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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

TPSAS-NF1676L-20108-DND

This presentation describes several enhancements planned for the version 4 aerosol subtyping and lidar ratio selection algorithms of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The CALIOP subtyping algorithm determines the most likely aerosol type from CALIOP measurements (attenuated backscatter, estimated particulate depolarization ratios de, layer altitude), and surface type. The aerosol type, so determined, is associated with a lidar ratio (LR) from a discrete set of values. In the version 3 algorithms, there are 6 pairs of 532 and 1064 nm lidar ratios. Some of these lidar ratios will be updated in the version 4 algorithms. In particular, the dust and polluted dust will be adjusted to reflect the latest measurements and model studies of these types. The algorithms are being updated to eliminate the occasional confusion between smoke and clean marine aerosols seen in version 3 by modifications to the elevated layer flag definitions that are used to determine the presence of smoke aerosols over the ocean. In the subtyping algorithms pure dust is determined by high estimated particulate depolarization ratios [de > 0.20]. Mixtures of dust and other aerosol types are determined by intermediate values of the estimated depolarization ratio [0.075< de <0.2]. The version 3 algorithms are limited to mixtures of dust and smoke, the so-called polluted dust aerosol type. To differentiate between mixtures of dust and smoke, and dust and marine aerosols, a new aerosol type will be added in the version 4 data products. In the revised classification algorithms, polluted dust will still defined as dust + smoke/pollution but in the marine boundary layer instances of moderate depolarization will be typed as dusty marine aerosols with a lower lidar ratio [LR = 35 sr] than polluted dust [currently LR = 55 sr]. The dusty marine type introduced in version 4 is modeled as a mixture of dust + marine aerosol. In the v3 algorithms the frequency of dust and polluted dust aerosols at daytime is higher than at nighttime. We present possible reasons for this and present the v4 distributions resulting from both improved background slope corrections of the daytime depolarization ratios and changes to the daytime thresholds for the polluted dust and dusty marine types of version 4. To gauge the impact of the enhancements, we contrast the following between versions 3 and 4: aerosol type, parameter distributions of each type, layer heights of maximum frequency, and distributions of smoke in biomass burning regions. To illustrate specific impacts the presentation shows case studies of version 3 and version 4 vertical feature masks of the aerosol subtypes, where appropriate, for the above enhancements.

A Omar↗
DOE OSTI2023

Design of Planarizing Growth Conditions on Unpolished and Faceted (100)-Oriented GaAs Substrates Using Hydride Vapor Phase Epitaxy

Here we performed a design of experiments (DoE) analysis to determine the effect of various growth parameters on in situ planarizing overgrowth of rough substrates using hydride vapor phase epitaxy (HVPE). We used two types of rough (100)-oriented GaAs substrates to compare the effect of the initial morphology on the epitaxial growth behavior: an irregular, as-cut surface resultant from cutting the wafer from a GaAs boule and a regularly faceted surface produced by controlled spalling. The DoE analysis identified trends in the overgrowth behavior with changing growth conditions, and we used these trends to design favorable planarization growth conditions for each surface type. These favorable conditions enabled full planarization of a spalled surface with >2.5 um facet height within 10.8 min of growth. The root mean squared (RMS) surface roughness of the resulting (100) surface was 24.5 nm over a 286 um x 215 um area. Our results show that planarization growth by HVPE is a promising technique to enable device growth directly on rough substrates, and the trends revealed through DoE analysis indicate a path toward further optimization of planarization growth conditions for as-cut and spalled surfaces.

14 SOLAR ENERGY↗
NASA NTRSDate not supplied

The Influence of Sea ice on Arctic Cloud Properties: What Can We Learn by Applying a In Situ Observational Strategy to Satellite Data?

