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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Infrared and Passive Microwave Radiometric Sea Surface Temperatures and Their Relationships to Atmospheric Forcing

The current generation of infrared (IR) and passive microwave (MW) satellite sensors provides highly complementary information for monitoring sea surface temperature (SST). On the one hand, infrared sensors provide high resolution and high accuracy but are obscured by clouds. Microwave sensors on the other hand, provide coverage through non-precipitating clouds but have coarser resolution and generally poorer accuracy. Assuming that the satellite SST measurements do not have spatially variable biases, they can be blended combining the merits of both SST products. These factors have motivated recent work in blending the MW and IR data in an attempt to produce high-accuracy SST products with improved coverage in regions with persistent clouds. The primary sources of retrieval uncertainty are, however, different for the two sensors. The main uncertainty in the MW retrievals lies in the effects of wind-induced surface roughness and foam on emissivity, whereas the IR retrievals are more sensitive to the atmospheric water vapor and aerosol content. Average nighttime differences between the products for the month periods of January 1999 and June 2000 are shown. These maps show complex spatial and temporal differences as indicated by the strong spatially coherent features in the product differences and the changes between seasons. Clearly such differences need to be understood and accounted for if the products are to be combined. The overall goals of this project are threefold: (1) To understand the sources of uncertainty in the IR and MW SST retrievals and to characterize the errors affecting the two types of retrieval as a fiction of atmospheric forcing; (2) To demonstrate how representative the temperature difference between the two satellite products is of Delta T; (3) To apply bias adjustments and to device a comprehensive treatment of the behavior of the temperature difference across the oceanic skin layer to determine the best method for blending thermal infrared and passive microwave measurements of SSTs.

Castro, Sandra L.↗
DOE OSTI2025

Advancing Organized Convection Representation in the Unified Model: Implementing and Enhancing Multiscale Coherent Structure Parameterization

To address the effect of stratiform latent heating on meso- to large-scale circulations, an enhanced implementation of the Multiscale Coherent Structure Parameterization (MCSP) is developed for the Met Office Unified Model. MCSP represents the top-heavy stratiform latent heating from under-resolved organized convection in general circulation models. We couple the MCSP with a mass-flux convection scheme (CoMorph-A) to improve storm lifecycle continuity. The improved MCSP trigger is specifically designed for mixed-phase deep convective cloud, combined with a background vertical wind shear, both known to be crucial for stratiform development. We also test a cloud top temperature dependent convective-stratiform heating partitioning, in contrast to the earlier fixed partitioning. Assessments from ensemble weather forecasts and decadal simulations demonstrate that MCSP directly reduces cloud deepening and precipitation areas by moderating mesoscale circulations. Indirectly, it amends tropical precipitation biases, notably correcting dry and wet biases over India and the Indian Ocean, respectively. Remarkably, the scheme outperforms a climate model ensemble by improving seasonal precipitation cycle predictions in these regions. The scheme also improves Madden-Julian Oscillation (MJO) spectra, achieving better alignment with observational and reanalysis data by intensifying the simulated MJO over the Indian Ocean during phases 4 to 5. However, the scheme increases precipitation overestimation over the Western Pacific. Shifting from fixed to temperature-dependent convective-stratiform partitioning reduces the Pacific precipitation overestimation and further improves the seasonal cycle in India. Spatially correlated biases highlight the necessity for advances beyond deterministic approaches to align MCSP with environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Retrospective Analysis and Bayesian Model Averaging of CMIP6 Precipitation in the Nile River Basin

The Nile River basin is one of the global hotspots vulnerable to climate change impacts because of a fast-growing population and geopolitical tensions. Previous studies demonstrated that general circulation models (GCMs) frequently show disagreement in the sign of change in annual precipitation projections. Here, we first evaluate the performance of 20 GCMs from phase six of the Coupled Model Intercomparison Project (CMIP6) benchmarked against a high-spatial-resolution precipitation dataset dating back to 1983 from Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR). Next, a Bayesian model averaging (BMA) approach is adopted to derive probability distributions of precipitation projections in the Nile basin. Retrospective analysis reveals that most GCMs exhibit considerable (up to 64% of mean annual precipitation) and spatially heterogenous bias in simulating annual precipitation. Moreover, it is shown that all GCMs underestimate interannual variability; thus, the ensemble range is underdispersive and is a poor indicator of uncertainty. The projected changes from the BMA model show that the value and sign of change vary considerably across the Nile basin. Specifically, it is found that projected changes in the two headwaters basins, namely, the Blue Nile and Upper White Nile, are 0.03% and -1.65%, respectively; both are statistically insignificant at α = 0.05. The uncertainty range estimated from the BMA model shows that the probability of a precipitation decrease is much higher in the Upper White Nile basin whereas projected change in the Blue Nile is highly uncertain both in magnitude and sign of change.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1978

