Development and validation of an X-band dual polarization Doppler weather radar test node for a tropical network, The

2012 Fall.Includes bibliographical references.An automated network of three X-band dual polarization Doppler weather radars is in process of being deployed and operational on the western coast of Puerto Rico. Colorado State University and the University of Puerto Rico at Mayaguez have collaborated to install the first polarimetric weather radar network in a tropical environment, known as TropiNet, to observe the lowest 2 km of the troposphere where the National Weather Service NEXRAD radar in Cayey, PR (TJUA) has obstructed views of the west coast, below 1.5 km due to terrain blockage and the Earth curvature problem. The CSU-X25P radar test node was developed, validated, and deployed to Mayaguez, PR in early 2011 to make first observations of this tropical region, and served as a pilot project to verify the infrastructure of the TropiNet network. This research describes the CSU-X25P radar test node, presenting the radar system specifications and an overview of the data acquisition and signal processing sub-systems, and the antenna positioner and control sub-system. The development and validation process included integration, sub-system calibration and test, and a final evaluation by conducting end-to-end calibration of the radar system. Validation of the calculated data moments, include Doppler velocity, reflectivity, differential reflectivity, differential propagation phase, and specific differential phase. The validation was accomplished by comparative analysis of data from coordinated scans between CSU-X25P and the well-established CSU-CHILL S-band polarimetric Doppler weather radar, in Greeley, CO. Upon validation, CSU-X25P was disassembled, packaged, and shipped to Puerto Rico to be fully deployed for operation in a tropical seaside environment. This research presents select observations of severe weather events, such as tropical storms and hurricanes, which attest to the robustness of the radar test node, and the TropiNet network infrastructure

Development of a polarimetric radar based hydrometeor classification algorithm for winter precipitation

2012 Fall.Includes bibliographical references.The nation-wide WSR-88D radar network is currently being upgraded for dual-polarized technology. While many convective, warm-season fuzzy-logic hydrometeor classification algorithms based on this new suite of radar variables and temperature have been refined, less progress has been made thus far in developing hydrometeor classification algorithms for winter precipitation. Unlike previous studies, the focus of this work is to exploit the discriminatory power of polarimetric variables to distinguish the most common precipitation types found in winter storms without the use of temperature as an additional variable. For the first time, detailed electromagnetic scattering of plates, dendrites, dry aggregated snowflakes, rain, freezing rain, and sleet are conducted at X-, C-, and S-band wavelengths. These physics-based results are used to determine the characteristic radar variable ranges associated with each precipitation type. A variable weighting system was also implemented in the algorithm's decision process to capitalize on the strengths of specific dual-polarimetric variables to discriminate between certain classes of hydrometeors, such as wet snow to indicate the melting layer. This algorithm was tested on observations during three different winter storms in Colorado and Oklahoma with the dual-wavelength X- and S-band CSU-CHILL, C-band OU-PRIME, and X-band CASA IP1 polarimetric radars. The algorithm showed success at all three frequencies, but was slightly more reliable at X-band because of the algorithm's strong dependence on specific differential phase. While plates were rarely distinguished from dendrites, the latter were satisfactorily differentiated from dry aggregated snowflakes and wet snow. Sleet and freezing rain could not be distinguished from rain or light rain based on polarimetric variables alone. However, high-resolution radar observations illustrated the refreezing process of raindrops into ice pellets, which has been documented before but not yet explained. Persistent, robust patterns of decreased correlation coefficient, enhanced differential reflectivity, and an inflection point around enhanced reflectivity occurred over the exact depth of the surface cold layer indicated by atmospheric soundings during times when sleet was reported at the surface. It is hypothesized that this refreezing signature is produced by a modulation of the drop size distribution such that smaller drops preferentially freeze into ice pellets first. The melting layer detection algorithm and fall speed spectra from vertically pointing radar also captured meaningful trends in the melting layer depth, height, and mean correlation coefficient during this transition from freezing rain to sleet at the surface. These findings demonstrate that this new radar-based winter hydrometeor classification algorithm is applicable for both research and operational sectors

Frequency diversity wideband digital receiver and signal processor for solid-state dual-polarimetric weather radars