Sea ice is declining because of anthropogenic climate change. This change alters many aspects of the Arctic climate system, including the way that the surface and atmosphere interact. Atmosphere-surface coupling processes represent an important cloud feedback mechanism that can alter the Arctic surface energy budget. For the Arctic, it has been hypothesized that a reduction in sea ice cover could lead to an increase in clouds. If this process were to occur as originally hypothesized, sea ice loss in all seasons would lead to an increase in clouds. Recent observational studies find a cloud response to sea ice loss in non-summer months and no cloud response in summer months. However, previous studies rely on inter-annual variability and reanalysis to control for the influence of meteorology, reducing the confidence in the resulting conclusions. We adopt a phenomenological, event-based approach that does not need to use meteorological reanalysis. The approach analyzes cloud properties derived from CALIPSO-CloudSat over sea ice and adjacent ice-free footprints by compositing individual satellite ground tracks that cross the Arctic sea ice edge. The underlying assumption, which we verify, is that footprints that are close to each other in space and time experience similar large-scale meteorological conditions. Our results show larger cloud fraction and more cloud liquid water over ice-free than over sea ice footprints and provide additional evidence for a seasonal dependence of cloud-sea ice coupling that is in line with previous work. We find a different result where the maximum cloud property differences between sea ice and ice-free ocean occurs in spring, not fall as earlier studies suggest. We argue that these cloud differences between sea ice and ice-free ocean are primarily caused by the influence of the surface type on the thermodynamic stability of the lower troposphere and not principally from an increase in surface evaporation. In addition, we explore the sensitivity of these results to marginal ice zone width, season, Atlantic vs. Pacific sector, and the cloud property dependence on the distance from the sea ice edge. Overall, our results provide further evidence that cloud-sea ice coupling processes are not offsetting the observed surface energy budget perturbation due to sea ice loss in summer; thus, the cloud response to declining sea ice appears to contribute to amplified Arctic warming.

Patrick C Taylor↗
NASA NTRS1997

Bidirectional Reflectance Functions for Application to Earth Radiation Budget Studies

Reflected solar radiative fluxes emerging for the Earth's top of the atmosphere are inferred from satellite broadband radiance measurements by applying bidirectional reflectance functions (BDRFs) to account for the anisotropy of the radiation field. BDRF's are dependent upon the viewing geometry (i.e. solar zenith angle, view zenith angle, and relative azimuth angle), the amount and type of cloud cover, the condition of the intervening atmosphere, and the reflectance characteristics of the underlying surface. A set of operational Earth Radiation Budget Experiment (ERBE) BDRFs is available which was developed from the Nimbus 7 ERB (Earth Radiation Budget) scanner data for a three-angle grid system, An improved set of bidirectional reflectance is required for mission planning and data analysis of future earth radiation budget instruments, such as the Clouds and Earth's Radiant Energy System (CERES), and for the enhancement of existing radiation budget data products. This study presents an analytic expression for BDRFs formulated by applying a fit to the ERBE operational model tabulations. A set of model coefficients applicable to any viewing condition is computed for an overcast and a clear sky scene over four geographical surface types: ocean, land, snow, and desert, and partly cloudy scenes over ocean and land. The models are smooth in terms of the directional angles and adhere to the principle of reciprocity, i.e., they are invariant with respect to the interchange of the incoming and outgoing directional angles. The analytic BDRFs and the radiance standard deviations are compared with the operational ERBE models and validated with ERBE data. The clear ocean model is validated with Dlhopolsky's clear ocean model. Dlhopolsky developed a BDRF of higher angular resolution for clear sky ocean from ERBE radiances. Additionally, the effectiveness of the models accounting for anisotropy for various viewing directions is tested with the ERBE along tract data. An area viewed from nadir and from the side give two different radiance measurements but should yield the same flux when converted by the BDRF. The analytic BDRFs are in very good qualitative agreement with the ERBE models. The overcast scenes exhibit constant retrieved albedo over viewing zenith angles for solar zenith angles less than 60 degrees. The clear ocean model does not produce constant retrieved albedo over viewing zenith angles but gives an improvement over the ERBE operational clear sky ocean BDRF.

Manalo-Smith, N.↗
DOE OSTI2024

Field-Effect Passivation by Desired Charge Injection into SiNx Passivation in Crystalline-Silicon Solar Cells

Effective surface passivation is a necessity for high efficiency crystalline silicon solar cells. The field effect passivation of p-type surfaces is typically accomplished using aluminum oxide (Al 2 O 3 ) which contains a high density of negative fixed charges. The accumulation layer formed by the negative charges prevents surface recombination loss of photo-generated carriers by repelling minority carriers (electrons) from the surface with interface state recombination centers. However, the Al 2 O 3 surface passivation technology has two key concerns of cost and safety associated with the precursor material tri-methyl-aluminum (TMA).

