Example of data quality improvements achieved by continuous DSP upgrades on the NWRT PAR. Top and bottom panels show reflectivity data obtained with original and upgraded processing, respectively.
The deployment of the new signal processing hardware marked the beginning of a series of software upgrades. Using a path of continuous upgrades with an average of two releases every year, we have been gradually incorporating new and improved functionality to the NWRT PAR. The need for software and signal processing improvements is twofold. On one hand, it is desirable that the NWRT PAR produces operational-like data with quality comparable to that of the WSR-88D. High data quality leads to better data interpretation and is conducive to the development of automatic algorithms. On the other hand, improvements are needed to demonstrate new capabilities, some of which are applicable to conventional and phased-array radars, and some that are unique or better suited to PAR technology. For example, the use of adaptive scanning strategies to perform focused observations of the atmosphere is not unique to PAR, but update times can be greatly reduced by using PAR’s electronic beam steering capabilities to scan individual storms of interest as opposed to having to overcome the mechanical inertia inherent to reflector antennas.
To support the evolutionary nature of the signal processing capabilities on the NWRT PAR, we designed a flexible and expandable architecture based on “processing modes.” Each processing mode ingests time-series data and produces spectral moment data in fundamentally different ways. To date, the system supports six processing modes; three modes operate in the time domain and three in the frequency domain. Processing modes are data-driven signal processing pipelines (i.e., sequences of processing blocks) that can be controlled with a set of “processing options.” These are user-defined, editable control flags and parameters for the processing blocks that compose a processing mode.
Signal processing techniques address needs in four major areas: calibration, artifact removal, range-and-velocity ambiguity mitigation, and data precision. By 2011, the system ran a few automatic calibration routines such as noise power and direct-current (DC) bias measurements. Time-series data are filtered to mitigate contamination from radio-frequency interference, strong point targets such as airplanes, and stationary returns from the ground such as buildings or trees. Ground clutter detection and filtering is done automatically in real time and the filter’s suppression is adjusted based on the strength of the contamination. To mitigate range and velocity ambiguities the signal processor can ingest multiple-pulse-repetition-time (PRT) data such as “batch” or staggered PRT. In addition, accuracy of meteorological data can be improved by using range oversampling techniques or beam multiplexing.
In October of 2008, I gave an NSSL seminar describing our latest accomplishments. This was a great way to inform our community of the new functionality being added to the NWRT. Last year, I taught a segment on Unique Capabilities of PAR as part of the "Short Course on the Basics of Phased Array Radar Concepts and Technology" held prior to the 2009 MPAR Symposium.
A timeline of upgrades to the NWRT PAR can be found here.
Specific signal processing techniques are described in the following conference papers:
An overall description of this project can be found in this paper presented at the 2010 AMS Annual Meeting.
This fall, I had the honor and privilege to teach an OLLI class with my friend and colleague Jami Boettcher. "NEXRAD Weather Radar: How it Works and What Those Images Tell Us" kept us busy for 5 weeks this fall.
Our paper "Bootstrap Dual-Polarimetric Spectral Density Estimator" made the cover of the April 2017 issue of the IEEE Transactions on Geoscience and Remote Sensing journal.
I have accepted to serve as an associate editor for the American Meteorological Society’s Journal of Atmospheric and Oceanic Technology.
I have been chosen as the winner of the 2016 OU College of Atmospheric and Geographic Sciences Dean’s Award for Outstanding Service.