Abstract
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 aforementioned disturbances in radar data
The recent dramatic increase in the number of radio local area network (RLAN) signals interfering with radar echoes (e.g. Saltikoff et al, 2016) as well as the greater number of wind farms used for energy generation (e.g. Hood et al, 2010) has created new and serious challenges to the developed QC algorithms despite the fact that the commonly used approaches show good performance in many observed cases
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 them of erroneous echoes (ground clutter, effects of anomalous beam propagation, biological scatterers such as birds and insects, etc.), correcting distorted data, and quantitative estimation of the final data uncertainty. Szturc: RADVOL-QC algorithms for weather radar data quality control or QPE based on multi-source information (Jatho et al, 2010; Jurczyk et al, 2020b; Méri et al, 2021). In the Polish national meteorological and hydrological service, i.e. the Institute of Meteorology and Water Management – National Research Institute, the RADVOL-QC system works operationally to perform QC of radar data delivered by the Polish weather radar network POLRAD (Osródka et al, 2014; Szturc et al, 2018). The paper is structured as follows: first, the RADVOLQC system, its structure, and applied approaches are briefly presented (Sect. 2); new solutions in the field of non-meteorological echo detection, incorporated into the RADVOL-QC after the publication describing its earlier version, are discussed in Sect. 3, which is the essential part of the paper; algorithms for correcting these detected distorted echoes are described (Sect. 4); there is a brief description of the verification of the system’s effectiveness (Sect. 5), followed by a concise summary (Sect. 6)
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