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WRaINfo is a software for real-time weather radar data processing developed by the Helmholtz Innovation Lab FERN.Lab, a technology and innovation platform of the German Research Centre for Geosciences Potsdam (GFZ). WRaINfo is specifically designed for processing X-band weather radar data of FURUNO devices. The modules of the package allow to read and process raw data of the WR2120 and WR2100. For this purpose, many functions of the library wradlib are used and adapted. The processing is controlled by a configuration file, main functionalities include formatting, attenuation correction, clutter detection, georeferencing and gridding of the data. This allows the construction of reproducible, automatic data processing chains. The package is written in the Python programming language. The source code is publicly available on GitLab. Compiled versions are also available on PyPi. The package is distributed under the Apache 2.0 license. [ABSTRACT FROM AUTHOR]

WRaINfo is a software for real-time weather radar data processing developed by the Helmholtz Innovation Lab FERN.Lab, a technology and innovation platform of the German Research Centre for Geosciences Potsdam (GFZ). WRaINfo is specifically designed for processing X-band weather radar data of FURUNO devices. The modules of the package allow to read and process raw data of the WR2120 and WR2100. For this purpose, many functions of the library wradlib are used and adapted. The processing is controlled by a configuration file, main functionalities include formatting, attenuation correction, clutter detection, georeferencing and gridding of the data. This allows the construction of reproducible, automatic data processing chains. The package is written in the Python programming language. The source code is publicly available on GitLab. Compiled versions are also available on PyPi. The package is distributed under the Apache 2.0 license.

This letter presents initial weather measurements with a cylindrical polarimetric phased array radar (CPPAR) demonstrator developed at The University of Oklahoma. The overall system specifications, waveform design, beam pattern measurement, and beam-to-beam calibration of the CPPAR demonstrator are presented. The weather observations of convective precipitation are provided, employing a single-beam mechanical scan and commutating beam electronic scan. Measurement results from these two scan modes are compared, and the error statistics are derived and discussed. A new feature of the CPPAR commutating beam electronic scan in clutter detection is observed and explained. [ABSTRACT FROM AUTHOR]

Our aim, in this paper, is to develop a clutter detection algorithm to provide more representative weather radar observations. The new discriminant function based on the phase fluctuation index (PFI) is introduced to achieve a better performance for clutter detection algorithms. Statistical properties of the PFI for pure weather and ground clutter are presented. A Bayesian classifier is used to make an optimal decision to detect clutter mixed with weather echoes. The performance improvements are demonstrated by applying the PFI detection algorithm to radar data collected by a WSR-88D polarimetric weather radar. Our proposed clutter detection algorithm is compared to several other detection algorithms and reveals the PFI algorithm yields the highest probability of detection. [ABSTRACT FROM AUTHOR]

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A novel dual-polarization and dual-scan (DPDS) classification algorithm is developed for clutter detection in weather radar observations. Two consecutive scans of dual-polarization radar echoes are jointly processed to estimate auto- and cross-correlation functions. Discriminants are then defined and estimated in order to separate clutter from weather based on their physical and statistical properties. An optimal Bayesian classifier is used to make a decision on clutter presence from the estimated discriminant functions. The DPDS algorithm is applied to the data collected with the KOUN polarimetric radar and compared with the existing detection methods. It is shown that the DPDS algorithm yields a higher probability of detection and lower false alarm rate in clutter detection. [ABSTRACT FROM AUTHOR]

A novel dual-polarization and dual-scan (DPDS) classification algorithm is developed for clutter detection in weather radar observations. Two consecutive scans of dual-polarization radar echoes are jointly processed to estimate auto- and cross-correlation functions. Discriminants are then defined and estimated in order to separate clutter from weather based on their physical and statistical properties. An optimal Bayesian classifier is used to make a decision on clutter presence from the estimated discriminant functions. The DPDS algorithm is applied to the data collected with the KOUN polarimetric radar and compared with the existing detection methods. It is shown that the DPDS algorithm yields a higher probability of detection and lower false alarm rate in clutter detection.