14 SOLAR ENERGY↗
DOE OSTI2021

Temperature-dependent dynamic fouling on superhydrophobic and slippery nonwetting copper surfaces

Bioinspired, superhydrophobic and slippery liquid infused surfaces that offer nonwetting characteristics have been explored in recent years for fouling mitigation. However, most of the studies are in the context of biofouling or under static immersion at ambient temperature conditions that are not reflective of the dynamic flow environment in practice. This article presents, for the first time, a systematic study of dynamic fouling of superhydrophobic (SHS) and slippery lubricant-infused surfaces (LIS) over a range of flow and temperature conditions. In this work, copper metallic surfaces were textured via electrodeposition or etching and further functionalized to achieve SHS and, additionally, infiltrated with a lubricant to fabricate LIS. The nonwetting surfaces were studied for their fouling behavior in a rotating Couette flow of a supersaturated calcium sulfate solution at different rotational speed and temperature. Fouling mineral mass accumulation on the different surfaces was measured as a function of time over a period of days using inductively coupled plasma mass spectroscopy and the fouled surfaces were investigated using scanning electron microscopy. Both SHS and LIS showed superior anti-scaling performance at all ranges of variables. An analytical Hill-Langmuir model is presented, for the first time, to describe the time evolution of scaling within 20% accuracy over the range of parameters studied. The study is the first to juxtapose two surface texturing methods, electrodeposition and etching, and two nonwetting surface types, SHS and LIS, subject to a common suite of experiments to elucidate fundamental understanding of mineral fouling on nonwetting surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Tuning Molecular Interactions between Peptoids and Substrates to Achieve Surface-Agnostic Coating

Achieving programmable and robust coatings that maintain functionality while adhering to various surface types with molecular-level tunability and programmable features remains challenging. In this study, we develop adaptable and stable surface-agnostic coatings (SACs) based on crystalline peptoid membranes by tuning interpeptoid and peptoid-substrate interactions. We utilize two complementary methods: (1) surfaceinduced assembly, where peptoid membranes form directly on substrates, and (2) depositing preformed peptoid crystalline membranes via an aqueous layer-by-layer (LbL) assembly technique. These strategies are applied to substrates with diverse surface chemistries and topographies, including mica, highly ordered pyrolytic graphite (HOPG), MoS 2 , sapphire, and porous membranes like porous alumina and polysulfide. Atomic force microscopy confirms the formation of peptoid coatings and reveals differences in assembly behavior across surfaces. Moisture vapor transport measurements serve as a proof-of-concept test for membrane continuity and tunable permeance. Together, these findings demonstrate the adaptability and programmability of peptoid-based SACs, enabling rational coating design on surfaces with diverse chemical and topographical features. Furthermore, this work opens pathways for using peptoid membranes as programmable surface modifiers in functional interfaces, protective coatings, and membrane platforms.

biomimetic polymers↗
NASA NTRS1988

Range data description based on multiple characteristics

An algorithm for describing range images based on Mean curvature (H) and Gaussian curvature (K) is presented. Range images are unique in that they directly approximate the physical surfaces of a real world 3-D scene. The curvature parameters are derived from the fundamental theorems of differential geometry and provides visible invariant pixel labels that can be used to characterize the scene. The sign of H and K can be used to classify each pixel into one of eight possible surface types. Due to the sensitivity of these parameters to noise the resulting HK-sing map does not directly identify surfaces in the range images and must be further processed. A region growing algorithm based on modeling the scene points with a Markov Random Field (MRF) of variable neighborhood size and edge models is suggested. This approach allows the integration of information from multiple characteristics in an efficient way. The performance of the proposed algorithm on a number of synthetic and real range images is discussed.

Al-Hujazi, Ezzet↗
NASA NTRS2011

Passive Microwave Remote Sensing of Falling Snow and Associated GPM Field Campaigns

