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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Improving the Time Resolution of Hyper-Spectral Infrared Sounding Observations With Trajectory Enhancement

Low Earth orbit (LEO) hyper-spectral infrared (IR) sounders have significant potential for characterizing the complex evolution of thermodynamic environments favorable for convective initiation and ongoing convection. The snapshots at fixed local times are unable to provide the temporal resolution needed to resolve the rapidly evolving convective environment. A novel methodology using trajectory modeling coupled with satellite soundings was developed to create proximity soundings near NCEI Storm Events to investigate differences in severe weather environments (Kalmus et al., 2019, https://doi.org/10.1175/MWR-D-18-0055.1). This methodology was recently extended to entire satellite swaths forward in time up to six hours into the future and was evaluated during NOAA’s Hazardous Weather Testbed (HWT) in a quasi-operational setting (Kahn et al., 2023, https://doi.org/10.1175/WAF-D-22-0204.1). This methodology is based on parcel forward-trajectory calculations from the satellite observing time to recreate future soundings of temperature and moisture at regularly gridded intervals in space and time. Using coincident Multi-Radar Multi-Sensor (MRMS) rainfall estimates, we show that convective available potential energy (CAPE) is increased and convective inhibition (CIN) is decreased at times and locations where convection initiated. This methodology was evaluated with ERA5 data sampled to mimic the satellite observing swaths (Richardson et al., 2023, https://doi.org/10.5194/egusphere-2023-97). Approximately 60–90% of the temporal and spatial variability in temperature and humidity is explained by parcel advection using trajectory modeling. This method will be applied to the multi-decadal LEO satellite record and could be used to examine capabilities of future satellite missions such as the GeoXO mission.

Emily Berndt↗
DOE OSTI2022

Concept of Operations of Next-Generation Traffic Control Utilizing Infrastructure-Based Cooperative Perception

This paper provides a system architecture for an infrastructure-based cooperative perception fusion engine for next-generation traffic control. This engine will provide a complete state-space digital representation with measurable accuracy to support a wide-range of applications. The architecture includes inputs, functional flow, data standardization recommendations, outputs, and supported applications. The cooperative perception engine addresses critical needs with respect to accelerating the benefits of automation through intelligent roadway infrastructure, which complements and accelerates connected and automated vehicle (CAV) technology. The cooperative perception acquires and fuses information from sensors (radar, LiDAR, and cameras) and CAVs to perceive roadway traffic states of moving objects, creates a complete 3D digital representation of that state-space, and communicates it to downstream application such as intelligent signal control, safety and energy applications, and cooperate driving applications. The intelligent roadway infrastructure approach, as opposed to a vehicle-centric approach, is more scalable because it can be deployed to the roughly 300,000 signalized intersections more readily than over 300 million vehicles in the United States, and accrues early-stage benefits equitable to all roadway users addressing safety, equity, fuel efficiency, and greenhouse gas reduction.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Concept of Operations of Next-Generation Traffic Control Utilizing Infrastructure-Based Cooperative Perception: Preprint

This paper puts forth a system architecture for an infrastructure-based cooperative perception (CP) fusion engine, to provide a complete state-space digital representation, with measurable accuracy, to support a wide-range of applications. The architecture includes the inputs, functional flow, data standardization recommendations, outputs and supported applications. The CP engine addresses critical needs with respect to accelerating the benefits of automation through intelligent roadway infrastructure (IRI), that complements and accelerates connected and automated vehicle (CAV) technology. that the CP acquires and fuses information from sensors (radar, LiDAR, and cameras), and CAVs to intelligently perceive roadway traffic states of all moving objects, create a complete three-dimensional digital representation of that state-space, and communicate it to downstream application such as intelligent signal control, safety and energy applications, and cooperate driving applications for CAVs as examples. The IRI approach, as opposed to a vehicle centric approach, is found to be more scalable in that it can deployed to the roughly 300,000 signalized intersections more readily than the over 300 million vehicles in the US, and accrues early-stage benefits equitable to all roadway users addressing safety, equity, fuel efficiency, and GHG reduction.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Final Technical Report: Moving ASR Cloud Microphysical Retrievals Beyond the Vertical Column

