The Bootstrap Dual Polarimetric Spectral Density Estimator

Weather radar moments and polarimetric variables provide useful information about the characteristics and motion of hydrometeors. However, the bulk information may be masked when the meteorological signal of interest is contaminated by clutter. The dual-polarimetric spectral densities (DPSD) may unveil additional information about the polarimetric characteristics of groups of scatterers moving at different Doppler velocities in a given radar resolution volume. Previous DPSD estimation methods required averaging a large number of spectra (obtained from different range gates, radials, or scans), or averaging in frequency to get accurate estimates; though by doing so, the resolution is degraded, and important features of the meteorological phenomenon may be masked, potentially affecting the ability to perform a good spectral analysis. In an attempt to overcome these limitations, the Bootstrap DPSD estimator is developed, which allows the estimation of DPSDs from a single dwell, with minimal resolution loss. Briefly, the estimator pre-processes the weather radar I/Q time-series signals and generates I/Q pseudo-realizations through bootstrap resampling, which are then used to compute PSD estimates that are averaged to obtain the DPSD estimate. Then, a post-processing stage applies a bias correction to the estimates. The Bootstrap DPSD estimator's performance is compared to that of conventional methods for single-dwell as well as for multiple-dwell estimates. Additionally, the performance and limitations of the Bootstrap and conventional DPSD estimators are assessed when identifying signals of different polarimetric signatures of scatterers moving at different radial velocities in the radar volume. The advantages of the Bootstrap DPSD estimator as a tool for polarimetric spectral analysis is demonstrated with a few examples of polarimetric spectral signatures in data from tornado cases, and from a physically-based simulator. It is expected that, with the Bootstrap DPSD and polarimetric spectral analysis, it will be possible to better understand tornado dynamics and their connection to weather radar measurements, as well as to elucidate important scientific questions that motivated this work

Radar Detection of High Concentrations of Ice Particles - Methodology and Preliminary Flight Test Results

High Ice Water Content (HIWC) has been identified as a primary causal factor in numerous engine events over the past two decades. Previous attempts to develop a remote detection process utilizing modern commercial radars have failed to produce reliable results. This paper discusses the reasons for previous failures and describes a new technique that has shown very encouraging accuracy and range performance without the need for any hardware modifications to industrys current radar designs. The performance of this new process was evaluated during the joint NASA/FAA HIWC RADAR II Flight Campaign in August of 2018. Results from that evaluation are discussed, along with the potential for commercial application, and development of minimum operational performance standards for a future commercial radar product

Spectral analyses of the dual polarization Doppler weather radar data.

Echoes in clear air from biological scatterers mixed within the resolution volumes over a large region are presented. These echoes were observed with the polarimetric prototype of the forthcoming WSR-88D weather radar. The study case occurred in the evening of September 7, 2004, at the beginning of the bird migrating season. Novel polarimetric spectral analyses are used for distinguishing signatures of birds and insects in multimodal spectra. These biological scatterers were present at the same time in the radar resolution volumes over a large area. Spectral techniques for (1) data censoring, (2) wind retrieval and (3) estimation of intrinsic values/functions of polarimetric variables for different types of scatterers are presented. The technique for data censoring in the frequency domain allows detection of weak signals. Censoring is performed on the level of spectral densities, allowing exposure of contributions to the spectrum from multiple types of scatterers. The spectral techniques for wind retrieval allow simultaneous estimation of wind from the data that are severely contaminated by migrating birds, and assessment of bird migration parameters. The intrinsic polarimetric signatures associated with the variety of scatterers can be evaluated using presented methodology. Algorithms for echo classification can be built on these. The possibilities of spectral processing using parametric estimation techniques are explored for resolving contributions to the Doppler spectrum from the three types of scatterers: passive wind tracers, actively flying insects and birds. A combination of parametric and non-parametric polarimetric spectral analyses is used to estimate the small bias introduced to the wind velocity by actively flying insects

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

Polarimetric Synthetic Aperture Radar (SAR) Application for Geological Mapping and Resource Exploration in the Canadian Arctic