Retrievals of falling snow from space represent one of the next important challenges for the atmospheric, hydrological, and energy budget scientific communities. Historically, retrievals of falling snow have been difficult due to the relative insensitivity of satellite rain-based channels as used in the past. We emphasize the use of high frequency passive microwave channels (85-200 GHz) since these are more sensitive to the ice in clouds and have been used to estimate falling snow from space. While satellite-based remote sensing provides global coverage of falling snow events and the science is relatively new, retrievals are still undergoing development with challenges remaining. There are several current satellite sensors, though not specifically designed for estimating falling snow, are capable of measuring snow from space. These include NOAA's AMSU-B, the MHS sensors, and CloudSat radar. They use high frequency (greater than 85 GHz) passive and active microwave and millimeter-wave channels that are sensitive to the scattering from ice and snow particles in the atmosphere. Sensors with water vapor channels near 183 GHz center line provide opaqueness to the Earth's surface features that can contaminate the falling snow signatures, especially over snow covered surface. In addition, the Global Precipitation Measurement (GPM) mission scheduled for launch in 2013 is specifically designed to measure both liquid rain and frozen snow precipitation. Since falling snow from space is the next precipitation measurement challenge from space, information must be determined in order to guide retrieval algorithm development for these current and future missions. This information includes thresholds of detection for various sensor channel configurations, snow event system characteristics, and surface types. For example, can a lake effect snow system with low cloud tops having an ice water content (IWC) at the surface of 1.0 gram per cubic meter be detected? If this information is known, we can focus retrieval efforts on detectable storms and concentrate advances on achievable results. In this work, the focus is to determine thresholds of detection for falling snow for various snow conditions over land and lake surfaces. The results rely on simulated Weather Research Forecasting (WRF) simulations of falling snow cases [9] since simulations provide all the information to determine the measurements from space and the ground truth. This analysis relies on data from the Canadian CloudSat/CALIPSO Validation Program (C3VP) field campaign held from October 31, 2006 through March 1, 2007. In January 2012 GPM will return to the C3VP area for the GPM Cold Precipitation Experiment (GCPEx). This presentation will describe the thresholds-of-detection procedure and results, as well as the field campaign details.

Skofronick-Jackson, Gail↗
NASA NTRS2011

Current Status of Multi-Angle Implementation of Atmospheric Correction (MAIAC) Algorithm

A new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has been developed for MODIS. MAIAC uses a time series and an image based rather than pixel-based processing to perform simultaneous retrievals of aerosol properties and surface bidirectional reflectance. It is a generic algorithm which works over all land surface types with the exception of snow. MAIAC has an internal Cloud Mask, a dynamic land-water-snow classification and a surface change mask which allows it to flexibly choose processing path over different surfaces. A distinct feature of MAIAC is a high 1 km resolution of aerosol retrievals including optical thickness and fine mode fraction, which is required in different applications including the air quality analysis. An overview of the algorithm, results of AERONET validation, and examples of comparison with MODIS Collection 5 aerosol product, including Deep Blue algorithm, will be presented for different parts of the world including continental USA, Persian Gulf region and India.

Lyapustin, A.↗
NASA NTRS1985

Surface characteristics of Venus derived from Pioneer Venus altimetry, roughness, and reflectivity measurements

Pioneer Venus data sets for global topography, surface roughness, and reflectivity are analyzed, and the geological significance of some of the distinctive surface types is locally calibrated by examining high-resolution images from the Venera lander spacecraft and the Arecibo Observatory. Regional rock and bedrock surfaces are found to cover the majority of the planet. The relatively smooth rock and bedrock surfaces probably represent lava flows. Porous and unconsolidated fine materials cover less than about 27 percent of the surface. The distribution of soil surfaces indicate that they are the result of local weathering and small amounts of lateral transport. Several hypotheses for the origin of the high-dielectric materials which cover a small portion of the surface are discussed.

Head, J. W., III↗
NASA NTRS2010

Hyperspectrally-Resolved Surface Emissivity Derived Under Optically Thin Clouds

Surface spectral emissivity derived from current and future satellites can and will reveal critical information about the Earth s ecosystem and land surface type properties, which can be utilized as a means of long-term monitoring of global environment and climate change. Hyperspectrally-resolved surface emissivities are derived with an algorithm utilizes a combined fast radiative transfer model (RTM) with a molecular RTM and a cloud RTM accounting for both atmospheric absorption and cloud absorption/scattering. Clouds are automatically detected and cloud microphysical parameters are retrieved; and emissivity is retrieved under clear and optically thin cloud conditions. This technique separates surface emissivity from skin temperature by representing the emissivity spectrum with eigenvectors derived from a laboratory measured emissivity database; in other words, using the constraint as a means for the emissivity to vary smoothly across atmospheric absorption lines. Here we present the emissivity derived under optically thin clouds in comparison with that under clear conditions.

