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DOE OSTI2021

Spontaneous dynamical disordering of borophenes in MgB2 and related metal borides

Abstract Layered boron compounds have attracted significant interest in applications from energy storage to electronic materials to device applications, owing in part to a diversity of surface properties tied to specific arrangements of boron atoms. Here we report the energy landscape for surface atomic configurations of MgB 2 by combining first-principles calculations, global optimization, material synthesis and characterization. We demonstrate that contrary to previous assumptions, multiple disordered reconstructions are thermodynamically preferred and kinetically accessible within exposed B surfaces in MgB 2 and other layered metal diborides at low boron chemical potentials. Such a dynamic environment and intrinsic disordering of the B surface atoms present new opportunities to realize a diverse set of 2D boron structures. We validated the predicted surface disorder by characterizing exfoliated boron-terminated MgB 2 nanosheets. We further discuss application-relevant implications, with a particular view towards understanding the impact of boron surface heterogeneity on hydrogen storage performance.

36 MATERIALS SCIENCE↗

NASA NTRS2001

Results from Global Land-Surface Data Assimilation Methods

Realistic representation of the land surface is crucial in global climate modeling (GCM). Recently, the Mosaic land-surface Model (LSM) has been driven off-line using GEOS DAS (Goddard Earth Observing System Data Assimilation System) atmospheric forcing, forming the Off-line Land-surface Global Assimilation (OLGA) system. This system provides a computationally efficient test bed for land surface data assimilation. Here, we validate the OLGA simulation of surface processes and the assimilation of ISCCP surface temperatures. Another component of this study as the incorporation of the Physical-space Statistical Analysis System (PSAS) into OLGA, in order to assimilate surface temperature observations from the International Satellite Cloud Climatology Project (ISCCP). To counteract the subsequent forcing of the analyzed skin temperature back to the initial state following the analysis. incremental bias correction (IBC) was included in the assimilation. The IBC scheme effectively removed the time mean bias, but did not remove him in the mean diurnal cycle. Therefore, a diurnal him correction (DBC) scheme was developed, where the time-dependent bias was modeled with a sine wave parameterization. In addition, quality control of the ISCCP data and anisotropic temperature correction were implemented in PSAS. Preliminary results showed a substantial impact from the inclusion of PSAS and DBC that was visible in the surface meteorology fields and energy budget. Also, the monthly mean diurnal cycle from the experiment closely matched the diurnal cycle from the observations.

Radakovich, Jon D.↗

NASA NTRS2003

Infrared Thermography Flight Experimentation

Analysis was done on IR data collected by DFRC on May 8, 2002. This includes the generation of a movie to initially examine the IR flight data. The production of the movie was challenged by the volume of data that needed to be processed, namely 40,500 images with each image (256 x 252) containing over 264 million points (pixel depth 4096). It was also observed during the initial analysis that the RTD surface coating has a different emissivity than the surroundings. This fact added unexpected complexity in obtaining a correlation between RTD data and IR data. A scheme was devised to generate IR data near the RTD location which is not affected by the surface coating This scheme is valid as long as the surface temperature as measured does not change too much over a few pixel distances from the RTD location. After obtaining IR data near the RTD location, it is possible to make a direct comparison with the temperature as measured during the flight after adjusting for the camera s auto scaling. The IR data seems to correlate well to the flight temperature data at three of the four RID locations. The maximum count intensity occurs closely to the maximum temperature as measured during flight. At one location (RTD #3), there is poor correlation and this must be investigated before any further progress is possible. However, with successful comparisons at three locations, it seems there is great potential to be able to find a calibration curve for the data. Moreover, as such it will be possible to measure temperature directly from the IR data in the near future.

Blanchard, Robert C.↗

NASA NTRS2012

Jet-Surface Interaction Test: Phased Array Noise Source Localization Results

Subsonic jets are relatively simple. The peak noise source location gradually moves upstream toward the nozzle as frequency increases. 2) Supersonic jets are more complicated. The peak noise source location moves downstream as frequency increases through a BBSN hump. 3) In both subsonic and supersonic jets the peak noise source location corresponding to a given frequency of noise moves downstream as jet Mach number increases. 4) The noise generated at a given frequency in a BBSN hump is generated by a small number of shocks, not from all the shocks at the same time. 5) Single microphone spectrum levels decrease when the noise source locations measured with the phased array are blocked by a shielding surface. This consistency validates the phased array data and the stationary monopole source model used to process it. 6) Reflecting surface data illustrate that the law of reflection must be satisfied for noise to reflect off a surface toward an observer. Depending on the relative locations of the jet, the surface and the observer only some of the jet noise sources may satisfy this requirement. 7) The low frequency noise created when a jet flow impinges on a surface comes primarily from the trailing edge regardless of the axial extent impacted by the flow.