The role of remote sensing in geological mapping has been rapidly growing by providing predictive maps in advance of field surveys. Remote predictive maps with broad spatial coverage have been produced for northern Canada and the Canadian Arctic which are typically very difficult to access. Multi and hyperspectral airborne and spaceborne sensors are widely used for geological mapping as spectral characteristics are able to constrain the minerals and rocks that are present in a target region. Rock surfaces in the Canadian Arctic are altered by extensive glacial activity and freeze-thaw weathering, and form different surface roughnesses depending on rock type. Different physical surface properties, such as surface roughness and soil moisture, can be revealed by distinct radar backscattering signatures at different polarizations. This thesis aims to provide a multidisciplinary approach for remote predictive mapping that integrates the lithological and physical surface properties of target rocks. This work investigates the physical surface properties of geological units in the Tunnunik and Haughton impact structures in the Canadian Arctic characterized by polarimetric synthetic aperture radar (SAR). It relates the radar scattering mechanisms of target surfaces to their lithological compositions from multispectral analysis for remote predictive geological mapping in the Canadian Arctic. This work quantitatively estimates the surface roughness relative to the transmitted radar wavelength and volumetric soil moisture by radar scattering model inversion. The SAR polarization signatures of different geological units were also characterized, which showed a significant correlation with their surface roughness. This work presents a modified radar scattering model for weathered rock surfaces. More broadly, it presents an integrative remote predictive mapping algorithm by combining multispectral and polarimetric SAR parameters

Influence of cloud microphysics schemes on weather model predictions of heavy precipitation

Cloud microphysics is one of the major sources of uncertainty in numerical weather prediction models. In this work, the ability of a numerical weather prediction model to correctly predict high-impact weather events, i.e., hail and heavy rain, using different cloud microphysics schemes is evaluated statistically. Polarimetric C-band radar observations over 30 convection days are used as observation dataset. Simulations are made using the regional-scale Weather Research and Forecasting Model (WRF) with five microphysical schemes of varying complexity (double moment, spectral bin (SBM), and particle property prediction (P3)). Statistical characteristics of heavy rain and hail events of varying intensities are compared between simulations and observations. All simulations, regardless of the microphysical scheme, predict heavy rain events that cover larger average areas than those observed by radar. The frequency of these heavy rain events is similar to radar-measured heavy rain events, but still scatters by a factor of 2 around the observations, depending on the microphysical scheme. The model is generally unable to simulate extreme hail events with reflectivity thresholds of 55 dBZ and higher, although they have been observed by radar during the evaluation period. For slightly weaker hail/graupel events, only the P3 model is able to reproduce the observed statistics. Analysis of the raindrop size distribution in combination with the model mixing ratio shows that the P3, Thompson 2-mom, and Thompson aerosol-aware models produce large raindrops too frequently, and the SBM model misses large rain and graupel particles.</p

Evaluation of convective cloud microphysics in numerical weather prediction models with dual-wavelength polarimetric radar observations: methods and examples

The representation of cloud microphysical processes contributes substantially to the uncertainty of numerical weather simulations. In part, this is owed to some fundamental knowledge gaps in the underlying processes due to the difficulty of observing them directly. On the path to closing these gaps, we present a setup for the systematic characterization of differences between numerical weather model and radar observations for convective weather situations. Radar observations are introduced which provide targeted dual-wavelength and polarimetric measurements of convective clouds with the potential to provide more detailed information about hydrometeor shapes and sizes. A convection-permitting regional weather model setup is established using five different microphysics schemes (double-moment, spectral bin ("Fast Spectral Bin Microphysics", FSBM), and particle property prediction (P3)). Observations are compared to hindcasts which are created with a polarimetric radar forward simulator for all measurement days. A cell-tracking algorithm applied to radar and model data facilitates comparison on a cell object basis. Statistical comparisons of radar observations and numerical weather model runs are presented on a data set of 30 convection days. In general, simulations show too few weak and small-scale convective cells. Contoured frequency by altitude diagrams of radar signatures reveal deviations between the schemes and observations in ice and liquid phase. Apart from the P3 scheme, high reflectivities in the ice phase are simulated too frequently. Dual-wavelength signatures demonstrate issues of most schemes to correctly represent ice particle size distributions, producing too large or too dense graupel particles. Comparison of polarimetric radar signatures reveals issues of all schemes except the FSBM to correctly represent rain particle size distributions

