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DOE OSTI2021

Evaluation and Applications of Multi-Instrument Boundary-Layer Thermodynamic Retrievals

Recent reports have highlighted the need for improved observations of the atmosphere boundary layer. In this study, we explore the combination of ground-based active and passive remote sensors deployed for thermodynamic profiling to analyze various boundary-layer observation strategies. Optimal-estimation retrievals of thermodynamic profiles from Atmospheric Emitted Radiance Interferometer (AERI) observed spectral radiance are compared with and without the addition of active sensor observations from a May–June 2017 observation period at the Atmospheric Radiation Measurement Southern Great Plains site. In all, three separate thermodynamic retrievals are considered here: retrievals including AERI data only, retrievals including AERI data and Vaisala water vapour differential-absorption lidar data, and retrievals including AERI data and Raman lidar data. First, the three retrievals are compared to each other and to reference radiosonde data over the full observation period to obtain a bulk understanding of their differences and characterize the impact of clouds on these retrieved profiles. These analyses show that the most significant differences are in the water vapour field, where the active sensors are better able to represent the moisture gradient in the entrainment zone near the boundary-layer top. Furthermore, we also explore how differences in retrievals may affect results of applied analyses including land–atmosphere coupling, convection indices, and severe storm environmental characterization. Overall, adding active sensors to the optimal-estimation retrieval shows some added information, particularly in the moisture field. Given the costs of such platforms, the value of that added information must be weighed for the application at hand.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

MetaHeart: Metasurface enabled biometrics camouflage

Privacy-invading biometrics monitoring is becoming a prominent security threat as modern sensing systems move to higher operating frequencies (mmWave, sub-THz), increasing sensing resolution and accuracy. As such, developing systems that can protect or obfuscate biometrics from adversarial intrusion becomes pivotal to preserving user privacy. In this work, we develop and implement MetaHeart, a real-time biometrics misinformation system based on reflective, programmable metasurfaces and dynamic phase-front manipulation of radar inferences. MetaHeart’s key goal is to prevent the leakage of a legitimate user’s heartbeat biometrics by spoofing fake heartbeat signals at a malicious, radar-equipped, heart rate sensing intruder. Furthermore, we experimentally demonstrate MetaHeart’s ability to fake Alice’s presence when she is not there and to fool Trudy’s inferences even when Alice is present, achieving an overall accuracy above 98%. Finally, we conduct a robustness analysis to determine MetaHeart’s required spatial placement within the intruder’s monitoring area that would allow for effective spoofing.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

NASA NTRS1979

Remote sensing of seasonal distribution of precipitable water vapor over the oceans and inference of boundary layer structure

Over the ocean satellite infrared spectral measurements in the 18 micrometer water vapor band and the 11 micrometer window region were used to derive precipitable water vapor, w, in the atmosphere and the sea surface temperature, SST. Seasonal maps of w on the oceans derived from these data reveal the dynamical influence of the large scale atmospheric circulation. With the help of a model for the vertical distribution of water vapor, the configuration of the atmospheric boundary layer over the oceans can be inferred from w when the information of SST is combined. The gross seasonal mean structure of the boundary layer inferred in this fashion reveals the broad areas of the trade wind inversion and the convectively active areas such as the intertropical convergence zones.

Prabhakara, C.↗

NASA NTRS1987

Microwave dielectric behavior of vegetation material

The microwave dielectric behavior of vegetation was examined through the development of theoretical models involving dielectric dispersion by both bound and free water and supported by extensive dielectric measurements conducted over a wide range of conditions. The experimental data were acquired using an open-ended coaxial probe that was developed for sensing the dielectric constant of thin layers of materials, such as leaves, from measurements of the complex reflection coefficient using a network analyzer. The probe system was successfully used to record the spectral variation of the dielectric constant over a wide frequency range extending from 0.5 to 20.4 GHz at numerous temperatures between -40 to +40 C. The vegetation samples were measured over a wide range of moisture conditions. To model the dielectric spectrum of the bound water component of the water included in vegetation, dielectric measurements were made for several sucrose-water solutions as analogs for the situation in vegetation. The results were used in conjunction with the experimental data for leaves to determine some of the constant coefficients in the theoretical models. Two models, both of which provide good fit to the data, are proposed.

