A Validation Procedure for a Polarimetric Weather Radar Signal Simulator

A simulator of weather radar signals can be exploited as a useful reference for many applications, such as weather forecasting and nowcasting models or for training artificial intelligence systems designed to optimize the trajectory of aircrafts with the purpose to reduce flight hazard and fuel consumption. However, before being used, it must be accurately examined under different operating conditions, in order to evaluate the consistency of the outputs produced. In this paper, we present a validation procedure for a newly developed polarimetric weather radar simulator (POWERS). The goal is to assess the ability of the simulator to deal with any kind of input data, be they simulated and real raindrop-size distributions, or outputs generated by numerical weather prediction models. Three different approaches are proposed, each providing a connection between meteorological inputs and the radar observables simulated by POWERS. The analysis is carried out in the case of rainfall, both at S- and X-bands

GNSS transpolar earth reflectometry exploriNg system (G-TERN): mission concept

The global navigation satellite system (GNSS) Transpolar Earth Reflectometry exploriNg system (G-TERN) was proposed in response to ESA's Earth Explorer 9 revised call by a team of 33 multi-disciplinary scientists. The primary objective of the mission is to quantify at high spatio-temporal resolution crucial characteristics, processes and interactions between sea ice, and other Earth system components in order to advance the understanding and prediction of climate change and its impacts on the environment and society. The objective is articulated through three key questions. 1) In a rapidly changing Arctic regime and under the resilient Antarctic sea ice trend, how will highly dynamic forcings and couplings between the various components of the ocean, atmosphere, and cryosphere modify or influence the processes governing the characteristics of the sea ice cover (ice production, growth, deformation, and melt)? 2) What are the impacts of extreme events and feedback mechanisms on sea ice evolution? 3) What are the effects of the cryosphere behaviors, either rapidly changing or resiliently stable, on the global oceanic and atmospheric circulation and mid-latitude extreme events? To contribute answering these questions, G-TERN will measure key parameters of the sea ice, the oceans, and the atmosphere with frequent and dense coverage over polar areas, becoming a “dynamic mapper”of the ice conditions, the ice production, and the loss in multiple time and space scales, and surrounding environment. Over polar areas, the G-TERN will measure sea ice surface elevation (<;10 cm precision), roughness, and polarimetry aspects at 30-km resolution and 3-days full coverage. G-TERN will implement the interferometric GNSS reflectometry concept, from a single satellite in near-polar orbit with capability for 12 simultaneous observations. Unlike currently orbiting GNSS reflectometry missions, the G-TERN uses the full GNSS available bandwidth to improve its ranging measurements. The lifetime would be 2025-2030 or optimally 2025-2035, covering key stages of the transition toward a nearly ice-free Arctic Ocean in summer. This paper describes the mission objectives, it reviews its measurement techniques, summarizes the suggested implementation, and finally, it estimates the expected performance.Peer ReviewedPostprint (published version

A support vector machine hydrometeor classification algorithm for dual-polarization radar

An algorithm based on a support vector machine (SVM) is proposed for hydrometeor classification. The training phase is driven by the output of a fuzzy logic hydrometeor classification algorithm, i.e., the most popular approach for hydrometer classification algorithms used for ground-based weather radar. The performance of SVM is evaluated by resorting to a weather scenario, generated by a weather model; the corresponding radar measurements are obtained by simulation and by comparing results of SVM classification with those obtained by a fuzzy logic classifier. Results based on the weather model and simulations show a higher accuracy of the SVM classification. Objective comparison of the two classifiers applied to real radar data shows that SVM classification maps are spatially more homogenous (textural indices, energy, and homogeneity increases by 21% and 12% respectively) and do not present non-classified data. The improvements found by SVM classifier, even though it is applied pixel-by-pixel, can be attributed to its ability to learn from the entire hyperspace of radar measurements and to the accurate training. The reliability of results and higher computing performance make SVM attractive for some challenging tasks such as its implementation in Decision Support Systems for helping pilots to make optimal decisions about changes in the flight route caused by unexpected adverse weather

Rainfall rate retrieval in presence of path attenuation using C-band polarimetric weather radars

Weather radar systems are very suitable tools for the monitoring of extreme rainfall events providing measurements with high spatial and temporal resolution over a wide geographical area. Nevertheless, radar rainfall retrieval at C-band is prone to several error sources, such as rain path attenuation which affects the accuracy of inversion algorithms. In this paper, the so-called rain profiling techniques (namely the surface reference method FV and the polarimetric method ZPHI) are applied to correct rain path attenuation and a new neural network algorithm is proposed to estimate the rain rate from the corrected measurements of reflectivity and differential reflectivity. A stochastic model, based on disdrometer measurements, is used to generate realistic range profiles of raindrop size distribution parameters while a T-matrix solution technique is adopted to compute the corresponding polarimetric variables. A sensitivity analysis is performed in order to evaluate the expected errors of these methods. It has been found that the ZPHI method is more reliable than FV, being less sensitive to calibration errors. Moreover, the proposed neural network algorithm has shown more accurate rain rate estimates than the corresponding parametric algorithm, especially in presence of calibration errors

