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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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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Reducing Errors in Velocity–Azimuth Display (VAD) Wind and Deformation Retrievals from Airborne Doppler Radars in Convective Environments

The present study describes methods to reduce the uncertainty of velocity–azimuth display (VAD) wind and deformation retrievals from downward-pointing, conically scanning, airborne Doppler radars. These retrievals have important applications in data assimilation and real-time data processing. Several error sources for VAD retrievals are considered here, including violations to the underlying wind field assumptions, Doppler velocity noise, data gaps, temporal variability, and the spatial weighting function of the VAD retrieval. Specific to airborne VAD retrievals, we also consider errors produced due to the radar scans occurring while the instrument platform is in motion. While VAD retrievals are typically performed using data from a single antenna revolution, other strategies for selecting data can be used to reduce retrieval errors. Four such data selection strategies for airborne VAD retrievals are evaluated here with respect to their effects on the errors. These methods are evaluated using the second hurricane nature run numerical simulation, analytic wind fields, and observed Doppler radar radial velocities. The proposed methods are shown to reduce the median absolute error of the VAD wind retrievals, especially in the vicinity of deep convection embedded in stratiform precipitation. The median absolute error due to wind field assumption violations for the along-track and for the across-track wind is reduced from 0.36 to 0.08 m s−1 and from 0.35 to 0.24 m s−1, respectively. Although the study focuses on Doppler radars, the results are equally applicable to conically scanning Doppler lidars as well.

Charles N Helms↗
NASA NTRS2020

Scheduling Mission Reconfiguration for an Interferometry Synthetic Aperture Radar Using Deep Reinforcement Learning

This paper presents a method to intelligently adapt the baseline of a synthetic aperture radar based on Deep Rein- forcement Learning to help create plans for missions that use formation flight for Earth observation purposes. The main contribution of this paper is the initial results we have found from applying the tool to a toy mission: measuring the ver- tical structure of forests by using a synthetic aperture radar mounted on a formation of 7 satellites orbiting the Earth in a Sun Synchronous Orbit. We have found that with a reward function based on expected science return over time and fuel usage, the Deep Reinforcement Learning planner is able to create plans with positive scientific returns while minimizing fuel usage. We also find that fuel usage and collision avoid- ance planning is better done with traditional methods, as Deep Reinforcement Learning does not converge to optimal solutions.

Viros-i-Martin, Antoni↗
NASA NTRS2010

Integrating Radar Image Data with Google Maps

A public Web site has been developed as a method for displaying the multitude of radar imagery collected by NASA s Airborne Synthetic Aperture Radar (AIRSAR) instrument during its 16-year mission. Utilizing NASA s internal AIRSAR site, the new Web site features more sophisticated visualization tools that enable the general public to have access to these images. The site was originally maintained at NASA on six computers: one that held the Oracle database, two that took care of the software for the interactive map, and three that were for the Web site itself. Several tasks were involved in moving this complicated setup to just one computer. First, the AIRSAR database was migrated from Oracle to MySQL. Then the back-end of the AIRSAR Web site was updated in order to access the MySQL database. To do this, a few of the scripts needed to be modified; specifically three Perl scripts that query that database. The database connections were then updated from Oracle to MySQL, numerous syntax errors were corrected, and a query was implemented that replaced one of the stored Oracle procedures. Lastly, the interactive map was designed, implemented, and tested so that users could easily browse and access the radar imagery through the Google Maps interface.

Chapman, Bruce D.↗
NASA NTRS1986

Methods for verifying the accuracy of wind profiles

Comparisons of radar-measured winds have been made with several types of measurements not only to verify radar data but also to seek a satisfactory comparison method. Three of the comparisons that have been made with Colorado Profiler radars are summarized. Radar measurements were compared with radiosonde measurements. Infrared lidar and 915 MHz radar were compared with radiosondes. A brief radar/radar comparison was made using the 50-MHz radar and a 3-cm wavelength meteorological Doppler radar during precipitation.

