SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This paper will discuss a new multifrequency dual channel coherent radar depth sounder for sounding ice. This sounder is unique since it is a fully coherent chirp radar design to operate at low (1500') as well as high altitudes (30,000').
This paper will focus on our analysis of the imaging radar data in support of archaeological studies and subsequent ground verification of some of the features visible in the radar data.
Planetary radar astronomy is the study of Solar System entities by transmitting a radio signal toward the target and then receiving and analyzing the echo. This field of research has primarily involved observations with Earth-based radar telescopes, but also includes certain experiments with a spaceborne transmitter and/or receiver.
We report on the geological interpretation of the mapping analysis of several successful radar experiments, which are part of a program to generate a high resolution digital elevation model (DEM) of the equatorial region of Venus between Phoebe Regio and far western Ovda Regio. These experiments used radar interferometry with Arecibo transmitting and Goldstone receiving.
This study demonstrates that radar interferometry provides an important contribution towards understanding the dynamic deformation of volcanoes. By revealing large scale changes in their pre-eruption deformation rates, radar interfeometery could play an important role in volcano eruption monitoring.
The NASA/Goddard Space Flight Center’s (GSFC’s) W-band (94 GHz) Cloud Radar System (CRS) has been comprehensively updated to modern solid-state and digital technology. This W-band (94 GHz) radar flies in nadir-pointing mode on the NASA ER-2 high-altitude aircraft, providing polarimetric reflectivity and Doppler measurements of clouds and precipitation. This paper describes the design and signal processing of the upgraded CRS. It includes details on the hardware upgrades (SSPA transmitter, antenna, and digital receiver) including a new reflect array antenna and solid-state transmitter. It also includes algorithms, including internal loop-back calibration, external calibration using a direct relationship between volume reflectivity and the range-integrated backscatter of the ocean, and a modified staggered-PRF Doppler algorithm that is highly resistant to unfolding errors. Data samples obtained by upgraded CRS through recent NASA airborne science missions are provided.
Since multilayer cloud scenes are common in the atmosphere and can be an important source of uncertainty in passive satellite sensor cloud retrievals, the MODIS MOD06 and MYD06 standard cloud optical property products include a multilayer cloud detection algorithm to assist with data quality assessment. This paper presents an evaluation of the Aqua MODIS MYD06 Collection 6 multilayer cloud detection algorithm through comparisons with active Cloud Profiling Radar (CPR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products that have the ability to provide cloud vertical distributions and directly classify multilayer cloud scenes and layer properties. To compare active sensor products with an imager such as MODIS, it is first necessary to define multilayer clouds in the context of their radiative impact on cloud retrievals. Three main parameters have thus been considered in this evaluation: (1) the maximum separation distance between two cloud layers, (2) the thermodynamic phase of those layers and (3) the upper-layer cloud optical thickness. The impact of including the Pavolonis–Heidinger multilayer cloud detection algorithm, introduced in Collection 6, to assist with multilayer cloud detection has also been assessed. For the year 2008, the MYD06 C6 multilayer cloud detection algorithm identifies roughly 20% of all cloudy pixels as multilayer (decreasing to about 13% if the Pavolonis–Heidinger algorithm output is not used). Evaluation against the merged CPR and CALIOP2B-CLDCLASS-lidar product shows that the MODIS multilayer detection results are quite sensitive to how multilayer clouds are defined in the radar and lidar product and that the algorithm performs better when the optical thickness of the upper cloud layer is greater than about 1.2 with a minimum layer separation distance of 1 km. Finally, we find that filtering the MYD06 cloud optical properties retrievals using the multilayer cloud flag improves aggregated statistics, particularly for ice cloud effective radius.
With the addition of the Space Fence (SFK) radar to the Space Surveillance Network (SSN), the NASA Conjunction Assessment Risk Analysis (CARA) team now has access to radar cross-section (RCS) measurements for many Earth orbiting satellites. The CARA team has developed a process to estimate satellite sizes and masses from the SFK RCS measurement data. This study describes the processes used to filter the RCS data, defines the algorithms used to esti-mate satellite sizes and masses, and presents comparisons of estimated values against known satellite sizes and masses.
Atmospheric surface pressure and pressure profiles are essential variables in weather modeling and forecasting. Pressure gradients generate atmospheric motion and are essential to the air-sea heat exchange feedbacks within the planetary boundary layer (PBL) that lead to convective storms and heavy precipitation. Despite the importance of pressure and pressure gradients on meso-, synoptic-, and global-scale weather patterns, current technology relies almost entirely on buoy measurements over the majority of the ocean. These measurements are too sparse to capture pressure gradients of even many synoptic scale events, and leave models starved of information. NASA/Goddard Space Flight Center (GSFC), NASA/Langley Research Center (LaRC), and Tomorrow.io have begun development of the Microwave Barometric Radar and Sounder (MBARS), an airborne sensor to retrieve surface air pressure and vertical pressure profiles, especially over oceans. MBARS is a new combined active/passive microwave instrument in the O2 absorption V-band (64-70 GHz) funded by the NASA Earth Science Technology Office (ESTO) Instrument Incubator Program (IIP). This instrument consists of an innovative scanning multi-channel differential absorption radar (DAR) to provide an estimation of total atmospheric column oxygen content and thus the surface dry air pressure. MBARS also will provide hyperspectral radiometric temperature profiling to enables vertical pressure profiles using the hypsometric relationship between pressure and temperature. A co-located microwave radiometer will provide the integrated water vapor mass contribution to atmospheric pressure. This presentation will summarize the project, retrieval concept, and engineering status of the MBARS project.
High-density airspace operations with multiple aircraft type and potentially noncooperative aircraft require distributed sensor detection and tracking systems to monitor airspace for safe, autonomous flight operations for advanced air mobility, urban air mobility, and high density small uncrewed air systems (SUAS) flight concepts. This work collected data using a distributed radar and camera sensor framework during the NASA Advanced Air Mobility High Density Vertiplex project SUAS flight operation. Node locations include on an onboard SUAS, affixed to the Landing and Impact Research Facility with approximate 200ft elevation, on a tripod on first-floor roof with an approximate elevation of 20 ft, and on a tripod on a concrete pad that is approximately 4 ft above sea level. Each node includes camera, radar, and GPS.
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.