Phased-Array Radar System Simulator (PASIM): Development and Simulation Result Assessment

In this paper, a system-specific phased-array radar system simulator was developed, based on a time-domain modeling and simulation method, mainly for system performance evaluation of the future Spectrum-Efficient National Surveillance Radar (SENSR). The goal of the simulation study was to establish a complete data quality prediction method based on specific radar hardware and electronics designs. The distributed weather targets were modeled using a covariance matrix-based method. The data quality analysis was conducted using Next-Generation Radar (NEXRAD) Level-II data as a basis, in which the impact of various pulse compression waveforms and channel electronic instability on weather radar data quality was evaluated. Two typical weather scenarios were employed to assess the simulator’s performance, including a tornado case and a convective precipitation case. Also, modeling of some demonstration systems was evaluated, including a generic weather radar, a planar polarimetric phased-array radar, and a cylindrical polarimetric phased-array radar. Corresponding error statistics were provided to help multifunction phased-array radar (MPAR) designers perform trade-off studies.Funding: The work was supported by NOAA/NSSL through Grant # NA16OAR4320115.A Open access fees fees for this article provided whole or in part by OU Libraries Open Access Fund. Acknowledgments: We thank Ramesh Nepal from the Intelligent Aerospace Radar Team (IART) of School of Electrical and Computer Engineering, the University of Oklahoma as the initial user of the MATLAB Phased-Array System Toolbox for weather radar simulations at OU, who gave numerous discussions regarding PASIM implementation. We deeply thank Honglei Chen from MathWorks Inc., who provided important guidance and support to the weather radar signal statistical modeling and MATLAB tool.Ye

Application of Machine Learning to Multiple Radar Missions and Operations

This dissertation investigated the application of Machine Learning (ML) in multiple radar missions. With the increasing computational power and data availability, machine learning is becoming a convenient tool in developing radar algorithms. The overall goal of the dissertation was to improve the transportation safety. Three specific applications were studied: improving safety in the airport operations, safer air travel and safer road travel. First, in the operations around airports, lightning prediction is necessary to enhance safety of the ground handling workers. Information about the future lightning can help the workers take necessary actions to avoid lightning related injuries. The mission was to investigate the use of ML algorithms with measurements produced by an S-band weather radar to predict the lightning flash rate. This study used radar variables, single pol and dual-pol, measured throughout a year to train the machine learning algorithm. The effectiveness of dual-pol radar variables for lighting flash rate prediction was validated, and Pearson's coefficient of about 0.88 was achieved in the selected ML scheme. Second, the detection of High Ice Water Content (HIWC),which impact the jet engine operations at high altitudes, is necessary to improve the safety of air transportation. The detection information help aircraft pilots avoid hazardous HIWC condition. The mission was to detect HIWC using ML and the X-band airborne weather radar. Due to the insufficiency of measured data, radar data was synthesized using an end-to-end airborne weather system simulator. The simulation employed the information about ice crystals' particle size distribution (PSDs), axial ratios, and orientation to generate the polarimetric radar variables. The simulated radar variables were used to train the machine learning to detect HIWC and estimate the IWC values. Pearson's coefficient of about 0.99 was achieved for this mission. The third mission included the improvement of angular resolution and explored the machine learning based target classification using an automotive radar. In an autonomous vehicle system, the classification of targets enhances the safety of ground transportation. The angular resolution was improved using Multiple Input Multiple Output (MIMO) techniques. The mission also involved classifying the targets (pedestrian vs. vehicle) using micro-Doppler features. The classification accuracy of about 94% was achieved

Quantitative Analysis of Rapid-Scan Phased Array Weather Radar Benefits and Data Quality Under Various Scan Conditions

Currently, NEXRAD provides weather radar coverage for the contiguous United States. It is believed that a replacement system for NEXRAD will be in place by the year 2040, where a major goal of such a system is to provide improved temporal resolution compared to the 5-10-min updates of NEXRAD. In this dissertation, multiple projects are undertaken to help achieve the goals of improved temporal resolution, and to understand possible scanning strategies and radar designs that can meet the goal of improved temporal resolution while either maintaining (or improving) data quality. Chapter 2 of this dissertation uses a radar simulator to simulate the effect of various scanning strategies on data quality. It is found that while simply reducing the number of pulses per radial decreases data quality, other methods such as beam multiplexing and radar imaging/digital beamforming offer significant promise for improving data quality and/or temporal resolution. Beam multiplexing is found to offer a speedup factor of 1.7-2.9, while transmit beam spoiling by 10 degrees in azimuth can offer speedup factors up to ~4 in some regions. Due to various limitations, it is recommended that these two methods be used judiciously for rapid-scan applications. Chapter 3 attempts to quantify the benefits of a rapid-scan weather radar system for tornado detection. The first goal of Chapter 3 is to track the development of a common tornado signature (tornadic debris signature, or TDS) and relate it to developments in tornado strength. This is the first study to analyze the evolution of common tornado signatures at very high temporal resolution (6 s updates) by using a storm-scale tornado model and a radar emulator. This study finds that the areal extent of the TDS is correlated with both debris availability and with tornado strength. We also find that significant changes in the radar moment variables occur on short (sub-1-min) timescales. Chapter 3 also shows that the calculated improvement in tornado detection latency time (137-207 s) is greater than that provided by theory alone (107 s). Together, the two results from Chapter 3 emphasize the need for sub-1-min updates in some applications such as tornado detection. The ability to achieve these rapid updates in certain situations will likely require a combination of advanced scanning strategies (such as those mentioned in Chapter 2) and adaptive scanning. Chapter 4 creates an optimization-based model to adaptively reallocate radar resources for the purpose of improving data quality. This model is primarily meant as a proof of concept to be expanded to other applications in the future. The result from applying this model to two real-world cases is that data quality is successfully improved in multiple areas of enhanced interest, at the expense of worsening data quality in regions where data quality is not as important. This model shows promise for using adaptive scanning in future radar applications. Together, these results can help the meteorological community understand the needs, challenges, and possible solutions to designing a replacement system for NEXRAD. All of the techniques studied herein either rely upon (or are most easily achieved by) phased array radar (PAR), which further emphasizes the utility of PAR for achieving rapid updates with sufficient data quality. It is hoped that the results in this dissertation will help guide future decisions about requirements and design specifications for the replacement system for NEXRAD