Clouds of all types play important roles in atmospheric heating, surface heating and emission of radiation, hence energy, to space. Clouds not only affect atmospheric heating via their impacts on solar and terrestrial radiation, they also release kinetic energy to the atmosphere when they form and they extract kinetic energy from the atmosphere when they evaporate. To understand the impacts of clouds on weather and climate, one must be able to make accurate observations of the amount of water associated with them and the impact of this water on atmospheric and surface heating rates. One important cloud type is liquid water clouds. Liquid clouds are often found in the boundary layers of Earth at altitudes from just above the surface to approximately 2000 m above it, at a variety of temperatures, including temperatures below the freezing point of liquid water (0 °C). As a result, they are important constituents over the world’s oceans as well as over cold, ice-covered surfaces such as in the Arctic. Observationally estimating the total amount of liquid contained in a column through a liquid water cloud can now be accomplished to acceptable accuracies (except for the thinnest, lowest liquid containing clouds) using instruments called passive microwave radiometers. However, observationally determining the spatial distribution of liquid within these columns has proven to be difficult. The focus of this project was on the application of two radars operating at two different frequencies to retrieve the distribution of liquid water within columns passing through liquid water clouds. Motivation in this project for using two different radars operating at different frequencies is that as overlapping (in space) beams from the two radars pass through the same column of liquid water cloud absorption of power from one beam (the 94-GHz beam) is greater than absorption of power from the other beam (the 35-GHz beam). Therefore, the difference in returned powers between the two radar beams with distance from the radars is proportional to the amount of liquid water that the two beams have passed through. Therefore, one can use the differences in power between the two beams with distance through a liquid water cloud to determine the distribution of liquid water throughout the cloud. The two radars used in this study are the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program Ka-band Scanning ARM Cloud Radar (Ka-SACR) and the W-band Scanning ARM Cloud Radar (W-SACR). When this projected started in 2013, the Ka- and W-band SACRs were new technology, state-of-the-art sensors. To put together a sufficiently diverse and big set of test cases for the study, data from the SACRs deployed to NSA, SGP, TCAP and MAGIC were used, leading to a test set composed of 65 hours of suitable data spread over approximately 17 case study days. Using the Ka- and W-band SACR data from the case study periods, vertical distributions of liquid water content through liquid water clouds were retrieved. Because there are no other data sets with which to compare the retrievals, distributions of liquid water through a column were summed and compared to the total amounts of liquid water obtained by retrievals applied to passive microwave radiometer observations. The total amounts of liquid water (in units of millimeters, or the thickness in millimeters of the total liquid water in a vertical column if allowed to pool at the bottom of the column) between the two retrievals agreed to within 0.060 mm with a standard deviation of 0.31 mm, or to within 0.016 mm with a standard deviation of 0.12 mm once outliers were removed. The standard deviation of 0.12 mm was fairly independent of the total amount of liquid water in the clouds, indicating that retrieval errors were larger for clouds with smaller amounts of liquid in them. Errors in the retrieved total amounts of liquid were also found to be sensitive to the a priori information that was fed into the retrieval approach. Overall, standard deviations of 0.12 mm are sufficient for some cloud studies but for radiation studies standard deviations on the order of 0.02 mm need to be achieved for clouds with the lowest amounts of liquid water. Many modifications to the retrieval approach were tested subsequently in an attempt to reduce the standard deviations relative to the radiometer retrievals below 0.12 mm but none of them worked reliably. We decided to wait until the DOE ARM Program deployed its second generation SACR2s to its NSA and ENA sites for further retrieval evaluation. The SACR2s were engineered to better synchronize data between the Ka- and W-band radars, reducing potential sources of noise for radar retrieval of liquid water distributions within clouds. The SACR2s have improved in reliability recently and the system deployed to ENA is best positioned to provide the data to advance research on this path. This will be an ongoing research activity beyond the project because of the promise in SACR2 data from the DOE ARM ENA Facility.

58 GEOSCIENCES↗
NASA NTRS1984

Quantitative use of multiincidence-angle SAR for geologic mapping

It is proposed that techniques be developed and used for quantitative interpretation of shuttle imaging radar-B (SIR-B) data for lithologic identification and mapping. The use of backscatter versus incidence angle signatures derived from SIR-B images is to be investigated. The use of SIR-B with other sensors for geologic mapping is also to be considered. Anticipated results are discussed in terms of geologic mapping.

Farr, T. G.↗
NASA NTRS1984

Structural investigation of the Grenville Province by radar and other imaging and nonimaging sensors

The structural investigation of the Canadian Shield by orbital radar and LANDSAT, is outlined. The area includes parts of the central metasedimentary belt and the Ontario gneiss belt, and major structures as well-expressed topographically. The primary objective is to apply SIR-B data to the mapping of this key part of the Grenville orogen, specifically ductile fold structures and associated features, and igneous, metamorphic, and sedimentary rock (including glacial and recent sediments). Secondary objectives are to support the Canadian RADARSAT project by evaluating the baseline parameters of a Canadian imaging radar satellite planned for late in the decade. The baseline parameters include optimum incidence and azimuth angles. The experiment is to develop techniques for the use of multiple data sets.