Podboy, Gary↗

NASA NTRS2018

An Initial Validation of Landsat 5 and 7 Derived Surface Water Temperature for U.S. Lakes, Reservoirs, and Estuaries

The United States Harmful Algal Bloom and Hypoxia Research Control Act of 2014 identified the need for forecasting and monitoring harmful algal blooms (HAB) in lakes, reservoirs, and estuaries across the nation. Temperature is a driver in HAB forecasting models that affects both HAB growth rates and toxin production. Therefore, temperature data derived from the U.S. Geological Survey Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus thermal band products were validated across 35 lakes and reservoirs, and 24 estuaries. In situ data from the Water Quality Portal (WQP) were used for validation. The WQP serves data collected by state, federal, and tribal groups. Discrete in situ temperature data included measurements at 11,910 U.S. lakes and reservoirs from 1980 through 2015. Landsat temperature measurements could include 170,240 lakes and reservoirs once an operational product is achieved. The Landsat-derived temperature mean absolute error was 1.34 C in lake pixels (is) greater than180 m from land, 4.89 C at the land-water boundary, and 1.11 C in estuaries based on comparison against discrete surface in situ measurements. This is the first study to quantify Landsat resolvable U.S. lakes and reservoirs, and large-scale validation of an operational satellite provisional temperature climate data record algorithm. Due to the high performance of open water pixels, Landsat satellite data may supplement traditional in situ sampling by providing data for most U.S. lakes, reservoirs, and estuaries over consistent seasonal intervals (even with cloud cover) for an extended period of record of more than 35 years.

Schaeffer, Blake A.↗

NASA NTRS1994

A simple hydrologically based model of land surface water and energy fluxes for general circulation models

A generalization of the single soil layer variable infiltration capacity (VIC) land surface hydrological model previously implemented in the Geophysical Fluid Dynamics Laboratory (GFDL) general circulation model (GCM) is described. The new model is comprised of a two-layer characterization of the soil column, and uses an aerodynamic representation of the latent and sensible heat fluxes at the land surface. The infiltration algorithm for the upper layer is essentially the same as for the single layer VIC model, while the lower layer drainage formulation is of the form previously implemented in the Max-Planck-Institut GCM. The model partitions the area of interest (e.g., grid cell) into multiple land surface cover types; for each land cover type the fraction of roots in the upper and lower zone is specified. Evapotranspiration consists of three components: canopy evaporation, evaporation from bare soils, and transpiration, which is represented using a canopy and architectural resistance formulation. Once the latent heat flux has been computed, the surface energy balance is iterated to solve for the land surface temperature at each time step. The model was tested using long-term hydrologic and climatological data for Kings Creek, Kansas to estimate and validate the hydrological parameters, and surface flux data from three First International Satellite Land Surface Climatology Project Field Experiment (FIFE) intensive field campaigns in the summer-fall of 1987 to validate the surface energy fluxes.

Liang, XU↗

NASA NTRS1993

Validation of ISCCP cloud detections

This paper describes the validation of the International Satellite Cloud Climatology project (ISCCP)cloud detections by verifying the accuracy of the infrared clear-sky radiances. Comparison of retrieved surface temperatures to other measurements shows that bias errors are less than 2 K and random errors are about 2 K for sea surface (monthly means at 280-km scales) and that bias errors are less than 2 K and random errors are about 4 K for land surfaces (3 hourly at 280-km scales). Bias errors over a few persistently cloudy locations are sometimes -(2-4) K and over winter sea ice may be about +2 K. Surface reflectances are confirmed to be within 3% of other measurements and models for ocean, except for sun glint geometries, and to be within 3%-5% for land surfaces. Sufficiently accurate validation data are not avilable for visible reflectances of sea ice and snow-covered land, but some tests of specific cases suggest that errors are approximately 10%. These errors in clear-sky radiances suggest uncertainties in the ISCCP cloud detections of about 10% with a small (3%-6%) negative bias over land. Some specific regions exhibit both larger rms uncertainties and somewhat larger biases in cloud amount approaching 10%. ISCCP cloud detections are more in error over the polar regions than anywhere else. Based on comparisons with an anlysis of radiances measured at other wavelengths, the ISCCP analysis appears to miss 15%-25% of the clouds in summer but only 5%-10% of the winter clouds.