An investigation of the observability of ocean-surface parameters using GEOS-3 backscatter data

The degree to which ocean surface roughness can be synoptically observed through use of the information extracted from the GEOS-3 backscattered waveform data was evaluated. Algorithms are given for use in estimating the radar sensed waveheight distribution or ocean-surface impulse response. Other factors discussed include comparisons between theoretical and experimental radar cross section values, sea state bias effects, spatial variability of significant waveheight data, and sensor-related considerations.

Miller, L. S.↗
NASA NTRS1986

Theoretical and material studies on thin-film electroluminescent devices

A theoretical study of resonant tunneling in multilayered heterostructures is presented based on an exact solution of the Schroedinger equation under the application of a constant electric field. By use of the transfer matrix approach, the transmissivity of the structure is determined as a function of the incident electron energy. The approach presented is easily extended to many layer structures where it is more accurate than other existing transfer matrix or WKB models. The transmission resonances are compared to the bound state energies calculated for a finite square well under bias using either an asymmetric square well model or the exact solution of an infinite square well under the application of an electric field. The results show good agreement with other existing models as well as with the bound state energies. The calculations were then applied to a new superlattice structure, the variablly spaced superlattice energy filter, (VSSEP) which is designed such that under bias the spatial quantization levels fully align. Based on these calculations, a new class of resonant tunneling superlattice devices can be designed.

Summers, C. J.↗
NASA NTRS1987

Theory of resonant tunneling in a variably spaced multiquantum well structure - An Airy function approach

A theoretical study of resonant tunneling in multilayered heterostructures is presented based on an exact solution of the Schroedinger equation under the application of a constant electric field. By use of the transfer matrix approach, the transmissivity of the structure is determined as a function of the incident electron energy. The approach presented herein is easily extended to many layer structures where it is more accurate than other existing transfer matrix or Wentzel-Kramers-Brillouin (WKB) models. The transmission resonances are compared to the bound-state energies calculated for a finite square well under bias using either an asymmetric square-well model or the exact solution of an infinite square well under the application of an electric field. The results show good agreement with other existing models as well as with the bound-state energies. The calculations were then applied to a new superlattice structure, the variably spaced superlattice energy filter, which is designed such that under bias the spatial quantization levels fully align. Based on these calculations, a new class of resonant tunneling superlattice devices can be designed.

Brennan, K. F.↗
NASA NTRS1999

Comparisons of Upper Tropospheric Humidity Retrievals from TOVS and METEOSAT

Two different methods for retrieving Upper Tropospheric Humidities (UTH) from the TOVS (TIROS Operational Vertical Sounder) instruments aboard NOAA polar orbiting satellites are presented and compared. The first one, from the Environmental Technology Laboratory, computed by J. Bates and D. Jackson (hereafter BJ method), estimates UTH from a simplified radiative transfer analysis of the upper tropospheric infrared water vapor channel at wavelength measured by HIRS (6.3 micrometer). The second one results from a neural network analysis of the TOVS (HIRS and MSU) data developed at, the Laboratoire de Meteorologie Dynamique (hereafter the 3I (Improved Initialization Inversion) method). Although the two methods give very similar retrievals in temperate regions (30-60 N and S), an absolute bias up to 16% appears in the convective zone of the tropics. The two datasets have also been compared with UTH retrievals from infrared radiance measurements in the 6.3 micrometer channel from the geostationary satellite METEOSAT (hereafter MET method). The METEOSAT retrievals are systematically drier than the TOVS-based results by an absolute bias between 5 and 25%. Despite the biases, the spatial and temporal correlations are very good. The purpose of this study is to explain the deviations observed between the three datasets. The sensitivity of UTH to air temperature and humidity profiles is analysed as are the clouds effects. Overall, the comparison of the three retrievals gives an assessment of the current uncertainties in water vapor amounts in the upper troposphere as determined from NOAA and METEOSAT satellites.

Escoffier, C.↗
NASA NTRS2003

The Orbiting Carbon Observatory (OCO) Mission

This viewgraph presentation gives a general overview of The Orbiting Carbon Observatory (OCO) Mission. The contents include: 1) The Orbiting Carbon Observatory (OCO); 2) Why Measure CO2?; 3) An Uncertain Future; 4) Monitoring CO2 from Space; 5) Validation Program Ensures Accuracy and Minimizes Spatially Coherent Biases; 6) The OCO Instrument; 7) The OCO Spacecraft; 8) Mission Operations and Data Downlink; 9) OCO Data Products; 10) OCO Data Product Summary; 11) OCO in the A-Train; 12) Summary; and 13) Simulated Performance.

Pathfinder Program↗
NASA NTRS2010

Advances in Landslide Hazard Forecasting: Evaluation of Global and Regional Modeling Approach

A prototype global satellite-based landslide hazard algorithm has been developed to identify areas that exhibit a high potential for landslide activity by combining a calculation of landslide susceptibility with satellite-derived rainfall estimates. A recent evaluation of this algorithm framework found that while this tool represents an important first step in larger-scale landslide forecasting efforts, it requires several modifications before it can be fully realized as an operational tool. The evaluation finds that the landslide forecasting may be more feasible at a regional scale. This study draws upon a prior work's recommendations to develop a new approach for considering landslide susceptibility and forecasting at the regional scale. This case study uses a database of landslides triggered by Hurricane Mitch in 1998 over four countries in Central America: Guatemala, Honduras, EI Salvador and Nicaragua. A regional susceptibility map is calculated from satellite and surface datasets using a statistical methodology. The susceptibility map is tested with a regional rainfall intensity-duration triggering relationship and results are compared to global algorithm framework for the Hurricane Mitch event. The statistical results suggest that this regional investigation provides one plausible way to approach some of the data and resolution issues identified in the global assessment, providing more realistic landslide forecasts for this case study. Evaluation of landslide hazards for this extreme event helps to identify several potential improvements of the algorithm framework, but also highlights several remaining challenges for the algorithm assessment, transferability and performance accuracy. Evaluation challenges include representation errors from comparing susceptibility maps of different spatial resolutions, biases in event-based landslide inventory data, and limited nonlandslide event data for more comprehensive evaluation. Additional factors that may improve algorithm performance accuracy include incorporating additional triggering factors such as tectonic activity, anthropogenic impacts and soil moisture into the algorithm calculation. Despite these limitations, the methodology presented in this regional evaluation is both straightforward to calculate and easy to interpret, making results transferable between regions and allowing findings to be placed within an inter-comparison framework. The regional algorithm scenario represents an important step in advancing regional and global-scale landslide hazard assessment and forecasting.

Kirschbaum, Dalia B.↗
NASA NTRS2010

Global Energy and Water Budgets in MERRA

Reanalyses, retrospectively analyzing observations over climatological time scales, represent a merger between satellite observations and models to provide globally continuous data and have improved over several generations. Balancing the Earth s global water and energy budgets has been a focus of research for more than two decades. Models tend to their own climate while remotely sensed observations have had varying degrees of uncertainty. This study evaluates the latest NASA reanalysis, called the Modern Era Retrospective-analysis for Research and Applications (MERRA), from a global water and energy cycles perspective. MERRA was configured to provide complete budgets in its output diagnostics, including the Incremental Analysis Update (IAU), the term that represents the observations influence on the analyzed states, alongside the physical flux terms. Precipitation in reanalyses is typically sensitive to the observational analysis. For MERRA, the global mean precipitation bias and spatial variability are more comparable to merged satellite observations (GPCP and CMAP) than previous generations of reanalyses. Ocean evaporation also has a much lower value which is comparable to observed data sets. The global energy budget shows that MERRA cloud effects may be generally weak, leading to excess shortwave radiation reaching the ocean surface. Evaluating the MERRA time series of budget terms, a significant change occurs, which does not appear to be represented in observations. In 1999, the global analysis increments of water vapor changes sign from negative to positive, and primarily lead to more oceanic precipitation. This change is coincident with the beginning of AMSU radiance assimilation. Previous and current reanalyses all exhibit some sensitivity to perturbations in the observation record, and this remains a significant research topic for reanalysis development. The effect of the changing observing system is evaluated for MERRA water and energy budget terms.

Bosilovich, Michael G.↗
NASA NTRS2014

Comparison Of Downscaled CMIP5 Precipitation Datasets For Projecting Changes In Extreme Precipitation In The San Francisco Bay Area.

Water resource managers planning for the adaptation to future events of extreme precipitation now have access to high resolution downscaled daily projections derived from statistical bias correction and constructed analogs. We also show that along the Pacific Coast the Northern Oscillation Index (NOI) is a reliable predictor of storm likelihood, and therefore a predictor of seasonal precipitation totals and likelihood of extremely intense precipitation. Such time series can be used to project intensity duration curves into the future or input into stormwater models. However, few climate projection studies have explored the impact of the type of downscaling method used on the range and uncertainty of predictions for local flood protection studies. Here we present a study of the future climate flood risk at NASA Ames Research Center, located in South Bay Area, by comparing the range of predictions in extreme precipitation events calculated from three sets of time series downscaled from CMIP5 data: 1) the Bias Correction Constructed Analogs method dataset downscaled to a 1/8 degree grid (12km); 2) the Bias Correction Spatial Disaggregation method downscaled to a 1km grid; 3) a statistical model of extreme daily precipitation events and projected NOI from CMIP5 models. In addition, predicted years of extreme precipitation are used to estimate the risk of overtopping of the retention pond located on the site through simulations of the EPA SWMM hydrologic model. Preliminary results indicate that the intensity of extreme precipitation events is expected to increase and flood the NASA Ames retention pond. The results from these estimations will assist flood protection managers in planning for infrastructure adaptations.

Storm↗
NASA NTRS2017

Mitigating Satellite-Based Fire Sampling Limitations in Deriving Biomass Burning Emission Rates: Application to WRF-Chem Model over the Northern sub-Saharan African Region

Largely used in several independent estimates of fire emissions, fire products based on MODIS sensors aboard the Terra and Aqua polar-orbiting satellites have a number of inherent limitations, including (a) inability to detect fires below clouds, (b) significant decrease of detection sensitivity at the edge of scan where pixel sizes are much larger than at nadir, and (c) gaps between adjacent swaths in tropical regions. To remedy these limitations, an empirical method is developed here and applied to correct fire emission estimates based on MODIS pixel level fire radiative power measurements and emission coefficients from the Fire Energetics and Emissions Research (FEER) biomass burning emission inventory. The analysis was performed for January 2010 over the northern sub-Saharan African region. Simulations from WRF-Chem model using original and adjusted emissions are compared with the aerosol optical depth (AOD) products from MODIS and AERONET as well as aerosol vertical profile from CALIOP data. The comparison confirmed an 30-50% improvement in the model simulation performance (in terms of correlation, bias, and spatial pattern of AOD with respect to observations) by the adjusted emissions that not only increases the original emission amount by a factor of two but also results in the spatially continuous estimates of instantaneous fire emissions at daily time scales. Such improvement cannot be achieved by simply scaling the original emission across the study domain. Even with this improvement, a factor of two underestimations still exists in the modeled AOD, which is within the current global fire emissions uncertainty envelope. Plain Language Summary Polar-orbiting satellites sensors, such as MODIS, have limitations in detecting fires under clouds or when viewing angles are large or in the gaps among satellites' different ground swaths. Here we developed an empirical method to mitigate the effect of these limitations in fire emission estimate. The method is applied to a fire emission inventory (FEER) based on MODIS. We show that, with our method, the adjusted emission inventory improves WRF-Chem simulation of smoke transport and distribution.

emissions properties↗
NASA NTRS1995

The Barberplaid illusion: Even Perceived Plaid Motion is Biased by Asymmetric Apertures

Our results show that the perceived direction of motion of plaids windowed by asymmetric spatial Gaussians is biased toward the long axis of the window. The bias increases as the relative angle between the plaid motion and the window increases, peaks at a relative angle of about 40 degrees, and then decreases. The peak bias was 14 degrees for a spatial frequency of 0.6 cpd and a window aspect ratio of 4.0. The biases increase as the window is elongated and decrease as the component spatial frequency increases. We tested the predictions of several models of human motion processing (cross correlation, motion energy, intersection of constraints, and vector sum), and show that none of these can predict our data. These results suggest that spatial integration of motion signals plays a crucial role in the perception of plaid motion.

Beutter, Brent↗
NASA NTRS1996

The barberplaid illusion: plaid motion is biased by elongated apertures

The perceived direction of motion of plaids windowed by elongated spatial Gaussians is biased toward the window's long axis. The bias increases as the relative angle between the plaid motion and the long axis of the window increases, peaks at a relative angle of approximately 45 deg, and then decreases. The bias increases as the window is made narrower (at fixed height) and decreases as the component spatial frequency increases (at fixed aperture size). We examine several models of human motion processing (cross-correlation, motion-energy, intersection-of-constraints, and vector-sum), and show that none of these standard models can predict our data. We conclude that spatial integration of motion signals plays a crucial role in plaid motion perception and that current models must be explicitly expanded to include such spatial interactions.

NASA Center ARC↗
DOE OSTI2023

Large Ensemble Particle Filter for Spatial Climate Reconstructions Using a Linear Inverse Model

Proxy records that document the last 2000 years of climate provide evidence for the wide range of the natural climate variability from inter-annual to secular timescales not captured by the short window of recent direct observations. Assessing climate models ability to reproduce such natural variations is crucial to understand climate sensitivity and impacts of future climate change. Paleoclimate data assimilation (PDA) offers a powerful way to extend the short instrumental period by optimally combining the physics described by General Circulation Climate Models (GCMs) with information from available proxy records while taking into account their uncertainties. Here we present a new PDA approach based on a sequential importance resampling (SIR) Particle filter (PF) that uses Linear Inverse Modeling (LIM) as an emulator of several CMIP-class GCMs. We examine in a perfect-model framework the skill of the various LIMs to forecast the dynamics of the surface temperatures and provide spatial field reconstructions over the last millennium in a SIR PF. Our results show that the LIMs allow for skillful ensemble forecasts at 1-year lead-time based on GCMs dynamical knowledge with best prediction in the tropics and the North Atlantic. The PDA further provides a set of physically consistent spatial fields allowing robust uncertainty quantification related to climate models biases and proxy spatial sampling. Our results indicate that the LIM yields dynamical memory improving climate variability reconstructions and support the use of the LIM as a GCM-emulator in real reconstruction to propagate large ensembles of particles at low cost in SIR PF.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Quantifying albedo susceptibility biases in shallow clouds

The evaluation of radiative forcing associated with aerosol–cloud interactions remains a significant source of uncertainty in future climate projections. The problem is confounded by the fact that aerosol particles influence clouds locally and that averaging to larger spatial and/or temporal scales carries biases that depend on the heterogeneity and spatial correlation of the interacting fields and the nonlinearity of the responses. Mimicking commonly applied satellite data analyses for calculation of albedo susceptibility S o , we quantify S o aggregation biases using an ensemble of 127 large eddy simulations of marine stratocumulus. We explore the cloud field properties that control this spatial aggregation bias and quantify the bias for a large range of shallow stratocumulus cloud conditions manifesting a variety of morphologies and ranges of cloud fractions. We show that S o spatial aggregation biases can be on the order of hundreds of percent, depending on the methodology. Key uncertainties emanate from the typically applied adiabatic drop concentration N d retrieval, the correlation between aerosol and cloud fields, and the extent to which averaging reduces the variance in cloud albedo A c and N d . So biases are more often positive than negative and are highly correlated with biases in the liquid water path adjustment. Temporal aggregation biases are shown to offset spatial aggregation biases. Both spatial and temporal biases have significant implications for observationally based assessments of aerosol indirect effects and our inferences of underlying aerosol–cloud–radiation effects.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Evaluating Retrieval Algorithm Climate Stability: Estimating 3D Optical Thickness Bias Distributions by Cloud Type

Detecting climate trends on large spatiotemporal scales requires accurate, stable measurements and stable retrieval algorithms. We strive to estimate how time-variant retrieval algorithm biases may impact trend detection. Here we focus on the 3D cloud optical thickness (τc) bias, which is among the largest in passive cloud retrieval algorithms. If this bias is time dependent, a possibility with potential decadal changes in cloud morphology, it may obscure genuine trends in τc. Although previous studies have evaluated the cloud- and sun-view geometry-dependent 3D τc bias on small spatial scales, before our current study none have evaluated the stability of this well-known bias on climate-relevant large spatiotemporal scales. These studies must estimate large scale distributions of the 3D τc bias by cloud type and estimate how cloud type amount may change between two climate states. We employ a novel approach to estimate large scale distributions of 3D τc using a proxy of the bias that quantifies the departure of clouds from satisfying the 1D radiative transfer assumption used in passive τc retrievals. This existing globally-distributed proxy is an angular consistency metric that was developed using fused Moderate-Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR) measurements. Calculating the 3D τc bias and the proxy, for known cloud fields enables us to establish statistical relationships between these two quantities, which can be used to calculate large-scale distributions of the 3D τc bias. This approach limits the number of 3D radiative transfer simulations required to only those needed to estimate a statistical relationship between the 3D τc bias for known cloud fields and a proxy of the bias. It is likely that future studies will be needed to evaluate retrieval algorithm bias stability for other geophysical variables as the community develops climate data records from satellite observations and their retrievals. This must be done in addition to monitoring and correcting measurement errors and uncertainties and understanding their impact on retrieved essential climate variables.

Yolanda Shea↗