Fuzzy logic filtering of radar reflectivity to remove non-meteorological echoes using dual polarization radar moments

• Published in: Atmospheric measurement techniques Vol. 8; no. 10; pp. 3985 - 4000
• Main Authors: Dufton, D R L, Collier, C G
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 01-10-2015; Copernicus Publications
• Subjects: Ambient noise; Analysis; Artificial intelligence; Atmospheric precipitations; Atmospheric sciences; Atmospherics; Background noise; Classification; Classifiers; Clutter; Convective precipitation; Cooling towers; Correlation coefficient; Correlation coefficients; Deployment; Doppler sonar; Dual polarization radar; Echoes; Electricity; Filtering; Filtration; Fuzzy logic; Hydrology; Identification; Insects; Meteorological radar; Polarization; Power lines; Precipitation; Precipitation (Meteorology); Precipitation estimation; Radar; Radar clutter; Radar data; Radar echoes; Radar reflectivity; Rain; Rainfall; Reflectance; Superhigh frequencies; Surface layer; Texture; Variables; Velocity; Weather
• ISSN: 1867-8548; 1867-1381; 1867-8548; 1867-1381
• DOI: 10.5194/amt-8-3985-2015

The ability of a fuzzy logic classifier to dynamically identify non-meteorological radar echoes is demonstrated using data from the National Centre for Atmospheric Science dual polarisation, Doppler, X-band mobile radar. Dynamic filtering of radar echoes is required due to the variable presence of spurious targets, which can include insects, ground clutter and background noise. The fuzzy logic classifier described here uses novel multi-vertex membership functions which allow a range of distributions to be incorporated into the final decision. These membership functions are derived using empirical observations, from a subset of the available radar data. The classifier incorporates a threshold of certainty (25 % of the total possible membership score) into the final fractional defuzzification to improve the reliability of the results. It is shown that the addition of linear texture fields, specifically the texture of the cross-correlation coefficient, differential phase shift and differential reflectivity, to the classifier along with standard dual polarisation radar moments enhances the ability of the fuzzy classifier to identify multiple features. Examples from the Convective Precipitation Experiment (COPE) show the ability of the filter to identify insects (18 August 2013) and ground clutter in the presence of precipitation (17 August 2013). Medium-duration rainfall accumulations across the whole of the COPE campaign show the benefit of applying the filter prior to making quantitative precipitation estimates. A second deployment at a second field site (Burn Airfield, 6 October 2014) shows the applicability of the method to multiple locations, with small echo features, including power lines and cooling towers, being successfully identified by the classifier without modification of the membership functions from the previous deployment. The fuzzy logic filter described can also be run in near real time, with a delay of less than 1 min, allowing its use on future field campaigns.