2012 Summer.Includes bibliographical references.The recent spate in the use of solid-state transmitters for weather radar systems has unexceptionably revolutionized the research in meteorology. The solid-state transmitters allow transmission of low peak powers without losing the radar range resolution by allowing the use of pulse compression waveforms. In this research, a novel frequency-diversity wideband waveform is proposed and realized to extenuate the low sensitivity of solid-state radars and mitigate the blind range problem tied with the longer pulse compression waveforms. The latest developments in the computing landscape have permitted the design of wideband digital receivers which can process this novel waveform on Field Programmable Gate Array (FPGA) chips. In terms of signal processing, wideband systems are generally characterized by the fact that the bandwidth of the signal of interest is comparable to the sampled bandwidth; that is, a band of frequencies must be selected and filtered out from a comparable spectral window in which the signal might occur. The development of such a wideband digital receiver opens a window for exciting research opportunities for improved estimation of precipitation measurements for higher frequency systems such as X, Ku and Ka bands, satellite-borne radars and other solid-state ground-based radars. This research describes various unique challenges associated with the design of a multi-channel wideband receiver. The receiver consists of twelve channels which simultaneously downconvert and filter the digitized intermediate-frequency (IF) signal for radar data processing. The product processing for the multi-channel digital receiver mandates a software and network architecture which provides for generating and archiving a single meteorological product profile culled from multi-pulse profiles at an increased data date. The multi-channel digital receiver also continuously samples the transmit pulse for calibration of radar receiver gain and transmit power. The multi-channel digital receiver has been successfully deployed as a key component in the recently developed National Aeronautical and Space Administration (NASA) Global Precipitation Measurement (GPM) Dual-Frequency Dual-Polarization Doppler Radar (D3R). The D3R is the principal ground validation instrument for the precipitation measurements of the Dual Precipitation Radar (DPR) onboard the GPM Core Observatory satellite scheduled for launch in 2014. The D3R system employs two broadly separated frequencies at Ku- and Ka-bands that together make measurements for precipitation types which need higher sensitivity such as light rain, drizzle and snow. This research describes unique design space to configure the digital receiver for D3R at several processing levels. At length, this research presents analysis and results obtained by employing the multi-carrier waveforms for D3R during the 2012 GPM Cold-Season Precipitation Experiment (GCPEx) campaign in Canada

Applications in Low-Power Phased Array Weather Radars

Low-cost X-band radars are an emerging technology that offer significant advantages over traditional systems for weather remote sensing applications. X-band radars provide enhanced angular resolution at a fraction of the aperture size compared to larger, lower frequency systems. Because of their low cost and small form factor, these radars can now be integrated into more research and commercial applications. This work presents research and development activities using a low-cost, X-band (9410 MHz) Phase-Tilt Radar. The phase-tilt design is a novel phased array architecture that allows for rapid electronic scanning in azimuth and mechanical tilting in elevation, as a compromise between cost and performance. This work focuses on field studies and experiments in three meteorological applications. The first stage of research focuses on the real-world application of phased array radars in forest fire monitoring and observation. From April to May 2013, a phase-tilt radar was deployed to South Australia and underwent a field campaign to make polarimetric observations of prescribed burns within and around the Adelaide Hills region. Measurements show the real-time evolution of the smoke plume dynamics at a spatial and temporal resolution that has never before been observed with an X-band radar. This dissertation will perform data analysis on results from this field campaign. Results are compared against existing work, theories, and approaches. In the second stage of research, field experiments are performed to assess the data quality of X-band phased array radars. Specifically, this research focuses on the measurement of and techniques to improve the variance of weather product estimators for dual-polarized systems. Variability in the radar products is a complicated relationship between the radar system specifications, scanning strategy, and the physics governing precipitation. Here, the variance of the radar product estimators is measured using standard radar scanning strategies employed in traditional mechanical antenna systems. Results are compared against adaptive scan strategies such as beam multiplexing and frequency diversity. This work investigates the improvement that complex scanning strategies offer in dual-polarized, X-band phased array radar systems. In the third stage of research, simulations and field experiments are conducted to investigate the performance benefits of adaptive scanning to optimize the data quality of radar returns. This research focuses on the development and implementation of a waveform agile and adaptive scanning strategy to improve the quality of weather product estimators. Active phased array radars allow radar systems to quickly vary both scan pointing angles and waveform parameters in response to real-time observations of the atmosphere. As an evolution of the previous research effort, this work develops techniques to adaptively change the scan pointing angles, transmit and matched filter waveform parameters to achieve a desired level of data quality. Strategies and techniques are developed to minimize the error between observed and desired data quality measures. Simulation and field experiments are performed to assess the quality of the developed strategies

Investigations of the uncertainties associated with HID algorithms and guiding input to a novel, synthetic polarimetric radar simulator