Strauch, R. C.↗
NASA NTRS2019

Orbital Debris Radar Measurements from the Haystack Ultra-Wideband Satellite Imaging Radar (HUSIR): 2014-2017

For many years, the NASA Orbital Debris Program Office (ODPO) has partnered with the U.S. Department of Defense and the Massachusetts Institute of Technology Lincoln Laboratory (MIT/LL) to collect data on the orbital debris environment using the Haystack radar. These measurements are used to characterize the small debris environment in low Earth orbit (LEO), down to a noise-limited size of approximately 5 mm—depending on altitude. The Haystack radar operated by MIT Lincoln Lab underwent upgrades starting in May 2010, with operations resuming in 2014 as the Haystack Ultra-wideband Satellite Imaging Radar (HUSIR). Hence, the data collected beginning in 2014 represents the first dataset available from this upgraded sensor. HUSIR is the primary source of data used by the ODPO to statistically sample orbital debris in the 5-mm to 10-cm size regime in LEO and is a key source of data to build and validate the NASA Orbital Debris Engineering Model. In this paper, we will present recent results from measurements performed during the US Government fiscal years 2014 – 2017. Using the NASA Size Estimation Model, a method based on laboratory radar measurements of debris, we will compare the size distributions of selected orbital debris populations over this 4-year period and flux measurements of orbital debris greater than 1 cm.

Murray, James↗
NASA NTRS1993

Guide to Magellan image interpretation

An overview of Magellan Mission requirements, radar system characteristics, and methods of data collection is followed by a description of the image data, mosaic formats, areal coverage, resolution, and pixel DN-to-dB conversion. The availability and sources of image data are outlined. Applications of the altimeter data to estimate relief, Fresnel reflectivity, and surface slope, and the radiometer data to derive microwave emissivity are summarized and illustrated in conjunction with corresponding SAR image data. Same-side and opposite-side stereo images provide examples of parallax differences from which to measure relief with a lateral resolution many times greater than that of the altimeter. Basic radar interactions with geologic surfaces are discussed with respect to radar-imaging geometry, surface roughness, backscatter modeling, and dielectric constant. Techniques are described for interpreting the geomorphology and surface properties of surficial features, impact craters, tectonically deformed terrain, and volcanic landforms. The morphologic characteristics that distinguish impact craters from volcanic craters are defined. Criteria for discriminating extensional and compressional origins of tectonic features are discussed. Volcanic edifices, constructs, and lava channels are readily identified from their radar outlines in images. Geologic map units are identified on the basis of surface texture, image brightness, pattern, and morphology. Superposition, cross-cutting relations, and areal distribution of the units serve to elucidate the geologic history.

Ford, John P.↗
NASA NTRS1986

An example of scaling MST Doppler spectra using median spectra, spectral smoothing, and velocity tracing

Although automatic, computer scaling methods appeared at the start of the MST (mesosphere stratosphere troposphere) radar technique, there is a continuing need for scaling algorithms that perform editing functions and increase the sensitivity of radar by post processing. The scaling method presented is an adaptation of the method of scaling MST Doppler spectra presented by Rastogi (1984). A brief overview of this method is as follows: a median spectrum is calculated from several sequential spectra; the median noise value is subtracted from this derived spectrum; the median spectrum is smoothed; the detection/nondetection decision is made by comparing the smoothed spectrum to the variance of the smoothed noise; and if a signal is detected, then the half-power points of the smoothed echo spectrum are used to place limits on the evaluation of the first two moments of the unsmoothed median spectrum. In all of the above steps, the algorithm is guided by tracing the expected velocity range upward from the lowest range as far as possible. The method is discussed in more detail.

Green, J. L.↗
DOE OSTI2019

Can liquid cloud microphysical processes be used for vertically pointing cloud radar calibration?

