Real‐time monitoring of weather radar antenna pointing using digital terrain elevation and a Bayes clutter classifier

Quart J Royal Meteoro Soc

Krämer

2016

The aim of this work is to update ground-clutter classification methods in weather radar rainfall measurements to more accurately identify clutter pixels from wind farms. Measurements from two dual-polarised weather radars, based in the United Kingdom, will be used to determine the characteristics of multiple wind farms in the North Sea and the Irish Sea. Currently 21 of the top 25 largest offshore wind farms are located in these regions. The extensive area occupied by the wind farms creates problems for weather radars located in the neighbouring European countries. Datasets of wind-farm, precipitation and ground-clutter pixels were aggregated from Thurnham Radar measurements to form novel membership functions that can be used in a fuzzy logic classification system to identify wind-farm clutter. When only ground-clutter datasets were used for classification, areas of the radar scans taken up by wind-farm clutter were misclassified as rainfall. The inclusion of wind-farm measurements led to an increase in the ability of the algorithm to detect these pixels as clutter, as the Heidke Skill Score increased from 67.4 to 97.8%. However there was a slight increase in the number of precipitation pixels incorrectly classified as clutter, with the false alarm rate increasing from 0.05 to 1.24% when all variables are used. The algorithm performed slightly better when applied to another radar on Hameldon Hill, showing promise for application to the UK network without recalibration of membership functions.

Section: Classifying Wind‐farm Cluttermentioning

confidence: 95%

Offshore wind turbine clutter characteristics and identification in operational C‐band weather radar measurements

Hall

Quart J Royal Meteoro Soc

Krämer

2016

“…The Z DR 'column' has also been reported with its coincidence to thunderstorm updrafts (Loney et al, 2002;Snyder et al, 2013). Another capability of Z DR is that it can provide significant information of clutter echoes through the use of its texture (Friedrich et al, 2009;Rico-Ramirez et al, 2009). Although Z DR is independent of radar concentration, because Z DR is a product of Z H and Z V , the bias in Z DR may be introduced due to the mismatch between the horizontal (H) and vertical (V) channels (Liu et al, 2010).…”

Section: Differential Reflectivity (Z Dr )mentioning

confidence: 95%

An overview of the remote sensing of precipitation with polarimetric radar

Islam

Progress in Physical Geography: Earth and Environment

2013

Recent advances in radar remote sensing of precipitation include the development of polarimetric radar, which has the capability of transmitting in both the horizontal ( H) and vertical ( V) polarization states, thus providing additional information on the target precipitation particles. Radar polarimetry has not only been proven to improve data quality and precipitation estimation, but also improves characterization of precipitation particles; thus it has great potential in weather monitoring and forecasting. Realizing the potential of this state-of-the-art technology, meteorological departments across the world are upgrading their radar networks to polarimetric capabilities. Commensurate with this new era in precipitation remote sensing, this article provides an overview of polarimetric radar measurements, emphasizing the intrinsic signatures and their association to precipitation particle shapes, sizes and distributions. The potential research and applications of polarimetric radar signatures in meteorology are discussed. A considerable number of recent peer reviewed journal articles dealing with the topic are included in the bibliography.

“…The elevations used in the comparisons were 0.2 • and 0.7 • for the Thurnham radar and 1 • for the Chenies radar. Rays affected by partial beam blocking were identified and discarded from the analysis using the clutter predictor described in [24].…”

Section: Data Setsmentioning

confidence: 99%

Adaptive Attenuation Correction Techniques for C-Band Polarimetric Weather Radars

IEEE Trans. Geosci. Remote Sensing

2012

Self Cite

This paper presents the results of a study designed to evaluate the performance of attenuation correction algorithms due to heavy precipitation at operational C-band weather radar frequencies and using differential phase measurements. This paper proposes the extension of two attenuation correction algorithms and shows a methodology to validate attenuation-corrected reflectivity measurements by using a nearby radar as opposed to the traditional use of rain gauge or disdrometer measurements. The results showed that the the adaptive attenuation correction techniques have a higher performance than the nonadaptive ones when comparing the measured and computed Φ DP profiles and also when comparing with reflectivity measurements from a nearby radar. The results also showed that the difference in the performance of the adaptive attenuation correction algorithms is minimal and optimizing a single parameter (e.g., α in the BRI algorithm) rather than three unknown parameters (e.g., a, b, and α in the AHB or AFV algorithms) is a more robust procedure.