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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Daily files containing radar reflectivity and mean Doppler velocity collected from vertically pointing W-band radar during the Macquarie Island Cloud & Radiation Experiment (MICRE): April 2016 to March 2017.
Abstract The increase in iceberg discharge into the polar oceans highlights the importance of understanding how quickly icebergs are deteriorating and where the resulting freshwater injection is occurring. Recent advances in quantifying iceberg deterioration through combinations of modeling, remote sensing and direct in situ measurements have successfully calculated overall ablation rates, and surface and sidewall ablation; however, in situ measurements of basal melt rates have been difficult to obtain. Radar has successfully measured iceberg thickness, but repeat measurements, which would capture a change in iceberg thickness with time, have not yet been collected. Here we test the applicability of using an on-iceberg autonomous phase-sensitive radar (ApRES) to quantify basal ablation rates of a large (~800 m long) non-tabular Arctic iceberg during an intensive 2019 summer field campaign in Sermilik Fjord, southeast Greenland. We find that ApRES can be used to measure basal ablation even over a short deployment period (10 d), and also provide a lower bound on sidewall melt. This study fills a critical gap in iceberg research and pushes the limits of field instrumentation.
Abstract The Back‐Projection Algorithm (BPA) is a time‐domain‐matched filtering technique to form synthetic aperture radar (SAR) images. To produce high‐quality BPA images, precise navigation data for the radar platform must be known. Errors in position, velocity, or attitude result in improperly formed images that are corrupted by shifting and blurring. The contribution of this paper is the development of analytical expressions that characterise the relationship between navigation errors and image formation errors from an inertial navigation point of view, where trajectory estimation errors in position, velocity, and attitude propagate through time and cause compounding errors in the vehicle state vector. These analytical expressions are verified via simulated image formation and real‐data image formation.
To probe the cosmic particle flux at the highest energies, large volumes of dense material like ice have to be monitored. This can be achieved by exploiting the radio signal. In this work, we provide a macroscopic model to predict the radar echo signatures found when a radio signal is reflected from a cosmic-ray or neutrino-induced particle cascade propagating in a dense medium like ice. Its macroscopic nature allows for an energy independent run-time, taking less than 10 s for simulating a single scatter event. As a first application, we discuss basic signal properties and simulate the expected signal for the T-576 beam-test experiment at the Stanford Linear Accelerator Center. Finally, we find good signal strength agreement with the only observed radar echo from a high-energy particle cascade to date.
We report here the observation of radar echoes from the ionization trails of high-energy particle cascades. Data were taken at the SLAC National Accelerator Laboratory, where the full electron beam ( ~ 10 9 e - at ~ 10 GeV / e - ) was directed into a plastic target to simulate an ultrahigh-energy neutrino interaction. The target was interrogated with radio waves, and coherent radio reflections from the cascades were detected with properties consistent with theoretical expectations. This is the first definitive observation of radar echoes from high-energy particle cascades, which could lead to a viable neutrino detection technology for energies ≳ 10 16 eV .
Airborne radar for intelligence, surveillance, and reconnaissance has become an indispensable tool, found in both crewed and uncrewed platforms. These radars are usually multimode to engage missions for all manner of phenomenology detection and characterization, and have a long history of success for their intended purposes. Nevertheless, the advance of technology is facilitating enhanced exploitation of generally existing capabilities, as well as enhanced utility with new capabilities. In conclusion, the author identifies several areas of promising research and subsequent development that would provide significant and substantial capability enhancements.
Radars were studied for measuring the moisture content of wood-based materials because of their non-destructive nature and their ability to measure the moisture content of several layers at once. These convenient characteristics enable a quick and non-invasive method to find areas of high moisture content in building envelopes so that problems can be fixed before the structure degrades. The need to operate the radar at millimeter ranges required the development of new techniques and methods for analyzing and calculating the moisture content of individual layers of building material. The techniques are providing accurate results for oriented strand board, and efforts are continuing for investigating other construction materials, meeting FCC requirements, and building a system that will be easy to deploy.