Elrayes, Mohamed A.↗

NASA NTRS1992

Stratospheric Backscatter, Extinction, and Lidar Ratio Profiling After the Mt. Pinatubo Eruptions

Since August 1991 the stratospheric aerosol layer is continuously monitored with a combination Raman elastic-backscatter lidar, groundbased systems. XeCl excimer laser is used as the radiation source. Signals elastically backscattered from air molecules and particles and inelastically Raman backscattered from nitrogen molecules are detected. The measurements of these two signal profiles up to 35 km height allows the separate and independent determination of aerosol extinction and backscatter properties in the perturbed lower stratosphere. The extinction coefficient is derived from the nitrogen Raman signal profile, while the backscatter coefficient is obtained from the elastic-to-inelastic backscatter signal ratio. In this way, also the extinction-to-backscatter, or lidar, ratio profile is determined. For the first time, lidar ratios are determined in the perturbed stratospheric layer by the use of remote sensing only. The lidar ratio contains information about the size range of the stratospheric particles which are assumed to be sulfuric acid droplets, and it is needed in the calculation of aerosol optical properties from data taken with a typical backscatter lidar.

Ansmann, Albert↗

NASA NTRS2001

The Vertical Error Characteristics of GOES-derived Winds: Description and Impact on Numerical Weather Prediction

Errors in the height assignment of some satellite-derived winds exist because the satellites sense radiation emitted from a finite layer of the atmosphere rather than a specific level. Potential problems in data assimilation may arise because the motion of a measured layer is often represented by a single-level value. In this research, cloud and water vapor motion winds that are derived from the Geostationary Operational Environmental Satellites (GOES winds) are compared to collocated rawinsonde observations (RAOBs). An important aspect of this work is that in addition to comparisons at each assigned height, the GOES winds are compared to the entire profile of the collocated RAOB data to determine the vertical error characteristics of the GOES winds. The impact of these results on numerical weather prediction is then investigated. The comparisons at individual vector height assignments indicate that the error of the GOES winds range from approx. 3 to 10 m/s and generally increase with height. However, if taken as a percentage of the total wind speed, accuracy is better at upper levels. As expected, comparisons with the entire profile of the collocated RAOBs indicate that clear-air water vapor winds represent deeper layers than do either infrared or water vapor cloud-tracked winds. This is because in cloud-free regions the signal from water vapor features may result from emittance over a thicker layer. To further investigate characteristics of the clear-air water vapor winds, they are stratified into two categories that are dependent on the depth of the layer represented by the vector. It is found that if the vertical gradient of moisture is smooth and uniform from near the height assignment upwards, the clear-air water vapor wind tends to represent a relatively deep layer. The information from the comparisons is then used in numerical model simulations of two separate events to determine the forecast impacts. Four simulations are performed for each case: 1) A control simulation that assimilates no satellite wind data, 2) assimilation of all GOES winds according to their assigned single level height, 3) assimilation of all GOES winds spread over multiple levels, and 4) assimilation of all GOES winds spread over multiple levels, but with variations in the vertical influence of clear-air water vapor winds based on the moisture profile in the model. In the first case, a strong mid-latitude cyclone is present and the use of the satellite data results in improved storm tracks during the initial approx. 36 h forecast period. This is because the satellite data improves the analysis of the environment into which the storm progresses. Statistics for mean wind vector and height differences show that, with the exception of the height field at later times in the first case, the use of GOES winds improves the simulation with time. The simulation results suggest that it is beneficial to spread the GOES wind information over multiple levels, particularly when the moisture profile is used to define the vertical influence.

