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

EVALUATING FEATURES FOR BROAD SPECIES BASED CLASSIFICATION OF BIRD OBSERVATIONS USING DUAL-POLARIZED DOPPLER WEATHER RADAR

Wind energy is one of the fastest-growing segments of the world energy market; however, wind energy facilities can have detrimental effects on wildlife, especially birds and bats. The ability to monitor vulnerable species in the vicinity of proposed wind sites could enable site selection that favors more vulnerable species, but current monitoring tools lack this classification capability. This work analyzes polarimetric and Doppler measurements of migrating birds for species based variation. A novel two stage feature extraction technique was developed to enable comparison between birds. Stage one involves mapping time changing radar measurements to the birds behavioral state in time (i.e. flapping and gliding); stage two uses this behavioral state information to produce temporal and statistical features that describe the frequency and appearance of these different behavioral states. General trends of temporal features (ex. wing-beat frequency) in the dataset match Ecological literature and validate the feature extraction approach. Preliminary clustering of bird detection data suggests possible species based subgroups of targets, although a larger dataset is needed for further validation

Multi-Radar Analysis of the 20 May 2013 Moore, Oklahoma Supercell through Tornadogenesis and Intensification

On 20 May 2013, a violent, long-track EF-5 tornado impacted Moore, Oklahoma and surrounding areas, occurring within a network of radars operating in close proximity to the supercell. This network of radars consisted of three WSR-88Ds – KTLX, KOUN, and KCRI, in addition to the PX-1000, a rapid-scan, transportable, X-band radar with 20-s temporal resolution. This analysis focused on detailing polarimetric supercell attributes leading up to tornadogenesis and through tornado intensification. Two analyses were conducted to analyze in-storm processes within the supercell – high-spatiotemporal supercell evolution using PX-1000 and volumetric characteristics of ZDR and KDP signatures using KTLX and KOUN. High-temporal observations from PX-1000 resolved three distinct rear-flank downdraft (RFD) surges, two of which occurred prior to tornadogenesis (1946:09 and 1952:27 UTC) and one coincident with tornadogenesis (1957:05 UTC). The RFD surges were characterized by transient intensifications in ΔV, an advancing ZH gradient wrapping cyclonically around the low-level mesocyclone, and a decrease in ρhv within the hook echo, likely resulting from the lofting of light debris from increased wind speeds. The second and third RFD surges are especially robust, with ΔV exceeding “tornado” threshold (> 40 m s-1) and detection of a tornado debris signature utilizing a hydrometeor classification algorithm. Patterns associated with the RFD surges suggests the possibility of brief, weak tornadoes occurring prior to tornadogenesis (1956 UTC), but ultimately resulting in the failure of the tornadoes to sustain themselves. Trends in ZDR arc and KDP foot volumetric characteristics as well as ZDR and KDP columns are also documented using KTLX and KOUN. In the times leading up to tornadogenesis, while ZDR arc remained relatively shallow, KDP foot exhibited consolidation / deepening immediately downshear of the updraft, indicating increased likelihood of tornadogenesis. Additionally, prior to tornadogenesis, ZDR and KDP columns displayed a decrease in both depth and volumetric extent, signifying a weakening updraft as a result of a strengthening downward-directed perturbation pressure gradient force. Contrary to an overall weakening trend prior to tornadogenesis, column analysis detected an updraft pulse at ~1942 UTC. Lastly, the steady decrease in column strength shifts to rapid growth just before (5 – 10 min after) tornadogenesis for KDP (ZDR) column

Bayesian Approaches to Detect and Mitigate Ground Clutter mixed with Weather Signals

Ground clutter is a long standing issue in radar meteorology, considering that it can bring significant bias to the estimations of weather moments, polarimetric parameters, rainfall rate, hydrometeor identification, etc. Bayes' theorem is introduced and applied to signal processing of weather radar signals which distinguishes it from existing empirical methods to improve data quality. Five ground clutter detection algorithms are discussed, which are the Spectrum Clutter Identification (SCI), Simple Bayesian Classifier applied to the Dual-Scan discriminants (SBC-DS), test statistic obtained from the Generalized Likelihood Ratio Test (GLRT), Simple Bayesian Classifier applied to the Dual-Pol discriminants (SBC-DP), and Simple Bayesian Classifier applied to the Dual-Pol Dual-Scan discriminants (SBC-DPDS). One ground clutter filtering algorithm is developed, which is the Bi-Gaussian Model Adaptive Processing (BGMAP). The BGMAP algorithm will be applied to the clutter contaminated gates identified by ground clutter detection algorithms. The performances of the clutter detection and filtering algorithms are evaluated using the data collected by the OU-PRIME (University of Oklahoma-Polarimetric Radar for Innovation in Meteorology and Engineering) 5-cm polarimetric radar and PX-1000 3-cm polarimetric transportable radar