Zhou, Daniel K.↗
NASA NTRS2012

Detection Thresholds of Falling Snow from Satellite-Borne Active and Passive Sensors

Precipitation, including rain and snow, is a critical part of the Earth's energy and hydrology cycles. In order to collect information on the complete global precipitation cycle and to understand the energy budget in terms of precipitation, uniform global estimates of both liquid and frozen precipitation must be collected. Active observations of falling snow are somewhat easier to estimate since the radar will detect the precipitation particles and one only needs to know surface temperature to determine if it is liquid rain or snow. The challenges of estimating falling snow from passive spaceborne observations still exist though progress is being made. While these challenges are still being addressed, knowledge of their impact on expected retrieval results is an important key for understanding falling snow retrieval estimations. Important information to assess falling snow retrievals includes knowing thresholds of detection for active and passive sensors, various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types. For example, can a lake effect snow system with low (~2.5 km) cloud tops having an ice water content (Iwe) at the surface of 0.25 g m-3 and dendrite snowflakes be detected? If this information is known, we can focus retrieval efforts on detectable storms and concentrate advances on achievable results. Here, the focus is to determine thresholds of detection for falling snow for various snow conditions over land and lake surfaces. The analysis relies on simulated Weather Research Forecasting (WRF) simulations of falling snow cases since simulations provide all the information to determine the measurements from space and the ground truth. Results are presented for active radar at Ku, Ka, and W-band and for passive radiometer channels from 10 to 183 GHz (Skofronick-Jackson, et al. submitted to IEEE TGRS, April 2012). The notable results show: (1) the W-Band radar has detection thresholds more than an order of magnitude lower than the future GPM sensors, (2) the cloud structure macrophysics influences the thresholds of detection for passive channels, (3) the snowflake microphysics plays a large role in the detection threshold for active and passive instruments, (4) with reasonable assumptions, the passive 166 GHz channel has detection threshold values comparable to the GPM DPR Ku and Ka band radars with ~0.05 g m-3 detected at the surface, or an ~0.5-1 mm hr-l melted snow rate (equivalent to 0.5-2 cm hr-l solid fluffy snowflake rate).

Jackson, Gail↗
NASA NTRS2021

Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval Evaluation

This study focuses on the ability of the Global Precipitation Measurement (GPM) passive microwave sensors to detect and provide quantitative precipitation estimates (QPE) for extreme lake-effect snowfall events over the United States lower Great Lakes region. GPM Microwave Imager (GMI) high frequency channels can clearly detect intense shallow convective snowfall events. However, GMI Goddard PROfiling (GPROF) QPE retrievals produce inconsistent results when compared against the Multi-Radar/Multi-Sensor (MRMS) ground-based radar reference dataset. While GPROF retrievals adequately capture intense snowfall rates and spatial patterns of one event, GPROF systematically underestimates intense snowfall rates in another event. Furthermore, GPROF produces abundant light snowfall rates that do not conform with MRMS observations. Ad-hoc precipitation rate thresholds are suggested to partially mitigate GPROF’s overproduction of light snowfall rates. The sensitivity and retrieval efficiency of GPROF to key parameters (2-meter temperature, total precipitable water, and background surface type) used to constrain the GPROF a-priori retrieval database are investigated. Results demonstrate that typical lake-effect snow environmental and surface conditions, especially coastal surfaces, are underpopulated in the database and adversely affect GPROF retrievals. For the two presented case studies, using snow cover a-priori database in the locations of originally deemed as coastline improves retrieval. This study suggests that it is particularly important to have more accurate GPROF surface classifications and better representativeness of the a-priori databases to improve intense lake-effect snow detection and retrieval performance.

Lisa Milani↗
NASA NTRS2020

An Algorithm for Hyperspectral Remote Sensing of Aerosols: 3. Application to the GEO-TASO Data in KORUS-AQ Field Campaign