Rao, P. Anil↗

NASA NTRS2001

Cloud Overlapping Detection Algorithm Using Solar and IR Wavelengths With GOSE Data Over ARM/SGP Site

One of the most perplexing problems in satellite cloud remote sensing is the overlapping of cloud layers. Although most techniques assume a 1-layer cloud system in a given retrieval of cloud properties, many observations are affected by radiation from more than one cloud layer. As such, cloud overlap can cause errors in the retrieval of many properties including cloud height, optical depth, phase, and particle size. A variety of methods have been developed to identify overlapped clouds in a given satellite imager pixel. Baum el al. (1995) used CO2 slicing and a spatial coherence method to demonstrate a possible analysis method for nighttime detection of multilayered clouds. Jin and Rossow (1997) also used a multispectral CO2 slicing technique for a global analysis of overlapped cloud amount. Lin et al. (1999) used a combination infrared, visible, and microwave data to detect overlapped clouds over water. Recently, Baum and Spinhirne (2000) proposed 1.6 and 11 microns. bispectral threshold method. While all of these methods have made progress in solving this stubborn problem, none have yet proven satisfactory for continuous and consistent monitoring of multilayer cloud systems. It is clear that detection of overlapping clouds from passive instruments such as satellite radiometers is in an immature stage of development and requires additional research. Overlapped cloud systems also affect the retrievals of cloud properties over the ARM domains (e.g., Minnis et al 1998) and hence should identified as accurately as possible. To reach this goal, it is necessary to determine which information can be exploited for detecting multilayered clouds from operational meteorological satellite data used by ARM. This paper examines the potential information available in spectral data available on the Geostationary Operational Environmental Satellite (GOES) imager and the NOAA Advanced Very High Resolution Radiometer (AVHRR) used over the ARM SGP and NSA sites to study the capability of detecting overlapping clouds

Kawamoto, Kazuaki↗

NASA NTRS2001

Cloud Overlapping Detection Algorithm Using Solar and IR Wavelengths with GOES Data Over ARM/SGP Site

One of the most perplexing problems in satellite cloud remote sensing is the overlapping of cloud layers. Although most techniques assume a one layer cloud system in a given retrieval of cloud properties, many observations are affected by radiation from more than one cloud layer. As such, cloud overlap can cause errors in the retrieval of many properties including cloud height, optical depth, phase, and particle size. A variety of methods have been developed to identify overlapped clouds in a given satellite imager pixel. Baum et al used CO2 slicing and a spatial coherence method to demonstrate a possible analysis method for nighttime detection of multilayered clouds. Jin and Rossow also used a multispectral CO2 slicing technique for a global analysis of overlapped cloud amount. Lin et al. used a combination infrared (IR), visible (VIS), and microwave data to detect overlapped clouds over water. Recently, Baum and Spinhirne proposed a 1.6 and 11 micron bispectral threshold method. While all of these methods have made progress in solving this stubborn problem none have yet proven satisfactory for continuous and consistent monitoring of multilayer cloud systems. It is clear that detection of overlapping clouds from passive instruments such as satellite radiometers is in an immature stage of development and requires additional research. Overlapped cloud systems also affect the retrievals of cloud properties over the Atmospheric Radiation Measurement (ARM) domains and hence should be identified as accurately as possible. To reach this goal, it is necessary to determine which information can be exploited for detecting multilayered clouds from operational meteorological satellite data used by ARM. This paper examines the potential information available in spectral data available on the Geostationary Operational Environmental Satellite (GOES) imager and the National Oceanic Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) used over the ARM Program's Southern Great Plains (SGP), and North Slope of Alaska (NSA) sites to study the capability of detecting overlapping clouds.