Electronic scan weather radar: scan strategy and signal processing for volume targets

2013 Fall.Includes bibliographical references.Following the success of the WSR-88D network, considerable effort has been directed toward searching for options for the next generation of weather radar technology. With its superior capability for rapidly scanning the atmosphere, electronically scanned phased array radar (PAR) is a potential candidate. A network of such radars has been recommended for consideration by the National Academies Committee on Weather Radar Technology beyond NEXRAD. While conventional weather radar uses a rotating parabolic antenna to form and direct the beam, a phased array radar superimposes outputs from an array of many similar radiating elements to yield a beam that is scanned electronically. An adaptive scan strategy and advanced signal designs and processing concepts are developed in this work to use PAR effectively for weather observation. An adaptive scan strategy for weather targets is developed based on the space-time variability of the storm under observation. Quickly evolving regions are scanned more often and spatial sampling resolution is matched to spatial scale. A model that includes the interaction between space and time is used to extract spatial and temporal scales of the medium and to define scanning regions. The temporal scale constrains the radar revisit time while the measurement accuracy controls the dwell time. These conditions are employed in a task scheduler that works on a ray-by-ray basis and is designed to balance task priority and radar resources. The scheduler algorithm also includes an optimization procedure for minimizing radar scan time. In this research, a signal model for polarimetric phased array weather radar (PAWR) is presented and analyzed. The electronic scan mechanism creates a complex coupling of horizontal and vertical polarizations that produce the bias in the polarimetric variables retrieval. Methods for bias correction for simultaneous and alternating transmission modes are proposed. It is shown that the bias can be effectively removed; however, data quality degradation occurs at far off boresight directions. The effective range for the bias correction methods is suggested by using radar simulation. The pulsing scheme used in PAWR requires a new ground clutter filtering method. The filter is designed to work with a signal covariance matrix in the time domain. The matrix size is set to match the data block size. The filter's design helps overcome limitations of spectral filtering methods and make efficient use of reducing ground clutter width in PAWR. Therefore, it works on modes with few samples. Additionally, the filter can be directly extended for staggered PRT waveforms. Filter implementation for polarimetric retrieval is also successfully developed and tested for simultaneous and alternating staggered PRT. The performance of these methods is discussed in detail. It is important to achieve high sensitivity for PAWR. The use of low-power solid state transmitters to keep costs down requires pulse compression technique. Wide-band pulse compression filters will partly reduce the system sensitivity performance. A system for sensitivity enhancement (SES) for pulse compression weather radar is developed to mitigate this issue. SES uses a dual-waveform transmission scheme and an adaptive pulse compression filter that is based on the self-consistency between signals of the two waveforms. Using SES, the system sensitivity can be improved by 8 to 10 dB

Bayesian statistical analysis of ground-clutter for the relative calibration of dual polarization weather radars

A new data processing methodology, based on the statistical analysis of ground-clutter echoes and aimed at investigating the stability of the weather radar relative calibration, is presented. A Bayesian classification scheme has been used to identify meteorological and/or ground-clutter echoes. The outcome is evaluated on a training dataset using statistical score indexes through the comparison with a deterministic clutter map. After discriminating the ground clutter areas, we have focused on the spatial analysis of robust and stable returns by using an automated region-merging algorithm. The temporal series of the ground-clutter statistical parameters, extracted from the spatial analysis and expressed in terms of percentile and mean values, have been used to estimate the relative clutter calibration and its uncertainty for both co-polar and differential reflectivity. The proposed methodology has been applied to a dataset collected by a C-band weather radar in southern Italy