This paper describes the third part of a series of investigations to develop algorithms for simultaneous retrieval of aerosol parameters and surface spectral reflectance from the data measured by GEOstationary Trace gas and Aerosol Sensor Optimization (GEO-TASO) instrument. Since the algorithm is designed for future hyperspectral and geostationary satellite sensors, such as Tropospheric Emissions: Monitoring of Pollution (TEMPO), it is applied to GEO-TASO data measured over the same area by different flights as part of the Korea-United Stated Air Quality Study (KORUS-AQ) field campaign in 2016. While GEO-TAOS has a spectral sampling interval of ~0.28 nm in the visible, its data is thinned through a band selection approach with consideration of atmospheric transmittance and different surface types, which yields 20 common spectral bands to be used by the algorithm. The algorithm starts with 4 common principal components (PCs) for surface spectral reflectance extracted from various spectral libraries, and the constraints of surface reflectance parameters and aerosol scattering models respectively from k- means clustering analysis of the Rayleigh-corrected GEO-TASO spectra and AERONET data. The algorithm then proceeds iteratively with an optimal estimation approach to update PCs and retrieve aerosol optical depth (AOD) until the best match between simulated and GEOS-TASO measured spectra is achieved. The spectral AODs are compared between those retrieved from GEO-TASO (x) and 7 AERONETS (y) in 440, 550, 550, 675 nm, respectively. The comparison reveals that iterative updates of surface spectral PCs (and so surface reflectance) yield significant enhancement in AOD retrievals, improving the mean linear fitting equations from y = 0.735x + 0.088 (without update of PCs) to y = 1.055x+ 0.01, and the Pearson correlation coefficient (𝑅𝑅2) from 0.54 to 0.76, respectively. Move case studies are need to further evaluate the algorithm for its application to TEMPO that carries a enhanced version of GEO-TASO instrument.

Tropospheric Emissions: Monitoring of Pollution (T↗
DOE OSTI2022

Effects of Temperature on Flow Fouling of Smooth and Nonwetting Surfaces

Mineral fouling (scaling) of heat transfer surfaces is a pervasive problem in heat exchangers, chemical reactors, and other equipment in energy, environmental, and process industrial applications. Many of the applications involve dynamic flow of fluids with the impurity minerals and, furthermore, operate at elevated temperature. Strategies to mitigate fouling under these conditions are of much value in industrial applications. This paper presents a comparative study of temperature-dependent mineral fouling deposition on smooth surfaces and nonwetting superhydrophobic and lubricant-infused surfaces under dynamic flow conditions. The surfaces are represented in a unified manner using the viscosity ratio of the infused material within the porous asperities on a surface to that of the flowing fluid such that the spectrum of surfaces from superhydrophobic to smooth is captured by the range of viscosity ratios from 0 to ∞. Using a forced convection experimental setup, deposition of calcium sulfate on the surfaces is quantified in terms of asymptotic fouling resistance over a range of temperature, Reynolds number, and mineral foulant supersaturation. Through a systematic set of accelerated fouling experiments, an empirical relationship for the asymptotic fouling resistance is developed in terms of Reynolds number, foulant concentration, temperature, and surface type. The empirical model is validated with a comprehensive set of experimental data from this study as well as from the literature. Optimum nonwetting surface designs for minimizing fouling resistance compared to conventional smooth surfaces are developed as a function of temperature. The results of the study offer insight into the temperature-dependent fouling of surfaces under flow conditions and a rational design of fouling-resistant nonwetting surfaces that can be readily translated to practice.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Imaging the Acceptor Wave Function Anisotropy in Silicon

We present the first scanning tunneling microscopy (STM) image of hydrogenic acceptor wave functions in silicon. These acceptor states appear as square-ring-like features in STM images and originate from near-surface defects introduced by high-energy bismuth implantation into a silicon (001) wafer. Scanning tunneling spectroscopy confirms the formation of a p-type surface. Effective-mass and tight-binding calculations provide an excellent description of the observed square-ring-like features, confirming their acceptor character and attributing their symmetry to the light- and heavy-hole band degeneracy in silicon. A detailed understanding of the energetic and spatial properties of acceptor wave functions in silicon is essential for engineering large-scale acceptor-based quantum devices.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS2010

Standard Deviation of Spatially-Averaged Surface Cross Section Data from the TRMM Precipitation Radar

We investigate the spatial variability of the normalized radar cross section of the surface (NRCS or Sigma(sup 0)) derived from measurements of the TRMM Precipitation Radar (PR) for the period from 1998 to 2009. The purpose of the study is to understand the way in which the sample standard deviation of the Sigma(sup 0) data changes as a function of spatial resolution, incidence angle, and surface type (land/ocean). The results have implications regarding the accuracy by which the path integrated attenuation from precipitation can be inferred by the use of surface scattering properties.

Meneghini, Robert↗