Lowman, P. D., Jr.↗
NASA NTRS1984

Space Shuttle development update

The development efforts, since the STS-4 flight, in the Space Shuttle (SS) program are presented. The SS improvements introduced in the last two years include lower-weight loads, communication through the Tracking and Data Relay Satellite, expanded extravehicular activity capability, a maneuvering backpack and the manipulator foot restraint, the improvements in thermal projection system, the 'optional terminal area management targeting' guidance software, a rendezvous system with radar and star tracker sensors, and improved on-orbit living conditions. The flight demonstrations include advanced launch techniques (e.g., night launch and direct insertion to orbit); the on-orbit demonstrations; and added entry and launching capabilities. The entry aerodynamic analysis and entry flight control fine tuning are described. Reusability, improved ascent performance, intact abort and landing flexibility, rollout control, and 'smart speedbrakes' are among the many improvements planned for the future.

Brand, V.↗
NASA NTRS1991

Remote sensing of earth terrain

In remote sensing, the encountered geophysical media such as agricultural canopy, forest, snow, or ice are inhomogeneous and contain scatters in a random manner. Furthermore, weather conditions such as fog, mist, or snow cover can intervene the electromagnetic observation of the remotely sensed media. In the modelling of such media accounting for the weather effects, a multi-layer random medium model has been developed. The scattering effects of the random media are described by three-dimensional correlation functions with variances and correlation lengths corresponding to the fluctuation strengths and the physical geometry of the inhomogeneities, respectively. With proper consideration of the dyadic Green's function and its singularities, the strong fluctuation theory is used to calculate the effective permittivities which account for the modification of the wave speed and attenuation in the presence of the scatters. The distorted Born approximation is then applied to obtain the correlations of the scattered fields. From the correlation of the scattered field, calculated is the complete set of scattering coefficients for polarimetric radar observation or brightness temperature in passive radiometer applications. In the remote sensing of terrestrial ecosystems, the development of microwave remote sensing technology and the potential of SAR to measure vegetation structure and biomass have increased effort to conduct experimental and theoretical researches on the interactions between microwave and vegetation canopies. The overall objective is to develop inversion algorithms to retrieve biophysical parameters from radar data. In this perspective, theoretical models and experimental data are methodically interconnected in the following manner: Due to the complexity of the interactions involved, all theoretical models have limited domains of validity; the proposed solution is to use theoretical models, which is validated by experiments, to establish the region in which the radar response is most sensitive to the parameters of interest; theoretically simulated data will be used to generate simple invertible models over the region. For applications to the remote sensing of sea ice, the developed theoretical models need to be tested with experimental measurements. With measured ground truth such as ice thickness, temperature, salinity, and structure, input parameters to the theoretical models can be obtained to calculate the polarimetric scattering coefficients for radars or brightness temperature for radiometers and then compare theoretical results with experimental data. Validated models will play an important role in the interpretation and classification of ice in monitoring global ice cover from space borne remote sensors in the future. We present an inversion algorithm based on a recently developed inversion method referred to as the Renormalized Source-Type Integral Equation approach. The objective of this method is to overcome some of the limitations and difficulties of the iterative Born technique. It recasts the inversion, which is nonlinear in nature, in terms of the solution of a set of linear equations; however, the final inversion equation is still nonlinear. The derived inversion equation is an exact equation which sums up the iterative Neuman (or Born) series in a closed form and, thus, is a valid representation even in the case when the Born series diverges; hence, the name Renormalized Source-Type Integral Equation Approach.

Yueh, Herng-Aung↗
NASA NTRS1990

Calibration experiments with DC-8 SAR data

This paper is a summary of some research activities in calibrating DC-8 SAR data conducted in 1989/90 by the author and others. Firstly, by examining the process of 'symmetrization' in the production of DC-8 Stokes' matrix data, it is shown that an exact solution to the problem of calibrating this data exists, given certain assumptions. Secondly a new, more general, system model for radar polarimeters is proposed, together with a simple approach for verifying the model's validity. The importance of the orientation angle about the radar line of sight for some types of calibration devices is discussed and some results of an experiment to cross-calibrate the C-Band VV data from the DC-8 and DLR E-SAR sensors are presented.