Kawamoto, K.↗

NASA NTRS2001

A Model Rotor in Axial Flight

A model rotor was mounted horizontally in the settling chamber of a wind tunnel to obtain performance and wake structure data under low climb conditions. The immediate wake of the rotor was carefully surveyed using 3-component particle image velocimetry to define the velocity and vortical content of the flow, and used in a subsequent study to validate a theory for the separate determination of induced and profile drag. Measurements were obtained for two collective pitch angles intended to render a predominately induced drag state and another with a marked increase in profile drag. A majority of the azimuthally directed vorticity in the wake was found to be concentrated in the tip vortices. However, adjacent layers of inboard vorticity with opposite sense were clearly present. At low collective, the close proximity of the tip vortex from the previous blade caused the wake from the most recent blade passage to be distorted. The deficit velocity component that was directed along the azimuth of the rotor blade was never more that 15 percent of the rotor tip speed, and except for the region of the tip vortex, appeared to have totally disappeared form the wake left by the previous blade.

McAlister, K. W.↗

NASA NTRS2021

Evaluation and Applications of Multi-Instrument Boundary-Layer Thermodynamic Retrievals

Recent reports have highlighted the need for improved observations of the boundary layer. In this study, we explore the combination of ground-based active and passive remote sensors deployed for thermodynamic profiling to analyze various boundary-layer observation strategies. Optimal-estimation retrievals of thermodynamic profiles from Atmospheric Emitted Radiance Interferometer (AERI) observed spectral radiance are compared with and without the addition of active sensor observations from a May–June 2017 observation period at the Atmospheric Radiation Measurement–Southern Great Plains Site. In all, three separate thermodynamic retrievals are considered here: retrievals including AERI data only, retrievals including AERI data and Vaisala water vapour differential absorption lidar data, and retrievals including AERI data and Raman lidar data. First, the three retrievals are compared to each other and to reference radiosonde data over the full observation period to get a bulk understanding of their differences and characterize the impact of clouds on these retrieved profiles. These analyses show that the most significant differences are in the water vapour field, where the active sensors are better able to represent the moisture gradient in the entrainment zone near boundary layer top. We also explore how differences in retrievals may impact results of applied analyses including land–atmosphere coupling, convection indices, and severe storm environmental characterization. Overall, adding active sensors to the optimal-estimation retrieval showed some added information, particularly in the moisture field. Given the costs of such platforms, the value of that added information must be weighed for the application at hand.

boundary-layer observation↗

NASA NTRS2022

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. One concept of the future of lunar communication, LunaNet, outlines a complex network of networks. Challenges such as scalability, interoperability, and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. Machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study four main areas as a means to further develop cognitive networking capabilities: networking protocol development, analysis of wireless data for modeling and simulation, development of algorithms for a multi-agent system, and spectrum sensing technology.

cognitive networking↗

DOE OSTI2025

REFRACTORY COMPACT HEAT EXCHANGERS WITH EMBEDDED SENSORS ENABLED BY HYBRID ADVANCED SINTERING AND ADDITIVE APPROACH

Structural health monitoring (SHM) of compact heat exchangers (CHXs) operating in extreme environments is essential for ensuring system reliability, safety, and longevity. This study presents the development of high-temperature sensors fabricated via aerosol jet printing (AJP) using platinum ink, selected for its exceptional thermal stability, oxidation resistance, and electrical conductivity. AJP enables precise deposition of fine-feature sensor patterns onto complex geometries, making it well-suited for integration within CHX architectures. To enhance sensor durability, an alumina-based ceramic protective layer was printed over the platinum sensing elements. The sensors demonstrated stable, repeatable performance up to 900?°C during extended thermal cycling. A custom test setup was developed to evaluate sensor accuracy and robustness under steady-state and transient conditions. Substrate screening identified HG-1 ceramic-coated stainless steel as the most effective platform, offering strong adhesion and low resistance. Furthermore, electric field-assisted sintering (EFAS) was employed to embed the sensors into stainless steel 316L matrices without degrading their functionality. Post-embedding electrical tests confirmed sensor integrity, and initial characterization suggests strong potential for in-situ monitoring. This work provides a scalable strategy for integrating high-performance temperature sensors directly into refractory components, advancing embedded SHM technologies for harsh operating environments.