Rossow, William B.↗

NASA NTRS2018

The Impact of Satellite Sea Surface Salinity for Prediction of the Coupled Indo-Pacific System

We assess the impact of satellite sea surface salinity (SSS) observations on seasonal to interannual variability of tropical Indo-Pacific Ocean dynamics as well as on dynamical ENSO forecasts. Our coupled model is composed of a primitive equation ocean model for the tropical Indo-Pacific region that is coupled with the global SPEEDY atmospheric model (Molteni, 2003). The Ensemble Reduced Order Kalman Filter is used to assimilate observations to constrain dynamics and thermodynamics for initialization of the coupled model. The baseline experiment assimilates satellite sea level, SST, and in situ subsurface temperature and salinity observations. This baseline is then compared with experiments that additionally assimilate Aquarius (version 4.0) and SMAP (version 2.0) SSS. Twelve-month forecasts are initialized for each month from Sep. 2011 to Dec. 2016. We find that including satellite SSS significantly improves NINO 3.4 sea surface temperature anomaly validation after 1 out to 12 month forecast lead times. For initialization of the coupled forecast, the positive impact of SSS assimilation is brought about by surface freshening near the eastern edge of the western Pacific warm pool and density changes that lead to shallower mixed layer between 10 degrees South latitude-5 degrees North latitude. SST differences at initialization force wide-spread downwelling favorable curl over most of the tropical Pacific. Over an average forecast, SST remains warmer with SSS assimilation at the eastern edge of the warm pool. This warm SST propagates into the eastern Pacific and drags westerly wind anomalies eastward into the NINO 3.4 region. In addition, salting near the ITCZ (Intertropical Convergence Zone) leads to a deepening of the mixed layer and thermocline near 8 degrees North latitude. These patterns together lead to a funneling effect that provides the background state to amplify equatorial Kelvin waves. We show that the downwelling Kelvin waves are amplified by assimilating satellite SSS and lead to significantly improved forecasts particularly for the 2015 El Nino.

El Nino↗

NASA NTRS2018

The Impact of Satellite Sea Surface Salinity for Prediction of the Coupled Indo-Pacific System

We assess the impact of satellite sea surface salinity (SSS) observations on seasonal to interannual variability of tropical Indo-Pacific Ocean dynamics as well as on dynamical ENSO forecasts. Our coupled model is composed of a primitive equation ocean model for the tropical Indo-Pacific region that is coupled with the global SPEEDY atmospheric model (Molteni, 2003). The Ensemble Reduced Order Kalman Filter is used to assimilate observations to constrain dynamics and thermodynamics for initialization of the coupled model. The baseline experiment assimilates satellite sea level, SST, and in situ subsurface temperature and salinity observations. This baseline is then compared with experiments that additionally assimilate Aquarius (version 4.0) and SMAP (version 2.0) SSS. Twelve-month forecasts are initialized for each month from Sep. 2011 to Dec. 2016. We find that including satellite SSS significantly improves NINO3.4 sea surface temperature anomaly validation after 1 out to 12 month forecast lead times. For initialization of the coupled forecast, the positive impact of SSS assimilation is brought about by surface freshening near the eastern edge of the western Pacific warm pool and density changes that lead to shallower mixed layer between 10S-5N. SST differences at initialization force wide-spread downwelling favorable curl over most of the tropical Pacific. Over an average forecast, SST remains warmer with SSS assimilation at the eastern edge of the warm pool. This warm SST propagates into the eastern Pacific and drags westerly wind anomalies eastward into the NINO3.4 region. In addition, salting near the ITCZ leads to a deepening of the mixed layer and thermocline near 8N. These patterns together lead to a funneling effect that provides the background state to amplify equatorial Kelvin waves. We show that the downwelling Kelvin waves are amplified by assimilating satellite SSS and lead to significantly improved forecasts particularly for the 2015 El Nino.

