radar data are widely employed in weather monitoring and forecasting, they have been significantly improved, and observational capabilities of radars have been enhanced in response to increasing demands for better resolution and accuracy
The main objective of this study is to present new, more effective algorithms incorporated into the RADVOL-quality control (QC) system which are able to effectively deal with the abovementioned disturbances in radar data
To mitigate or eliminate the aforementioned radio local area network (RLAN) interference and effects of wind turbines in quantitative rainfall estimation and subsequent applications, this paper proposes the incorporation of enhanced automated approaches into the RADVOL-QC system
Summary
Weather radar data are widely employed in weather monitoring and forecasting, they have been significantly improved, and observational capabilities of radars have been enhanced in response to increasing demands for better resolution and accuracy. While employing weather radar observations it is crucial to perform an advanced quality control (QC) of the data, which consists of clearing it from erroneous echoes (groundclutter, effects of anomalous beam propagation, and biological scatterers such as birds and insects, etc.), correcting distorted data, and quantitative estimation of the final data uncertainty. The quantity called quality index (QI) plays an increasingly important role in quality control of weather radar data (e.g., Einfalt et al, 2010; Michelson et al, 2014), as it provides quantitative information on the quality of data, but can be used to generate more reliable individual products (Ośródka and Szturc, 2015), to produce composite maps (Fornasiero et al, 2006; Sandford and Gaussiat, 2012; Jurczyk et al, 2020a) or QPE based on multi-source information (Jatho et al, 2010; Jurczyk et al, 2020b; Méri et al, 2021). Management – National Research Institute, the RADVOL-QC system works operationally to perform QC of radar data delivered by the Polish weather radar network POLRAD (Ośródka et al, 2014; Szturc et al, 2018).
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