RadTraQ is a python library housing routines related to assessing and monitoring the quality of a radar's quality and calibration. This library is a work in progress, currently housing scripts to plot radar CFADs, plot and analyze corner reflector scans, calculate noise floors, create cloud masks, and plot up average profiles for comparisons.
CloudSat’s Cloud Profiling Radar is a valuable tool for remotely monitoring high-latitude snowfall, but its ability to observe hydrometeor activity near the Earth’s surface is limited by a radar blind zone caused by ground clutter contamination. This study presents the development of a deeply supervised U-Net-style convolutional neural network to predict cold season reflectivity profiles within the blind zone at two Arctic locations. The network learns to predict the presence and intensity of near-surface hydrometeors by coupling latent features encoded in blind zone-aloft clouds with additional context from collocated atmospheric state variables (i.e., temperature, specific humidity, and wind speed). Results show that the U-Net predictions outperform traditional linear extrapolation methods, with low mean absolute error, a 38% higher Sørensen–Dice coefficient, and vertical reflectivity distributions 60% closer to observed values. The U-Net is also able to detect the presence of near-surface cloud with a critical success index (CSI) of 72% and cases of shallow cumuliform snowfall and virga with 18% higher CSI values compared to linear methods. An explainability analysis shows that reflectivity information throughout the scene, especially at cloud edges and at the 1.2-km blind zone threshold, along with atmospheric state variables near the tropopause, are the most significant contributors to model skill. This surface-trained generative inpainting technique has the potential to enhance current and future remote sensing precipitation missions by providing a better understanding of the nonlinear relationship between blind zone reflectivity values and the surrounding atmospheric state.
Phased array radars (PARs) are a promising observing technology, at the cusp of being available to the broader meteorological community. PARs offer near instantaneous sampling of the atmosphere with flexible beam forming, multi-functionality, low operational and maintenance costs, and without mechanical inertia limitations. These PAR features are transformative compared to those offered by our current reflector-based meteorological radars. The integration of PARs into meteorological research has the potential to revolutionize the way we observe the atmosphere. The rate of adoption of PARs in research will depend on many factors including i) the need to continue educating the scientific community on the full technical capabilities and trade-offs of PARs through an engaging dialogue with the science and engineering communities and ii) the need to communicate the breadth of scientific bottlenecks that PARs can overcome in atmospheric measurements and the new research avenues that are now possible using PARs in concert with other measurement systems. The former is the subject of a companion article that focuses on PAR technology while the latter is the objective here.
The retrieval of hail kinetic energy with weather radars or its simulation in numerical models is challenging because of the shape complexity and variable density of hailstones. We combine 3D scans of individual hailstones with measurements of the particle size distributions (PSD) and T-matrix calculations to understand how hail reflectivity Z changes when approximating hailstones as spheroids, as compared to the realistic shapes obtained by 3D scanning technology. Additionally, recent terminal velocity relations are used to compare Z to the hail kinetic energy flux E ˙ . We parameterize the hail backscattering cross sections at L, S, C, and X bands as a function of size between 0.5 and 5.0 cm, matching the range of the observed PSDs. The scattering calculations use the T-matrix method for size parameters below 1.0 and the discrete dipole approximation (DDA) method otherwise. The DDA calculations are done for 48 digital models of realistic hailstones of sizes between 1 and 5 cm. The DDA cross sections are calculated for multiple orientations and averaged assuming a fully random orientation distribution to provide a single value per hailstone. The T-matrix reflectivity assuming solid ice spheres presents negligible differences to DDA results for size parameters below 1.0. Therefore, T matrix was used to fill in the gaps left by the DDA calculations. The results are mapped to the same size bins of the observed PSDs, allowing the calculation of the radar reflectivity. This is then correlated to E ˙ , allowing a potential improvement of past retrieval methods of E ˙ from Z in multiple wavelengths.