Freeman, Anthony↗
NASA NTRS1995

Interaction of the terrestrial and atmospheric hydrological cycles in the context of the North American southwest summer monsoon

Work under this grant has used information on precipitation and water vapor fluxes in the area of the Mexican Monsoon to analyze the regional precipitation climatology, to understand the nature of water vapor transport during the monsoon using model and observational data, and to analyze the ability of the TRMM remote sensing algorithm to characterize precipitation. An algorithm for estimating daily surface rain volumes from hourly GOES infrared images was developed and compared to radar data. Estimates were usually within a factor of two, but different linear relations between satellite reflectances and rainfall rate were obtained for each day, storm type and storm development stage. This result suggests that using TRMM sensors to calibrate other satellite IR will need to be a complex process taking into account all three of the above factors. Another study, this one of the space-time variability of the Mexican Monsoon, indicate that TRMM will have a difficult time, over the course of its expected three year lifetime, identifying the diurnal cycle of precipitation over monsoon region. Even when considering monthly rainfalls, projected satellite estimates of August rainfall show a root mean square error of 38 percent. A related examination of spatial variability of mean monthly rainfall using a novel method for removing the effects of elevation from gridded gauge data, show wide variation from a satellite-based rainfall estimates for the same time and space resolution. One issue addressed by our research, relating to the basic character of the monsoon circulation, is the determination of the source region for moisture. The monthly maps produced from our study of monsoon variability show the presence of two rainfall maxima in the analysis normalized to sea level, one in south-central Arizona associated with the Mexican monsoon maximum and one in southeastern New Mexico associated with the Gulf of Mexico. From the point of view of vertically-integrated fluxes and flux divergence of water vapor from ECMWF data, most moisture at upper levels arrives from the Gulf of Mexico, while low level moisture comes from the northern Gulf of California. Composites of ECMWF analyses for wet and dry periods (classified by rain gauge data) show that both regimes show low level moisture arriving from northern and central Gulf of California. Above 700 MB, moisture comes from both source regions and the Sierra Madre Occidental. During wet periods a longer fetch through the moist air mass above western Mexico results in a greater moisture flux into the Sonoran Desert region, while there is less moisture from the Gulf of Mexico both above and below 700 mb. Work on the grant subcontract at the University of Colorado concentrated on the development of a technique useful to TRMM combining visible, infrared and passive microwave data for measuring precipitation. Two established techniques using either visible or infrared data applied over the US Southwest correlated with gauges at the 0.58 to 0.70 level. The application of some established passive microwave techniques were less successful for a variety of reason, including problems in both the gauge and satellite data quality, sampling problems and weaknesses inherent in the algorithms themselves. A more promising solution for accurate rainfall estimation was explored using visible and infrared data to perform a cloud classification, which when combined with information about the background (e.g. Iand/ocean), was used to select the most appropriate microwave algorithm from a suite of possibilities.

Dickinson, Robert E.↗
DOE OSTI2019

Utah FORGE: InSAR Data 2019

This dataset contains Interferometric Synthetic Aperture Radar (InSAR) data used for ground deformation monitoring during Phase 2C of the Utah FORGE project. The dataset includes measurements of the mean rate of range change and associated standard errors, provided in both CSV and NetCDF formats. Supporting materials include histograms, maps, and MATLAB scripts used for data processing, as well as unit vector information describing the satellite's sensor orientation. Compressed archives contain additional metadata and masked range change data for individual interferometric pairs. A README file is included to provide further details on the dataset's structure and contents.

15 GEOTHERMAL ENERGY↗
NASA NTRSDate not supplied

Mapping Inundation from Hurricane Florence (2018) with L-Band Synthetic Aperture Radar, Commercial Imagery, and Ancillary Data via Random Forest Classification

Mapping the extent of floodwaters following extreme rainfall aids in the distribution of resources, recovery efforts, and damage assessment practices. Development of a land cover classification system focused on mapping inundation after major hurricane events using synthetic aperture radar (SAR) data could allow for the production of near-real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of a developing situation. In response to Hurricane Florence of 2018, NASA JPL collected numerous swaths of quad-pol L-band SAR data with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument observing the record-setting river stages across North and South Carolina. The resulting fully-polarized SAR images allow for mapping of inundation extent at a high spatial resolution with a unique advantage over optical imaging stemming from the sensor’s ability to penetrate cloud cover and dense vegetation. This study seeks to determine how accurately maps of inundation can be generated from L-band SAR imagery through Random Forest classification. Once the extent of water and inundated vegetation is classified, cleanup operations are performed using fuzzy logic to reduce false detections. Estimates of water extent are then combined with datasets describing the distribution of population, buildings, and roads throughout the domain to evaluate societal impacts. Results from the Hurricane Florence case study will be discussed along with the limitations of available validation data for assessment of the classifier’s accuracy.