SST↗

NASA NTRS2004

Physical Validation of TRMM TMI and PR Monthly Rain Products Over Oklahoma

The Tropical Rainfall Measuring Mission (TRMM) provides monthly rainfall estimates using data collected by the TRMM satellite. These estimates cover a substantial fraction of the earth's surface. The physical validation of TRMM estimates involves corroborating the accuracy of spaceborne estimates of areal rainfall by inferring errors and biases from ground-based rain estimates. The TRMM error budget consists of two major sources of error: retrieval and sampling. Sampling errors are intrinsic to the process of estimating monthly rainfall and occur because the satellite extrapolates monthly rainfall from a small subset of measurements collected only during satellite overpasses. Retrieval errors, on the other hand, are related to the process of collecting measurements while the satellite is overhead. One of the big challenges confronting the TRMM validation effort is how to best estimate these two main components of the TRMM error budget, which are not easily decoupled. This four-year study computed bulk sampling and retrieval errors for the TRMM microwave imager (TMI) and the precipitation radar (PR) by applying a technique that sub-samples gauge data at TRMM overpass times. Gridded monthly rain estimates are then computed from the monthly bulk statistics of the collected samples, providing a sensor-dependent gauge rain estimate that is assumed to include a TRMM equivalent sampling error. The sub-sampled gauge rain estimates are then used in conjunction with the monthly satellite and gauge (without sub- sampling) estimates to decouple retrieval and sampling errors. The computed mean sampling errors for the TMI and PR were 5.9% and 7.796, respectively, in good agreement with theoretical predictions. The PR year-to-year retrieval biases exceeded corresponding TMI biases, but it was found that these differences were partially due to negative TMI biases during cold months and positive TMI biases during warm months.

Fisher, Brad L.↗

NASA NTRS2011

Ultraspectral Sounding Retrieval Error Budget and Estimation

The ultraspectral infrared radiances obtained from satellite observations provide atmospheric, surface, and/or cloud information. The intent of the measurement of the thermodynamic state is the initialization of weather and climate models. Great effort has been given to retrieving and validating these atmospheric, surface, and/or cloud properties. Error Consistency Analysis Scheme (ECAS), through fast radiative transfer model (RTM) forward and inverse calculations, has been developed to estimate the error budget in terms of absolute and standard deviation of differences in both spectral radiance and retrieved geophysical parameter domains. The retrieval error is assessed through ECAS without assistance of other independent measurements such as radiosonde data. ECAS re-evaluates instrument random noise, and establishes the link between radiometric accuracy and retrieved geophysical parameter accuracy. ECAS can be applied to measurements of any ultraspectral instrument and any retrieval scheme with associated RTM. In this paper, ECAS is described and demonstration is made with the measurements of the METOP-A satellite Infrared Atmospheric Sounding Interferometer (IASI)..

Zhou, Daniel K.↗

NASA NTRS2018

Evaluation of the Land Surface Reflectance Fundamental Climate Data Record

The land surface reflectance is a fundamental climate data record at the basis of the derivation of other climate data records (Albedo, LAI/Fpar, Vegetation indices) and has been recognized as a key parameter in the understanding of the land-surface-climate processes. In this presentation, we present the validation of the Land surface reflectance used for MODIS, VIIRS, Landsat 8 and Sentinel 2 data. This methodology uses the 6SV Code and data from the AERONET network. The overall accuracy clearly reaches the satellite specifications. To understand how to improve the validation, we developed an exhaustive error budget. Results show an impact of the absorption of aerosol and of the fine mode volume concentration.

Atmospheric correction↗

NASA NTRS2023

Atmospheric Correction Inter-comparison eXercise, ACIX-II Land: An Assessment of Amospheric Correction Processors for Landsat 8 and Sentinel-2 Over Land

The correction of the atmospheric effects on optical satellite images is essential for quantitative and multi-temporal remote sensing applications. In order to study the performance of the state-of-the-art methods in an integrated way, a voluntary and open-access benchmark Atmospheric Correction Inter-comparison eXercise (ACIX) was initiated in 2016 in the frame of Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). The first exercise was extended in a second edition wherein twelve atmospheric correction (AC) processors, a substantially larger testing dataset and additional validation metrics were involved. The sites for the inter-comparison analysis were defined by investigating the full catalogue of the Aerosol Robotic Network (AERONET) sites for coincident measurements with satellites' overpass. Although there were more than one hundred sites for Copernicus Sentinel-2 and Landsat 8 acquisitions, the analysis presented in this paper concerns only the common matchups amongst all processors, reducing the number to 79 and 62 sites respectively. Aerosol Optical Depth (AOD) and Water Vapour (WV) retrievals were consequently validated based on the available AERONET observations. The processors mostly succeeded in retrieving AOD for relatively light to medium aerosol loading (AOD < 0.2) with uncertainties <0.08, while the overall uncertainty values were typically 0.23 ± 0.15. Better performances were observed for WV retrievals with >90% of the results falling within the suggested empirical specifications and with the Root Mean Square Error (RMSE) being mostly <0.25 g/cm2. Regarding Surface Reflectance (SR) validation two main approaches were followed. For the first one, a simulated SR reference dataset was computed over all of the test sites by using the 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum vector code) full radiative transfer modelling (RTM) and AERONET measurements for the required aerosol variables and water vapour content. The performance assessment demonstrated that the retrievals were not biased for most of the bands. The uncertainties ranged from approximately 0.003 to 0.01 (excluding B01) for the best performing processors in both sensors' analyses. For the second one, measurements from the radiometric calibration network RadCalNet over La Crau (France) and Gobabeb (Namibia) were involved in the validation. The performance of the processors was in general consistent across all bands for both sensors and with low standard deviations (<0.04) between on-site and estimated surface reflectance. Overall, our study provides a good insight of AC algorithms' performance to developers and users, pointing out similarities and differences for AOD, WV and SR retrievals. Such validation though still lacks of ground-based measurements of known uncertainty to better assess and characterize the uncertainties in SR retrievals.