2018 Spring.Includes bibliographical references.A methodology for model evaluation against observations is presented. With the advent of polarimetric radars, the need to produce simulated radar observables from model has also become apparent, in order to directly compare the same quantities between observations and models (e.g. rain rate calculations, hydrometeor identification - HID). To the end of evaluating model performance, for both a spectral bin microphysics (SBM) scheme and bulk microphysics scheme (BMS), a novel, synthetic polarimetric radar simulator created by Matsui et al. (2017) was implemented in this study: POLArimetric Radar Retrieval and Instrument Simulator (POLARRIS). POLARRIS takes in model data and simulates polarimetric radar variables in the forward component (POLARRIS-f), and then the inverse component of POLARRIS (iPOLARRIS) utilizes retrieval algorithms that are also employed in observations to make direct 1-to-1 comparisons between model simulations and observations. This inverse component is novel in its ability to help bridge the gap between model output and observations due to the fact that model output and observations without this framework are not directly comparable. The simulation of ice hydrometeors is not straightforward, and several assumptions are required to create polarimetric data for these species, such as the assumption of the size distribution, particle densities, particle melting, the input axis ratio, and canting angle assumptions. The last two variables are notoriously difficult to pin down for ice hydrometeors. This work aims to narrow down the appropriate inputs for axis ratio and canting angle assumptions that create the most comparable results with observations for three ice hydrometeors: aggregates, ice crystals, and graupel for two different meteorological regimes (mid-latitude supercell and tropical, monsoon MCS). Rain was also carried through as a check on model output. Through various sensitivity tests, it was concluded that, when run through the range of potential values, changes in axis ratio had a larger impact on the resulting polarimetric data than did changes in the canting angle assumptions. With this in mind, the 18 Z integrated hour from the 23 January 2006 monsoon MCS TWP – ICE case and the 22 Z integrated hour mid-latitude supercell from the 23 May 2011 MC3E case were simulated to help determine, for each hydrometeor type, the most appropriate axis ratio value(s) and canting angle assumptions that produced comparable results with observations. It was found using co-variance plots that, for 4ICE, the use of a singular axis ratio, mean canting angle, and degree of particle tumbling often produced differential reflectivity and specific differential phase values that converged to one value. While these values were within the observed values, they did not manage to simulate the breadth of observed values. Reflectivity values were also much too low compared to observations. SBM results, regardless of the type of input assumptions, tended to produce broader ranges for these variables, and also managed to better capture the reflectivity range seen in observations than was the case for the BMS. However, the reflectivity ranges seen in SBM were at times too expansive. The differences between SBM output and BMS output is likely due to the differing inherent assumptions in each microphysical scheme. The sensitivity of the simulated hydrometeors' polarimetric data was also probed against changing axis ratio and canting angle input assumptions. It was found that, in particular, BMS differential reflectivity values were quite sensitive to changes in input assumptions, regardless of the regime (tropical MCS vs. mid-latitude supercell). HID was found to be the most effective method to evaluate the performance of the two different model microphysical schemes (SBM vs. BMS) with respect to observations. Input assumptions that produced the most comparable results with respect to observations for each hydrometeor were compared using HID stacked frequency by altitude (SFAD) diagrams for convective and stratiform precipitation. This analysis found that although the co-variance plots revealed many model shortcomings, the HID proved to be fairly robust, especially for MC3E. The sensitivity of the HID retrieval itself was also investigated with respect to changing inputs (i.e. the membership beta functions) to the HID algorithm. The resulting HID was fairly sensitive to changes in the inputs to HID, particularly for model simulations. Observations seemed less responsive to changes in these input assumptions to HID. Longer simulation time frames, the potential inclusion of simulated melting hydrometeors, and investigation of other radar wavelengths are all suggested to help further utilize this methodology for evaluating model microphysical schemes' abilities to accurately simulate polarimetric data and HID retrievals with respect to observations

Storm microphysics and kinematics at the ARM-SGP site using dual polarized radar observations at multiple frequencies

2014 Fall.Includes bibliographical references.This research utilizes observations from the Atmospheric Radiation Measurement (ARM) Climate Research Facility at the Southern Great Plains location to investigate the kinematic and microphysical processes present in various types of weather systems. The majority of the data used was collected during the Mid-latitude Continental Convective Cloud Experiment (MC3E), and utilizes the network of scanning radars to arrive at a multi-Doppler wind retrieval and is compared to vertical wind measurements from a centrally located profiling radar. Microphysical compositions of the storms are analyzed using a multi-wavelength hydrometeor identification algorithm utilizing the strengths of each of the radar wavelengths available (X, C, S). When available, a comparison is done between observational analysis and simulated model output from the Weather Research Forecasting model with Spectral-bin Microphysics (WRF-SBM) using bulk statistics to look at reflectivity, vertical motions, and microphysics