Cloud radars are unique instruments for observing cloud processes, but uncertainties in radar calibration have frequently limited data quality. Thus far, no single robust method exists for assessing the calibration of past cloud radar data sets. Here, we investigate whether observations of microphysical processes in liquid clouds such as the transition of cloud droplets to drizzle drops can be used to calibrate cloud radars. Specifically, we study the relationships between the radar reflectivity factor and three variables not affected by absolute radar calibration: the skewness of the radar Doppler spectrum ( γ ), the radar mean Doppler velocity ( W ), and the liquid water path (LWP). For each relation, we evaluate the potential for radar calibration. For γ and W , we use box model simulations to determine typical radar reflectivity values for reference points. We apply the new methods to observations at the Atmospheric Radiation Measurement (ARM) sites North Slope of Alaska (NSA) and Oliktok Point (OLI) in 2016 using two 35 GHz K a -band ARM Zenith Radars (KAZR). For periods with a sufficient number of liquid cloud observations, we find that liquid cloud processes are robust enough for cloud radar calibration, with the LWP-based method performing best. We estimate that, in 2016, the radar reflectivity at NSA was about 1±1  dB too low but stable. For OLI, we identify serious problems with maintaining an accurate calibration including a sudden decrease of 5 to 7 dB in June 2016.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

On the calibration of climatological satellite rainfall measurements using a transportable ground-based polarization radar

The use of a ground-based polarization radar is proposed for the calibration of climatological satellite rainfall measurements. A distribution matching method for improving the correlations between radar reflectivity and rainfall brightness temperatures is discussed. The importance of collecting measurements of all observables at the same spatial resolution is demonstrated using GATE rainrate data (Patterson et al., 1979). The use of the matched distribution method to calibrate techniques such as HART (Atlas et al., 1988; Rosenfeld et al., 1988) is examined. Also, consideration is given to the application of polarization radar data to the interpretation of brightness temperatures.

Jameson, A. R.↗
NASA NTRS2009

Evaluation of TRMM Ground-Validation Radar-Rain Errors Using Rain Gauge Measurements

Ground-validation (GV) radar-rain products are often utilized for validation of the Tropical Rainfall Measuring Mission (TRMM) spaced-based rain estimates, and hence, quantitative evaluation of the GV radar-rain product error characteristics is vital. This study uses quality-controlled gauge data to compare with TRMM GV radar rain rates in an effort to provide such error characteristics. The results show that significant differences of concurrent radar-gauge rain rates exist at various time scales ranging from 5 min to 1 day, despite lower overall long-term bias. However, the differences between the radar area-averaged rain rates and gauge point rain rates cannot be explained as due to radar error only. The error variance separation method is adapted to partition the variance of radar-gauge differences into the gauge area-point error variance and radar rain estimation error variance. The results provide relatively reliable quantitative uncertainty evaluation of TRMM GV radar rain estimates at various times scales, and are helpful to better understand the differences between measured radar and gauge rain rates. It is envisaged that this study will contribute to better utilization of GV radar rain products to validate versatile spaced-based rain estimates from TRMM, as well as the proposed Global Precipitation Measurement, and other satellites.

Wang, Jianxin↗
DOE OSTI2019

Aperture‐Synthesis Radar Imaging With Compressive Sensing for Ionospheric Research

Abstract Inverse methods involving compressive sensing are tested in the application of two‐dimensional aperture‐synthesis imaging of radar backscatter from field‐aligned plasma density irregularities in the ionosphere. We consider basis pursuit denoising, implemented with the fast iterative shrinkage thresholding algorithm, and orthogonal matching pursuit (OMP) with a wavelet basis in the evaluation. These methods are compared with two more conventional optimization methods rooted in entropy maximization (MaxENT) and adaptive beamforming (linearly constrained minimum variance or often “Capon's Method.”) Synthetic data corresponding to an extended ionospheric radar target are considered. We find that MaxENT outperforms the other methods in terms of its ability to recover imagery of an extended target with broad dynamic range. Fast iterative shrinkage thresholding algorithm performs reasonably well but does not reproduce the full dynamic range of the target. It is also the most computationally expensive of the methods tested. OMP is very fast computationally but prone to a high degree of clutter in this application. We also point out that the formulation of MaxENT used here is very similar to OMP in some respects, the difference being that the former reconstructs the logarithm of the image rather than the image itself from basis vectors extracted from the observation matrix. MaxENT could in that regard be considered a form of compressive sensing.