36 - MATERIALS SCIENCE↗

NASA NTRS1990

Remote sensing of Earth terrain

Remote sensing of earth terrain is examined. The layered random medium model is used to investigate the fully polarimetric scattering of electromagnetic waves from vegetation. The model is used to interpret the measured data for vegetation fields such as rice, wheat, or soybean over water or soil. Accurate calibration of polarimetric radar systems is essential for the polarimetric remote sensing of earth terrain. A polarimetric calibration algorithm using three arbitrary in-scene reflectors is developed. In the interpretation of active and passive microwave remote sensing data from the earth terrain, the random medium model was shown to be quite successful. A multivariate K-distribution is proposed to model the statistics of fully polarimetric radar returns from earth terrain. In the terrain cover classification using the synthetic aperture radar (SAR) images, the applications of the K-distribution model will provide better performance than the conventional Gaussian classifiers. The layered random medium model is used to study the polarimetric response of sea ice. Supervised and unsupervised classification procedures are also developed and applied to synthetic aperture radar polarimetric images in order to identify their various earth terrain components for more than two classes. These classification procedures were applied to San Francisco Bay and Traverse City SAR images.

Kong, Jin AU↗

DOE OSTI2022

Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Network.

Neural operators have recently become popular tools for designing solution maps between function spaces in the form of neural networks. Differently from classical scientific machine learning approaches that learn parameters of a known partial differential equation (PDE) for a single instance of the input parameters at a fixed resolution, neural operators approximate the solution map of a family of PDEs [6, 7]. Despite their success, the uses of neural operators are so far restricted to relatively shallow neural networks and confined to learning hidden governing laws. In this work, we propose a novel nonlocal neural operator, which we refer to as nonlocal kernel network (NKN), that is resolution independent, characterized by deep neural networks, and capable of handling a variety of tasks such as learning governing equations and classifying images. Our NKN stems from the interpretation of the neural network as a discrete nonlocal diffusion reaction equation that, in the limit of infinite layers, is equivalent to a parabolic nonlocal equation, whose stability is analyzed via nonlocal vector calculus. The resemblance with integral forms of neural operators allows NKNs to capture long-range dependencies in the feature space, while the continuous treatment of node-to-node interactions makes NKNs resolution independent. The resemblance with neural ODEs, reinterpreted in a nonlocal sense, and the stable network dynamics between layers allow for generalization of NKN’s optimal parameters from shallow to deep networks. This fact enables the use of shallow-to-deep initialization techniques [8]. Our tests show that NKNs outperform baseline methods in both learning governing equations and image classification tasks and generalize well to different resolutions and depths.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2011

SansEC Sensing Technology - A New Tool for Designing Space Systems and Components

This paper presents concepts for using the NASA developed SansEC sensing technology for reconfiguring/modifying many space subsystems to add to their original function the ability to be sensors/sensor arrays without the addition of the electrical circuitry typically used for sensors. Each sensor is a self-resonating planar pattern of electrically conductive material that is an open-circuit single component without electrical connections. The sensors are wirelessly powered using external oscillating magnetic fields and when electrically excited respond with their own magnetic fields whose frequency, amplitude and bandwidth can be correlated with the magnitude of multiple unrelated physical quantities. These sensors have been demonstrated for numerous measurements required for spacecraft and inflatable/expandable structures. SansEC sensors are damage resilient and simple to fabricate. Thin films of conductive material can be used to create sensor arrays that function as sensing skins. Each sensor on the skin can be tailored for a science or engineering measurement. Additionally, each sensor has an inherent damage detection capability. These sensing skins can be used to redesign inflatable habitat multi-layer insulation to provide additional functions of environmental measurement and micrometeorite/orbital debris damage detections. The sensing skins can be deposited on planetary exploratory vehicles to increase the number of measurements with negligible weight increase.