Effects of spatial resolution on radar-based precipitation estimation using sub-kilometer X-band radar measurements

Known for the ability to observe precipitation at spatial resolution higher than rain gauge networks and satellite products, weather radars allow us to measure precipitation at spatial resolutions of 1 kilometer (typical resolution for operational radars) and a few hundred meters (often used in research activities). In principle, we can operate a weather radar at resolution higher than 100m and the expectation is that radar data at higher spatial resolution can provide more information. However, there is no systematic research about whether the additional information is noise or useful data contributing to the quantitative precipitation estimation. In order to quantitatively investigate the changes, as either benefits or drawbacks, caused by increasing the spatial resolution of radar measurements, we set up an X-band radar field experiment from May to October in 2017 in the Stuttgart metropolitan region. The scan strategy consists of two quasi-simultaneous scans with a 75-m and a 250-m radial resolution respectively. They are named as the fine scan and the coarse scan, respectively. Both scans are compared to each other in terms of the radar data quality and their radar-based precipitation estimates. The primary results from these comparisons between the radar data of these two scans show that, in contrast to the coarse scan, the fine scan data are characterized with losses of weak echoes, are more subjected to external signals and second-trip echoes (drawback), are more effective in removing non-meteorological echoes (benefit), are more skillful in delineating convective storms (benefit), and show a better agreement with the external reference data (benefit)

Model-based iterative approach to polarimetric radar rainfall estimation in presence of path attenuation

A new model-based iterative technique to correct for attenuation and differential attenuation and retrieve rain rate, based on a neural-network scheme and a differential phase constraint, is presented. Numerical simulations are used to investigate the efficiency and accuracy of this approach named NIPPER. The simulator is based on a T-matrix solution technique, while precipitation is characterized with respect to shape, raindrop size distribution and orientation. A sensitivity analysis is performed in order to evaluate the expected errors of this method. The performance of the proposed methodology on radar measurements is evaluated by using one-dimensional Gaussian shaped rain cell models and synthetic radar data derived from disdrometer measurements. Numerical results are discussed in order to evaluate the robustness of the proposed techniqu

X-band Dual Polarization Phased-Array Radar for Meteorological Applications

This dissertation details the development and operation of a novel dual-polarized Phase-Tilt Weather Radar (PTWR) designed for meteorological applications. The use of radar has a well-documented history in detection and classification of weather phenomena, but due to the limited mechanical scanning speed, its usage for severe weather observations remains far from ideal. The PTWR utilizes phased-array technology and provides unique capabilities such as smart scanning, fast scan update, and tracking. This technology is considered a candidate for a replacement and consolidation of the current US weather and surveillance radar networks. The dissertation can be divided into three parts. First, the hardware design of the radar is presented. Methods of an element and array calibration are discussed. The measured sidelobe level and pattern match exhibit satisfactory performance. The algorithms for signal processing in alternate transmit alternate receive mode of operation are described in detail. The PTWR weather detection capability is validated by an inter-comparison with a collocated X-band high-power radar. These tests showed correlation exceeding 90% for measurements of reflectivity in a convective storm system. The results support the hypothesis that phased-array technology poses an attractive solution for weather remote sensing. The second part addresses the radar waveform considerations. The sensitivity of the radar can be improved by several decibels by means of pulse compression techniques. This is necessary, since the PTWR utilizes low-power solid-state transmitters. The work discusses the trade-offs in waveform design and introduces a novel compression filter, which outperforms traditional window-based solutions. The pulse compression performance is validated using clutter data collected by the PTWR, proving that a deep sidelobe reduction in excess of 40dB can be achieved at the minimal penalty in signal-to-noise level (below 0.5dB). Finally, the third part focuses on the scanning geometry of a 1-D phase-tilt ar- chitecture. It is shown that as the elevation angle is increased, the measurements are affected by a self-induced apparent canting angle. The methods of polarization rotation correction are presented. The biases in typical weather radar products such as reflectivity, differential reflectivity, correlation coefficient, and specific propagation phase, are investigated. The analysis shows that for elevation angles below 15deg , the retrievals errors are acceptable