Atmospheric correction↗

NASA NTRS2008

Extreme Universe Space Observatory (EUSO) Optics Module

A demonstration part will be manufactured in Japan on one of the large Toshiba machines with a diameter of 2.5 meters. This will be a flat PMMA disk that is cut between 0.5 and 1.25 meters radius. The cut should demonstrate manufacturing the most difficult parts of the 2.5 meter Fresnel pattern and the blazed grating on the diffractive surface. Optical simulations, validated with the subscale prototype, will be used to determine the limits on manufacturing errors (tolerances) that will result in optics that meet EUSO s requirements. There will be limits on surface roughness (or errors at high spatial frequency); radial and azimuthal slope errors (at lower spatial frequencies) and plunge cut depth errors in the blazed grating. The demonstration part will be measured to determine whether it was made within the allowable tolerances.

Young, Roy↗

NASA NTRS1994

Atmospheric correction of LANDSAT TM thermal band using surface energy balance

Thermal infrared data of LANDSAT Thematic Mapper (TM) are hardly used, probably due to the difficulties met when trying to correct them for atmospheric effects. A method for correcting these data was designed, based on surface energy balance estimation of known wet and dry targets included in the TM image to be corrected. This method, only using the image itself and local meteorological data was tested and validated on various surfaces: agricultural, forest and rangeland. The root mean square error on corrected temperatures is on the order of 1C.

Vidal, Alain↗

NASA NTRS1995

Regional scale hydrology with a new land surface processes model

Through the CaPE Hydrometeorology Project, we have developed an understanding of some of the unique data quality issues involved in assimilating data of disparate types for regional-scale hydrologic modeling within a GIS framework. Among others, the issues addressed here include the development of adequate validation of the surface water budget, implementation of the STATSGO soil data set, and implementation of a remote sensing-derived landcover data set to account for surface heterogeneity. A model of land surface processes has been developed and used in studies of the sensitivity of surface fluxes and runoff to soil and landcover characterization. Results of these experiments have raised many questions about how to treat the scale-dependence of land surface-atmosphere interactions on spatial and temporal variability. In light of these questions, additional modifications are being considered for the Marshall Land Surface Processes Model. It is anticipated that these techniques can be tested and applied in conjunction with GCIP activities over regional scales.

Laymon, Charles↗

NASA NTRSDate not supplied

Towards A More Realistic Representation of Nasa Ceres-Derived Surface Radiative Fluxes During Polar Night: A Comparison With the Mosaic Field Campaign

The Arctic remains one of the more difficult regions observe, and so satellite observations are a critical tool for observing the region, such as those from Clouds and the Earth’s Radiant Energy System (CERES). But validating the satellite surface radiative flux estimates is difficult because of the lack of in situ measurements. The extensive high-quality surface radiative flux and meteorological measurements collected from MOSAiC provide a useful check on flux retrievals from CERES instruments. We compare MOSAiC and CERES surface radiative fluxes during October through March of 2019-2020 using the large set of meteorological measurements also collected by MOSAiC, specifically cloud properties. Previous work identified a significant source of error in the CERES estimate of surface downwelling longwave flux as the estimate of low level cloud amount when compared with MOSAiC W-band radar measurements. Continuing this work, we examine the effects of errors in cloud water path on surface radiative fluxes. When using all cloud conditions, errors in cloud water are also significantly correlated with surface radiative flux errors, though the size off the effect is only about half that of cloud amount. But when considering low cloud conditions only, the effects of cloud water and cloud amount are comparable.

J. Brant Dodson↗

NASA NTRSDate not supplied

Towards a More Realistic Representation of NASA CERES-derived Surface Radiative Fluxes during Polar Night: A Comparison with the MOSAiC Field Campaign

J Brant Dodson↗