Multistatic Passive Weather Radar

Practical and accurate estimation of three-dimensional wind fields is an ongoing challenge in radar meteorology. Multistatic (single transmitter / multiple receivers) radar architectures offer a cost effective solution for obtaining the multiple Doppler measurements necessary to achieve such estimates. In this work, the history and fundamental concepts of multistatic weather radar are reviewed. Several developments in multistatic weather radar enabled by recent technological progress, such as the widespread availability of high performance single-chip RF transceivers and the proliferation of phased array weather radars, are then presented. First, a network of compact, low-cost passive receiver prototypes is used to demonstrate a set of signal processing techniques that have been developed to enable transmitter / receiver synchronization through sidelobe radiation. Next, a pattern synthesis technique is developed which allows for the use of sidelobe whitening to mitigate velocity biases in multistatic radar systems. The efficacy of this technique is then demonstrated using a multistatic weather radar system simulator

CYLINDRICAL POLARIMETRIC PHASED ARRAY RADAR DEMONSTRATOR: PERFORMANCE ASSESSMENT AND WEATHER MEASUREMENTS

A desirable candidate for future weather observation is a polarimetric phased array radar (PPAR), which is capable of both using polarimetry for multi-parameter measurements and the fast-scan proficiency of the PAR. However, it is challenging to collect high-quality polarimetric radar data of weather with a planar PPAR (PPPAR), whose beam and polarization characteristics change with the electronic beam direction, causing geometrically induced cross-polarization coupling, sensitivity losses, and measurement biases when the PPPAR beam is steered away from the broadside. As an alternative to PPPAR, the concept of cylindrical polarimetric phased array radar (CPPAR) was proposed, which has scan-invariant beam characteristics in azimuth and polarization purity in all directions using commutating scan, thus enables high quality polarimetric weather measurements. To validate the CPPAR concept, a small-scale CPPAR demonstrator has been jointly developed by the Advanced Radar Research Center (ARRC) at the University of Oklahoma (OU) and the National Severe Storms Laboratory (NSSL) of NOAA. This dissertation presents the results of initial weather measurements, shows the performance of the CPPAR demonstrator, and evaluates the polarimetric data quality that has been achieved. The system specifications and field tests of the CPPAR demonstrator are provided, including system overview, waveform design and verification, pattern optimization and far-field tests. In addition, three methods of system calibration are introduced and compared, including calibration with an external source, calibration with weather measurements of mechanical scan, and calibration with ground clutter. It is found that calibration with weather measurements of mechanical scan has the best performance and it is applied on the CPPAR demonstrator for the first time, which effectively improved the beam-to-beam consistency and radar data quality in commutating beam electronic scan by minimizing gain and beamwidth variations. Performance of the CPPAR is assessed through system simulation and weather measurements. The CPPAR is evaluated through an end-to-end phased array radar system simulator (PASIM). The simulation framework, weather returns modeling, antenna pattern, channel electronics, and simulation results of CPPAR, as well as comparison with those that would be obtained with a PPPAR, are provided. Also, weather measurements of a few convective precipitation cases and a stratiform precipitation case made with the CPPAR, employing the single beam mechanical scan and commutating beam electronic scan respectively, are presented. First, a qualitative comparison is made between the CPPAR and a nearby operational NEXRAD. Then a quantitative comparison is conducted between the mechanical scan and electronic scan, and error statistics are estimated and discussed. In addition, a theoretical explanation of a feature of the commutating beam electronic scan in clutter detection that is different from mechanical scan is presented and verified by measurements in clear air conditions with the CPPAR. Moreover, clutter detection results based on multi-lag phase structure function, dual-scan cross-correlation coefficient, copolar correlation coefficient, and differential reflectivity obtained from both electronic scan and mechanical scan modes of the CPPAR are compared