Hysell, D. L.↗
NASA NTRS1989

Nonprincipal plane scattering of flat plates and pattern control of horn antennas

Using the geometrical theory of diffraction, the traditional method of high frequency scattering analysis, the prediction of the radar cross section of a perfectly conducting, flat, rectangular plate is limited to principal planes. Part A of this report predicts the radar cross section in nonprincipal planes using the method of equivalent currents. This technique is based on an asymptotic end-point reduction of the surface radiation integrals for an infinite wedge and enables nonprincipal plane prediction. The predicted radar cross sections for both horizontal and vertical polarizations are compared to moment method results and experimental data from Arizona State University's anechoic chamber. In part B, a variational calculus approach to the pattern control of the horn antenna is outlined. The approach starts with the optimization of the aperture field distribution so that the control of the radiation pattern in a range of directions can be realized. A control functional is thus formulated. Next, a spectral analysis method is introduced to solve for the eigenfunctions from the extremal condition of the formulated functional. Solutions to the optimized aperture field distribution are then obtained.

Balanis, Constantine A.↗
DOE OSTI2022

Deep-learning-guided high-resolution subsurface reflectivity imaging with application to ground-penetrating radar data

Subsurface reflectivity imaging is one of the most important geophysical characterization methods for revealing subsurface structures. In many cases, accurate subsurface reflectivity imaging is challenging because of, for example, random or coherent noise in the data and sparse source-receiver observation geometry. Here, we develop a deep-learning-guided iterative imaging method to improve subsurface structure imaging. Specifically, we train a supervised neural network to infer a noise-free, high-resolution image from a noisy, low-resolution image and use this estimated image as guidance to regularize least-squares imaging. We develop a systematic method to generate high-quality synthetic training data (data-label pairs) to train the guidance neural network. The trained neural network can provide high-fidelity predictions even for field-data images that are not in the training data. We validate our new imaging method using one synthetic and two field ground-penetrating radar data examples, and find that our method can produce clean, high-resolution subsurface reflectivity images where existing single-pass and least-squares imaging methods fail due to noise and insufficient data coverage.

58 GEOSCIENCES↗
DOE OSTI2021

Evaluation of KDP Estimation Algorithm Performance in Rain Using a Known-Truth Framework

Accurate estimation of specific differential phase ( K DP ) is necessary for rain rate estimation, attenuation correction, and hydrometeor classification algorithms. There are numerous published methods to process polarimetric radar observations of propagation differential phase shift (Φ DP ) and estimate K DP , but the corresponding K DP estimate uncertainty is unquantified. This study provides guidance on how commonly used K DP estimation algorithms perform in various environments. Here, we create numerous synthetic (“true”) K DP profiles, integrate over them to obtain “smoothed” Φ DP , and then add noise typical of S-band operational weather radar measurements. Each algorithm is applied to our noisy Φ DP profiles and compared to the true K DP profile such that the errors and uncertainty are quantified. The synthetic K DP profiles are Gaussian in shape, which allows systematic variations in their magnitude and width to determine how each algorithm performs in smooth, slowly changing K DP profiles, as well as steep profiles. Results demonstrate that algorithm performance is dependent on the Φ DP field received. These results are further supported by an error analysis of each algorithm for two more complicated synthetic K DP profiles. Some K DP algorithms allow users to change various tuning parameters; a subset of these tuning parameters is tested to provide guidance on how changing these parameters impacts algorithm performance. We then provide evidence that our known-truth framework provides insight into algorithm performance in observed data through two case studies.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1980

KU-Band rendezvous radar performance computer simulation model

The preparation of a real time computer simulation model of the KU band rendezvous radar to be integrated into the shuttle mission simulator (SMS), the shuttle engineering simulator (SES), and the shuttle avionics integration laboratory (SAIL) simulator is described. To meet crew training requirements a radar tracking performance model, and a target modeling method were developed. The parent simulation/radar simulation interface requirements, and the method selected to model target scattering properties, including an application of this method to the SPAS spacecraft are described. The radar search and acquisition mode performance model and the radar track mode signal processor model are examined and analyzed. The angle, angle rate, range, and range rate tracking loops are also discussed.

Griffin, J. W.↗