On 10 November 2018, during the RELAMPAGO field campaign in Argentina, South America, a thunderstorm with supercell characteristics was observed by an array of mobile observing instruments, including three Doppler on Wheels (DOW) radars. This is believed to be the first such storm ever sampled by multiple Doppler radars in South America, and one of perhaps two such sets of observations in the Southern Hemisphere. The observational strategies employed within the Córdoba Province on 10 November are described, as also is the evolution of the initial deep convective clouds through their organization into a supercell. In contrast to the archetypal supercell, the updraft rotation in this storm was rather short lived (~20 min), as also was the occurrence of damaging hail, causing us to question whether this indeed was indeed a supercell. However, additional data collected within the Córdoba Province provide us with evidence of other storms with this behavior, which appears to be attributable in part to effects of the local terrain. Thus, the structure and evolution of this storm documented during RELAMPAGO may be typical in this region.
Modern high-performance radar systems are employing ever-more Digital Signal Processing (DSP), replacing ever-more formerly analog components. Precisely predicting the performance of digital filters and correlators requires an awareness of some of the finer points and characteristics of digital filters. We examine a representative radar receiver DSP chain that is processing a Linear Frequency Modulated (LFM) chirp.
Synthetic Aperture Radar (SAR) projects a 3-D scene’s reflectivity into a 2-D image. In doing so, it generally focusses the image to a surface, usually a ground plane. Consequently, scatterers above or below the focal/ground plane typically exhibit some degree of distortion manifesting as a geometric distortion and misfocusing or smearing. Limits to acceptable misfocusing define a Height of Focus (HOF), analogous to Depth of Field in optical systems. This may be exacerbated by the radar’s flightpath during the synthetic aperture data collection. It might also be exploited for target height estimation and offer insight to other height estimation techniques.
Security systems that protect the nation’s critical facilities must be capable of detecting physical intrusions in all weather conditions. Intrusion detection sensors in a perimeter with a high nuisance alarm rate (NAR) significantly undermine detection performance and degrade security system effectiveness. This research demonstrated a fused sensor system that can differentiate foliage and weather-induced nuisance alarms from those caused by intruders, providing reliable detection within a two-fence perimeter or beyond the fence. A key element of this work is the creation and application of a “deliberate motion algorithm” that fuses alarm data from radar and video analytics to create video motion detection fused radar system. The two-layer architecture of the algorithm uses machine learning, multi-hypothesis tracking, and Dynamic Bayes Nets to differentiate intruder alarms from weather induced alarms.
This document contains the final report for the midyear LDRD titled "Extension of Interferometric Synthetic Aperture Radar to Multiple Phase-Centers." This report presents an overview of several methods for approaching the two-target in layover problem that exists in interferometric synthetic aperture radar systems. Simulation results for one of the methods are presented. In addition, a new direct approach is introduced.
The U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility deployed many instruments on board a German ice breaker, the Research Vessel (RV) Polarstern, for one year from October 2019 to October 2020. The purpose of the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) field campaign was to study the decline in the sea-ice pack around the North Pole, and what factors may be at play. After the campaign ended, efforts were undertaken to provide a calibrated radar data set for future studies. MOSAiC presented new challenges to this process, as existing methodologies often were not applicable for the frozen environment with little to no ground clutter, and concurrent engineering updates or calibrations could not be accomplished once the ship set off. Like the previous Cloud, Aerosol, and Complex Terrain Interactions (CACTI) and Cold-air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) documentation (Hardin et al. 2020, Matthews et al. 2023), the data correction and calibration process is known in ARM as creating a “b1” datastream. This means that the radar datastreams have been well characterized to the best possible quality. This report will detail the status of the raw “a1” level data sets during the MOSAiC campaign, the corrections that were applied to create the “b1” data files, and the details of the applied methods.
Development of a proof-of-concept solution consisting of a network of radars to identify and discriminate an object based on its fixed position and reports on its movement with the resolution of a few centimeters. This includes scaling the solution, addressing other critical requirements (multi-look radar to overcome occlusions and obstructions, interference), and also the ability to scale up to large warehouses. Finally, improved higher-dimensional resolution will be developed.