Woodard, Stanley E.↗

NASA NTRS2000

BOREAS AFM-03-NCAR Electra 1994 Aircraft Sounding Data

The Boreal Ecosystem-Atmosphere Study (BOREAS) Airborne Fluxes and Meteorology (AFM)-3 team used the National Center for Atmospheric Research's (NCAR) Electra aircraft to make sounding measurements to study the planetary boundary layer using in situ and remote-sensing measurements. Measurements were made of wind speed and direction, air pressure and temperature, potential temperature, dewpoint, mixing ratio of H, O, CO, concentration, and ozone concentration. Twenty-five research missions were flown over the Northern Study Area (NSA), Southern Study Area (SSA), and the transect during BOREAS Intensive Field Campaigns (IFCs) 1, 2, and 3 during 1994. All missions had from four to ten soundings through the top of the planetary boundary layer. This sounding data set contains all of the in situ vertical profiles through the boundary layer top that were made (with the exception of 'porpoise' maneuvers). Data were recorded in one-second time intervals. These data are stored in tabular ASCII files. The NCAR Electra 1994 aircraft sounding data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Lenschow, Donald H.↗

NASA NTRSDate not supplied

Overview of ACTIVATE’s First Year of Flights Targeting Aerosol-Cloud-Meteorology Interactions

The Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) included 40 research flights in the winter and summer months of 2020 using a two-aircraft approach to simultaneously obtain in situ and remote sensing data of regions below, inside, and above boundary layer clouds. This presentation will summarize early findings from the first year of ACTIVATE flights with focus on aerosol and cloud droplet number concentration behavior, formation and evolution of clouds, and analysis of remote sensing retrievals of aerosol and cloud properties. There is a clear annual cycle in cloud droplet number concentration suggesting a marked annual cycle in aerosol-cloud interactions. Although aerosol particles are more abundant in spring and summer, the conditions needed to activate those particles into cloud droplets are weaker than in other colder months. Several flights targeted post-frontal clouds associated with cold air outbreaks pointing to enhanced levels of nucleated particles immediately above such clouds, and that ice formation hastens the transition from closed to open cloud structures in the sampled CAO events. Also observed in the marine boundary layer during CAOs is the frequent occurrence of non-spherical sea salt particles, which has significant implications for remote sensing retrievals.

Taylor Shingler↗

NASA NTRS1995

Is chemical heating a major cause of the mesosphere inversion layer?

A region of thermal enhancement of the mesosphere has been detected on numerous occasions by in situ measurements, remote sensing from space, and lidar techniques. The source of these 'temperature inversion layers' has been attributed in the literature to the dissipation relating to dynamical forcing by gravity wave or tidal activity. However, evidence that gravity wave breaking can produce the inversion layer with amplitude as large as that observed in lidar measurements has been limited to results of numerical modeling. An alternative source for the production of the thermal inversion layer in the mesosphere is the direct deposition of heat by exothermic chemical reactions. Two-dimensional modeling combining a comprehensive model of the mesosphere photochemistry with the dynamical transport of long-lived species shows that the region from 80 to 95 km may be heated as much as 3 to 10 K/d during the night and half this rate during the day. Given the uncertainties in our understanding of the dynamics and chemistry for the mesopause region, separating the two sources by passive observations of the mesosphere thermal structure looks to be difficult. Therefore we have considered an active means for producing a mesopause thermal layer, namely the release of ozone into the upper mesosphere from a rocket payload. The induced effects would include artificial enhancements of the OH and Na airglow intensities as well as the mesopause thermal structure. The advantages of the rocket release of ozone is that detection of these effects by ground-based imaging, radar, and lidar systems and comparison of these effects with model predictions would help quantify the partition of the artificial inversion layer production into sources of dynamical and chemical forcing.

Meriwether, John W.↗