Advanced spectral processing for dual polarization weather radars
Date
2020
Authors
Dutta, Amit, author
Chandrasekaran, V., advisor
Cheney, Margaret, committee member
Siller, Thomas, committee member
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Abstract
This thesis focuses on the importance of spectral-domain processing and analysis in weather radar applications such as sea-clutter mitigation and the study of rain-hail mixtures in severe storms. An advanced spectral filtering technique has been proposed that helps in obtaining precipitation spectrum thus helping us to filter sea clutter and also carefully study the spectrum of different rain and hail cases in severe storms. Traditionally, time-domain auto-correlation techniques are used for the estimation of dual-polarization radar moments from the time series data. With the advent of low cost high-speed modern signal processors, frequency-domain processing techniques are feasible to be implemented in real-time. Hence spectral processing can be used for radar moments estimation. Previously, researchers have concluded that spectral filtering has improved the calculation of dual-polarization radar moments. Many algorithms have been implemented in real-time for clutter mitigation and data quality control. In this thesis, various existing frequency and time domain algorithms, such as standard notch filters, Gaussian Model Adaptive Processing (GMAP), and Parametric Time-Domain Method (PTDM) have been used for sea clutter mitigation, and their performances are studied. Spectral Signal Quality Index (SSQI), which is dependent on the auto-correlation spectral density of the signals, has been used to threshold noisy spectrum to obtain a clean precipitation spectrum. Next, using the results from PTDM along with the SSQI thresholding technique, the Polarimetric Spectral Filter for Adaptive Clutter and Noise Suppression [1] has been implemented. The combination of these spectral filtering techniques is regarded as Advanced Spectral Filter (ASF). The algorithms are applied to the observations recorded by the CSU-SEAPOL (Colorado State University - Sea-Going Polarimetric) radar data to identify and filter sea clutter. The ASF has been observed to perform better in terms of sea clutter suppression and identification. In general, spectral analysis of radar time-series data reveals various characteristics of different hydrometeors. Incorporating Doppler information along with polarimetric measurements in dual-polarization weather radar can unveil various microphysical properties in relation to the dynamics of storms in a radar resolution volume. This study is regarded as Spectral Polarimetry. Spectral analysis has been done on observations that were collected during the RELAMPAGO (Remote Sensing Of Electrification, Lightning, And Mesoscale/Microscale Processes With Adaptive Ground Observations) campaign in Argentina by the CSU-CHIVO (Colorado State University-C-band Hydro-meteorological Instrument for Volumetric Observation) radar. Spectral polarimetry revealed various spectral features such as bi-modal power spectrum, slopes in the spectral differential reflectivity, lowering of co-pol correlation spectrum, etc. from the observations. These features essentially helped to characterize and determine the microphysical properties of different storms. Thus the main goal of this thesis is to show the importance of spectral domain processing and analysis in relation to clutter mitigation and micro-physical study of storms.
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Subject
fuzzy logic
spectral analysis
dual polarization
spectral processing
parameter estimation