Alexander Melancon↗
NASA NTRS2017

Real-Time Radar-Based Tracking and State Estimation of Multiple Non-Conformant Aircraft

In this study, a novel solution for automated tracking of multiple unknown aircraft is proposed. Many current methods use transponders to self-report state information and augment track identification. While conformant aircraft typically report transponder information to alert surrounding aircraft of its state, vehicles may exist in the airspace that are non-compliant and need to be accurately tracked using alternative methods. In this study, a multi-agent tracking solution is presented that solely utilizes primary surveillance radar data to estimate aircraft state information. Main research challenges include state estimation, track management, data association, and establishing persistent track validity. In an effort to realize these challenges, techniques such as Maximum a Posteriori estimation, Kalman filtering, degree of membership data association, and Nearest Neighbor Spanning Tree clustering are implemented for this application.

RADAR tracking↗
NASA NTRS1974

MAS2-8 radar and digital control unit

The design of the MAS 2-8 (2 to 8 GHz microwave-active spectrometer), a ground-based sensor system, is presented. A major modification in 1974 to the MAS 2-8, that of a control subsystem to automate the data-taking operation, is the prime focus. The digital control unit automatically changes all system parameters except FM rate and records the return signal on paper tape. The overall system operation and a detailed discussion of the design and operation of the digital control unit are presented.

Oberg, J. M.↗
DOE OSTI2026

BNF C-band Scanning ARM Precipitation Radar 2nd Generation (CSAPR-2) Extracted Radar Columns and In-Situ Sensors (RadCLss)

Corrected Moments in Antenna Coordinates (CMAC) calculates quantitative precipitation estimates (QPE) from empirical relationships based on equivalent radar reflectivity factor, specific differ- ential phase, and specific attenuation. To evaluate these empirical relationships, the Extracted Radar Columns and In-Situ Sensors (RADclss) product was developed. Utilizing Py-ART, RAD- clss extracts CMAC radar columns above various ARM and partner locations. These columns are then spatiotemporally synced with in-situ observations at the surface utilizing the Atmospheric data Community Toolkit (ACT; Theisen et al. 2025), allowing direct comparison of radar parameters with rain gauges and laser disdrometers for further investigation.

bankhead↗
NASA NTRS1978

Modulation of centimetric waves by long gravity waves - Progress report on field and laboratory results

Results are presented from laboratory and field experiments on the modulation of short waves by long waves. The field study employed a wave follower capable of tracking ocean waves with frequencies less than 1.0 Hz and heights less than 2.0 m. A high-response laser-optical system was used to detect upwind-downwind and cross-wind slopes of short waves. The laboratory study was conducted with wind over periodic long waves. The laboratory findings are discussed and compared with laboratory radar measurements and also short wave measurements obtained in the field. It is found that long waves significantly modulate the short wave dispersion by their orbital velocity, and that demodulation is necessary if the data collected by remote sensors are to be compared to surface penetrating devices. The modulation level is weak for wavelengths in the range 2.76-3.30 cm. Other relevant results are also presented.

Shemdin, O. H.↗
NASA NTRS1991

LADAR vision technology for automated rendezvous and capture

LADAR Vision Technology at Autonomous Technologies Corporation consists of two sensor/processing technology elements: high performance long range multifunction coherent Doppler laser radar (LADAR) technology; and short range integrated CCD camera with direct detection laser ranging sensors. Algorithms and specific signal processing implementations have been simulated for both sensor/processing approaches to position and attitude tracking applicable to AR&C. Experimental data supporting certain sensor measurement accuracies have been generated.

Frey, Randy W.↗
NASA NTRS2000

Characterization of Global Near-Nadir Backscatter for Remote Sensing Radar Design

In order to evaluate side-lobe contamination from the near-nadir region for Ku-Band radars, a statistical characterization of global near-nadir backscatter is constructed. This characterization is performed for a variety of surface types using data from TRMM, Seasat, and Topex. An assessment of the relative calibration accuracy of these sensors is also presented.

Spencer, Michael W.↗