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Abstract
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 UK, 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. Data sets 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 data sets 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 lead 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 in Hameldon Hill showing promise for application to the UK network without recalibration of membership functions.
Original language | English |
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Pages (from-to) | 720–730 |
Number of pages | 11 |
Journal | Quarterly Journal of the Royal Meteorological Society |
Volume | 143 |
Issue number | 703 |
Early online date | 27 Dec 2016 |
DOIs | |
Publication status | Published - Jan 2017 |
Research Groups and Themes
- Water and Environmental Engineering
Keywords
- Polarimetric Radar
- Weather Radar
- Wind Farm
- Wind Turbine
- Clutter
- C-band
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Dive into the research topics of 'Off-shore wind turbine clutter characteristics and identification in operational C-band weather radar measurements'. Together they form a unique fingerprint.Projects
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Profiles
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Dr Miguel A Rico-Ramirez
- School of Civil, Aerospace and Design Engineering - Associate Professor of Radar Hydrology and Hydroinformatics
- Water and Environmental Engineering
- Cabot Institute for the Environment
Person: Academic , Member