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)

A polarimetric Doppler radar time‐series simulator for biological applications

The high mobility of airborne organisms makes them inherently difficult to study, motivating the use of radars and radar networks as biological surveillance tools. While the utility of radar for ecological studies has been demonstrated, a number of challenges remain in expanding and optimizing their use for surveillance of birds, bats and insects. To explore these topics, a Lagrangian simulation scheme has been developed to synthesize realistic, polarimetric, pulsed Doppler radar baseband signals from modelled flocks of biological point scatterers. This radar simulation algorithm is described, and an application is presented using an agent-based model of the nocturnal emergence of a cave-dwelling colony of Brazilian free-tailed bats (Tadarida brasiliensis). Dualpolarization radar signals for an S-band weather surveillance radar are synthesized and used to develop a new extension of the spectral velocity azimuth display for polarimetric roost-ring signature analysis, demonstrating one capability of this simulation scheme. While these developments will have direct benefits for radar engineers and meteorologists, continuing investment in radar methods such as these will have cascading effects toward improving ecological models and developing new observational techniques for monitoring aerial wildlife

CLUTTER DETECTION AND MITIGATION FOR DUAL-POLARIZATION WEATHER RADAR

Ground clutter in weather radar observations causes degradation of data quality and can lead to misinterpretation of radar echoes. It is important to detect clutter and mitigate its effects to obtain accurate weather measurements. The focus of this study is to improve the performance of clutter detection algorithms by presenting different discriminant functions. A Bayesian classifier is used to make an optimal decision based on discriminant functions to detect clutter mixed with weather echoes. The conditional probability density functions for clutter and weather signals may change and may need to be updated due to changing weather conditions, clutter, and radar parameters. Therefore, to make it more efficient, a multivariate Gaussian mixture model is presented to parametrize discriminant functions and reduce the complexity of detection algorithms. The model parameters are estimated based on the maximum likelihood, using the Expectation-Maximization (ML-EM) method. A dual-polarization clutter filtering algorithm is also presented to mitigate ground clutter effects on weather radar measurements. A multivariate Gaussian model is introduced to parametrize clutter and weather power spectrums, and the Maximum A Posterior (MAP) method is used to estimate weather components. Instead of using a random phase, the phase of the retrieved weather spectrum is estimated based on the statistical properties of dual-polarization weather signals. The performance of the clutter detection and filtering algorithms are shown by applying them to the radar data collected by the national WSR-88D (KOUN) polarimetric radar and are compared to existing detection and filtering algorithms. It is shown that the proposed algorithms can effectively mitigate clutter effects and substantially improve polarimetric weather radar data quality

Challenges in measuring winter precipitation : Advances in combining microwave remote sensing and surface observations

Globally, snow influences Earth and its ecosystems in several ways by having a significant impact on, e.g., climate and weather, Earth radiation balance, hydrology, and societal infrastructures. In mountainous regions and at high latitudes snowfall is vital in providing freshwater resources by accumulating water within the snowpack and releasing the water during the warm summer season. Snowfall also has an impact on transportation services, both in aviation and road maintenance. Remote sensing instrumentation, such as radars and radiometers, provide the needed temporal and spatial coverage for monitoring precipitation globally and on regional scales. In microwave remote sensing, the quantitative precipitation estimation is based on the assumed relations between the electromagnetic and physical properties of hydrometeors. To determine these relations for solid winter precipitation is challenging. Snow particles have an irregular structure, and their properties evolve continuously due to microphysical processes that take place aloft. Hence also the scattering properties, which are dependent on the size, shape, and dielectric permittivity of the hydrometeors, are changing. In this thesis, the microphysical properties of snowfall are studied with ground-based measurements, and the changes in prevailing snow particle characteristics are linked to remote sensing observations. Detailed ground observations from heavily rimed snow particles to openstructured low-density snowflakes are shown to be connected to collocated triple-frequency signatures. As a part of this work, two methods are implemented to retrieve mass estimates for an ensemble of snow particles combining observations of a video-disdrometer and a precipitation gauge. The changes in the retrieved mass-dimensional relations are shown to correspond to microphysical growth processes. The dependence of the C-band weather radar observations on the microphysical properties of snow is investigated and parametrized. The results apply to improve the accuracy of the radar-based snowfall estimation, and the developed methodology also provides uncertainties of the estimates. Furthermore, the created data set is utilized to validate space-borne snowfall measurements. This work demonstrates that the C-band weather radar signal propagating through a low melting layer can significantly be attenuated by the melting snow particles. The expected modeled attenuation is parametrized according to microphysical properties of snow at the top of the melting layer

Polarimetric Synthetic Aperture Radar

This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans