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At least 73 records · Page 4

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NASA NTRSDate not supplied

Airborne Remote Sensing Measurements of Particles in Different Parts of the World

Around the globe, atmospheric aerosols pose a significant yet uncertain impact on the Earth's climate, environmental air quality and human health. Suborbital sunphotometer measurements of Aerosol Optical Depth (AOD) (i.e., the amount of direct-beam solar light attenuated by aerosols within the atmospheric column) are used to validate satellite aerosol retrievals and are valuable when studying aerosol-solar radiation and/ or aerosol-cloud interactions. Since 1985, NASA Ames Research Center (ARC) has been using airborne sun-tracking sunphotometers to measure atmospheric constituents such as aerosols, clouds, and gases. The NASA Ames Airborne Tracking Sunphotometer (AATS-6) and then (AATS-14) operate by tracking the sun and measuring the transmission of solar radiation in 6 to 14 discrete spectral channels (Russell et al., 1986). The AATS systems were followed by the more advanced hyperspectral 4STAR instrument (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research; Dunagan et al., 2013), which combines the airborne sun-tracking capability of the AATS-14 with a sky scanning feature similar to the ground-based Aerosol Robotic Network Sun/sky photometers (AERONET). We present a comprehensive analysis of ~30 airborne campaigns undertaken by NASA and DoE based on a new database of airborne sunphotometer measurements. Using this database, we present a clear picture of airborne AODs over the range of historical campaigns and the aerosol sizes per region, especially over the ocean where AERONET data is limited. With this database, we can investigate systematic differences in AOD such as those observed over different land and ocean surfaces in specific geographical regions. Finally, we will briefly show comparisons with satellite aerosol retrievals from MODIS and/or VIIRS. Russell, P. B., T. Matsumoto, V. J. Banta, J. M. Livingston, C. Mina, D. S. Colburn, and R. F. Pueschel (1986), Measurements with an airborne, autotracking, external-head sunphotometer, Preprint Volume, Sixth Conference on Atmospheric Radiation, May 13–16, 1986, Amer. Meteor.Soc., Boston, MA, 55–58. Dunagan, Stephen E., et al. "Spectrometer for sky-scanning sun-tracking atmospheric research (4STAR): Instrument technology." Remote Sensing 5.8 (2013): 3872-3895

Brendan Stover Cornelison↗

NASA NTRS1975

CARETS: A prototype regional environmental information system. Volume 9: Shore zone land use and land cover; Central Atlantic Regional Ecological Test Site

Anderson's 1972 United States Geological Survey classification in modified form was applied to the barrier-island coastline within the CARETS region. High-altitude, color-infrared photography of December, 1972, and January, 1973, served as the primary data base in this study. The CARETS shore zone studied was divided into six distinct geographical regions; area percentages for each class in the modified Anderson classification are presented. Similarities and differences between regions are discussed within the framework of man's modification of these landscapes. The results of this study are presented as a series of 19 maps of land-use categories. Recommendations are made for a remote-sensing system for monitoring the CARETS shore zone within the context of the dynamics of the landscapes studied.

Coastal ecosystems↗

NASA NTRS1988

Verification of regional climates of GISS GCM. Part 1: Winter

Verification is made of the synoptic fields, sea level pressure, precipitation rate, 200 mb zonal wind and the surface resultant wind, generated by two versions of the GISS climate model. The models differ regarding the horizontal resolution of the computational grids and the specification of the sea surface temperatures. Maps of the regional distributions of seasonal variations of the model fields are shown alongside maps showing the observed distributions. Comparisons of the model results with observations are discussed, and also summarized in tables according to geographic regions.

Druyan, Leonard M.↗

NASA NTRS1989

Verification of regional climates of GISS GCM. Part 2: Summer

Verification is made of the synoptic fields, sea-level pressure, precipitation rate, 200mb zonal wind and the surface resultant wind generated by two versions of the Goddard Institute for Space Studies (GISS) climate model. The models differ regarding the horizontal resolution of the computation grids and the specification of the sea-surface temperatures. Maps of the regional distributions of seasonal means of the model fields are shown alongside maps that show the observed distributions. Comparisons of the model results with observations are discussed and also summarized in tables according to geographic region.

Druyan, Leonard M.↗

NASA NTRSDate not supplied

A Comparative Study of Contrail Frequency Indices and GOES-16 Contrail Data Set

Contrail formations have been shown to contribute to the greenhouse effect: they are practically transparent to incoming solar radiation and do little to reflect heat away from Earth but are highly effective at trapping heat within Earth’s atmosphere. To understand the impact contrails have on climate change, contrail frequency indices (CFIs) can be used as a method to quantify aircraft-induced persistent contrails. These indices are capable of tracking long-term contrail formation and identify regions of airspace with the highest contrail formation rates. In this aper, an algorithm is proposed which is capable of using NASA Sherlock and Global Forecast System (GFS) datasets and computing CFIs over large geographic regions and long temporal intervals using NASA Ames’ High-End Computing Capability (HECC) supercomputing system. CFIs are computed using nowcast weather data and previously flown flight tracks. This paper calculated the CFIs of all twenty Air Route Traffic Control Centers in the National Airspace System on October 28th, 2019 and compared the distribution of non-zero CFIs with observed contrail data collected from GOES-16 Satellite data in order to assess the accuracy of the CFI system as a contrail prediction model. It was ultimately determined that the computed CFIs were broadly distributed in the same way as the GOES-16 contrail data and that the individual CFIs computed at the latitude/longitude points at which GOES-16 contrail masks were available had high precision and recall (at 0.75 and 0.86 respectively). While these validation results bode well for the accuracy of the CFI method, the number of provided GOES-16 masks was quite small. Future work should aim to increase the size of the GOES-16 dataset in order to perform a more comprehensive comparison between these two datasets.

Contrails↗

NASA NTRSDate not supplied

A Comparative Study of Contrail Frequency Indices and GOES-16 Contrail Data Set

Contrails↗

DOE OSTI2020

Evaluation of historical CMIP6 model simulations of extreme precipitation over contiguous US regions

Simulated historical precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices. The model indices are evaluated against corresponding indices from the CPC unified gauge-based analyses of precipitation over seven geographical regions across the contiguous US (CONUS). The regions assessed match those in recent US National Climate Assessment Reports. To estimate observational uncertainty, precipitation indices for three other observational datasets (HadEx2, Livneh and PRISM) are evaluated against the CPC analyses. Both the moderate and extreme mean precipitation intensities are overestimated over the western CONUS and underestimated in the areas of the Central Great Plains (CGP) in most CMIP6 models tested. Most CMIP6 models overestimate the mean and variability of wet spell durations and underestimate the mean and variability of dry spell durations across the CONUS. Biases in interannual variability of most of the indices have similar patterns to those in corresponding mean biases. The median and interquartile model spreads in CMIP6 model biases are clearly smaller than those in CMIP5 model biases for wet spell durations. Multimodel medians of CMIP6 (CMIP6-MMM) and CMIP5 (CMIP5-MMM) have similar biases in climatology and variability but biases tend to be smaller in CMIP6-MMM. Depending on the index, extreme precipitation is slightly better in parts of the eastern half of the CONUS in CMIP6-MMM, otherwise, the biases in climatology and variability are similar to CMIP5-MMM. CMIP6-MMM performs better than individual models and even observational datasets in some cases. Differences between observational datasets for most indices are comparable to the CMIP6 interquartile model spread. The better-performing observational and model datasets are different in different parts of the CONUS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1977

Satellite-based interference analyzer

System identifies terrestrial sources of radiofrequency interference and measures their frequency spectra and amplitudes. Designed to protect satellite communication networks, system measures entire noise spectrum over selected frequency band and can raster-scan geographical region to locate noise sources. Once interference is analyzed, realistic interference protection ratios are determined and mathematical models for predicting ratio-frequency noise spectra are established. This enhances signal-detection and locates optimum geographical positions and frequency bands for communication equipment.

Varice, H.↗

NASA NTRS1988

Observation of sea-ice dynamics using synthetic aperture radar images: Automated analysis

The European Space Agency's ERS-1 satellite, as well as others planned to follow, is expected to carry synthetic-aperture radars (SARs) over the polar regions beginning in 1989. A key component in utilization of these SAR data is an automated scheme for extracting the sea-ice velocity field from a time sequence of SAR images of the same geographical region. Two techniques for automated sea-ice tracking, image pyramid area correlation (hierarchical correlation) and feature tracking, are described. Each technique is applied to a pair of Seasat SAR sea-ice images. The results compare well with each other and with manually tracked estimates of the ice velocity. The advantages and disadvantages of these automated methods are pointed out. Using these ice velocity field estimates it is possible to construct one sea-ice image from the other member of the pair. Comparing the reconstructed image with the observed image, errors in the estimated velocity field can be recognized and a useful probable error display created automatically to accompany ice velocity estimates. It is suggested that this error display may be useful in segmenting the sea ice observed into regions that move as rigid plates of significant ice velocity shear and distortion.

Vesecky, John F.↗

NASA NTRS2017

A Tree-Ring Chronology and Paleoclimate Record for the Younger Dryas-Early Holocene Transition from Northeastern North America

Spruce and tamarack logs dating from the Younger Dryas and Early Holocene (YDEH; approx. 12.9 - 11.3k cal a BP) were found at Bell Creek in the Lake Ontario lowlands of the Great Lakes region, North America. A 211-year tree-ring chronology dates to approx. 11 755 -11 545 cal a BP, across the YDEH transition. A 23-year period of higher year-to-year ring-width variability dates to around 11 650 cal a BP, infers strong regional climatic perturbations and may represent the end of the YD. Tamarack and spruce were dominant species throughout the YD - EH interval at the site, indicating that boreal conditions persisted into the EH, in contrast to geographical regions immediately south and east of the lowlands, but consistent with the Great Lakes interior lowlands. This infers that Bell Creek was at the eastern boundary of a boreal ecotone, perhaps a result of its lower elevation and the non-analog dynamics of the Laurentide Ice Sheet. This finding suggests that the ecotone boundary extended farther east during the YD - EH transition than previously thought.

dendroclimatology;northeastern North America;spruc↗

NASA NTRS2013

Comparison of Precision of Biomass Estimates in Regional Field Sample Surveys and Airborne LiDAR-Assisted Surveys in Hedmark County, Norway

Airborne scanning LiDAR (Light Detection and Ranging) has emerged as a promising tool to provide auxiliary data for sample surveys aiming at estimation of above-ground tree biomass (AGB), with potential applications in REDD forest monitoring. For larger geographical regions such as counties, states or nations, it is not feasible to collect airborne LiDAR data continuously ("wall-to-wall") over the entire area of interest. Two-stage cluster survey designs have therefore been demonstrated by which LiDAR data are collected along selected individual flight-lines treated as clusters and with ground plots sampled along these LiDAR swaths. Recently, analytical AGB estimators and associated variance estimators that quantify the sampling variability have been proposed. Empirical studies employing these estimators have shown a seemingly equal or even larger uncertainty of the AGB estimates obtained with extensive use of LiDAR data to support the estimation as compared to pure field-based estimates employing estimators appropriate under simple random sampling (SRS). However, comparison of uncertainty estimates under SRS and sophisticated two-stage designs is complicated by large differences in the designs and assumptions. In this study, probability-based principles to estimation and inference were followed. We assumed designs of a field sample and a LiDAR-assisted survey of Hedmark County (HC) (27,390 km2), Norway, considered to be more comparable than those assumed in previous studies. The field sample consisted of 659 systematically distributed National Forest Inventory (NFI) plots and the airborne scanning LiDAR data were collected along 53 parallel flight-lines flown over the NFI plots. We compared AGB estimates based on the field survey only assuming SRS against corresponding estimates assuming two-phase (double) sampling with LiDAR and employing model-assisted estimators. We also compared AGB estimates based on the field survey only assuming two-stage sampling (the NFI plots being grouped in clusters) against corresponding estimates assuming two-stage sampling with the LiDAR and employing model-assisted estimators. For each of the two comparisons, the standard errors of the AGB estimates were consistently lower for the LiDAR-assisted designs. The overall reduction of the standard errors in the LiDAR-assisted estimation was around 40-60% compared to the pure field survey. We conclude that the previously proposed two-stage model-assisted estimators are inappropriate for surveys with unequal lengths of the LiDAR flight-lines and new estimators are needed. Some options for design of LiDAR-assisted sample surveys under REDD are also discussed, which capitalize on the flexibility offered when the field survey is designed as an integrated part of the overall survey design as opposed to previous LiDAR-assisted sample surveys in the boreal and temperate zones which have been restricted by the current design of an existing NFI.

Comparison↗

NASA NTRS1995

Comparison of simulated cloud cover with satellite obsrvations over the Western United States

Satellite imagery datasets and regional climate model results are intercompared for evaluation of model accuracy in the simulation of cloud cover. Both monthly average individual simulation times are analyzed. To provide a consistent comparison, satellite data are first mapped into the model's geographic projection, grid domain, and resolution. It is found that September 1988 monthly average cloud fraction results from the modeled simulations correspond to observations, in both spatial pattern and magnitude, with bias less than +/- 20% cloud fraction over the entire inland West. Agreement in the pattern of cloud fraction also is evident for monthly average cloud fraction in July, but there is no negative bias of 10%-30% cloud fraction in the model diagnosis of cloud cover. Correlations between the spatial distributions of model-derived and observed cloud fractions are found to exceed 0.80 for certain geographic regions of the West, and these correlations are largest over mountainous areas during summer. Case studies of a series of daily cloud cover demonstrate the ability of the model to simulate the effects of frontal passage on cloud distribution. The ability of the RegCM1 to simulate daily cloud fraction and diurnal cloud evolution is somewhat weak for the summer convective season. It is anticipated that a more recent version of the regional climate model may improve the simulation of summer season cloud cover, through changes in cloud parameterization and improvements in model resolution.

Wetzel, Melanie A.↗

NASA NTRS1975

SEASAT economic assessment. Volume 3: Offshore oil and natural gas industry case study and generalization

The economic benefits of improved ocean condition, weather and ice forecasts by SEASAT satellites to the exploration, development and production of oil and natural gas in the offshore regions are considered. The results of case studies which investigate the effects of forecast accuracy on offshore operations in the North Sea, the Celtic Sea, and the Gulf of Mexico are reported. A methodology for generalizing the results to other geographic regions of offshore oil and natural gas exploration and development is described.

Source record↗

DOE OSTI2024

Empirical Validation of UBEM: An Assessment of Bias in Urban Building Energy Modeling for Chicago

Residential and commercial buildings currently account for 30% of total global final energy consumption. Urban-scale building energy modeling (UBEM) can enable scalable investments and unlock building improvements by quantifying energy, demand, emissions, and cost reductions of specific measures or packages for building-specific technologies in large geographic regions. While the sophistication of UBEM data sources and technologies have increased dramatically in the past decade, there remains a knowledge gap for empirical validation and sources of bias between building-specific energy models and measured data at varying geographic scales.As UBEM continues to develop, systemic analysis of accuracy, bias, and limitations of the resulting models is necessary to inform best practices and move toward standardization. These are characterized for the Automatic Building Energy Modeling (AutoBEM) software suite with an initial case study involving metered electricity consumption data from 247,188 buildings in Chicago, Illinois, USA - averaged across years 2019-2021 - compared to the following datasets: (1) the AutoBEM-generated nation-scale Model America version 2 (MAv2) data for 596,064 buildings, (2) tax assessor data for 579,829 buildings, (3) tax assessor data filled with MAv2, and (4) 102 representative dynamic archetypes. The accuracy is reported for every building type and vintage combination, along with multiple sources of bias for unique building descriptors. The AutoBEM simulation workflow produced energy consumption estimates that closely match aggregated metered electricity consumption data for different types of buildings constructed during various time periods at the city scale - with initial normalized mean bias error of 10.9%, and 1.1% after removing outliers. Contribution of statistically significant factors including building type, land use, age, and size to variance in UBEM bias is quantified.

Garg, Ankur↗

NASA NTRS2011

Influence of Convection and Aerosol Pollution on Ice Cloud Particle Effective Radius

Satellite observations show that ice cloud effective radius (r(sub e)) increases with ice water content (IWC) but decreases with aerosol optical thickness (AOT). Using least-squares fitting to the observed data, we obtain an analytical formula to describe the variations of r(sub e) with IWC and AOT for several regions with distinct characteristics of r(sub e) -IWC-AOT relationships. As IWC directly relates to convective strength and AOT represents aerosol loading, our empirical formula provides a means to quantify the relative roles of dynamics and aerosols in controlling r(sub e) in different geographical regions, and to establish a framework for parameterization of aerosol effects on r(sub e) in climate models.

Jiang, J. H.↗

DOE OSTI2025

CareWELL: Multimodal Region Representation Learning with Spatial Contexts for Urban Health

Rapid urbanization affects living environments by intensifying exposure to air pollution, heat, noise, and urban dynamics, which together contribute to uneven health outcomes across neighborhoods. For instance, cardiovascular, respiratory, and mental health conditions are each influenced by distinct exposures such as air pollution, extreme temperatures, or limited access to green space. These heterogeneous patterns require understanding the characteristics of geographic regions in order to explain why urban health risks vary across urban areas. Recent work in self-supervised region representation learning provides a promising way to model such characteristics from multimodal geospatial data. However, existing methods face two major limitations: (i) they often depend on non-public datasets, limiting reproducibility and applicability, and (ii) their generic pretraining objectives overlook health-relevant determinants, including temporal variability in environmental exposures and inequalities in social conditions. To address these gaps, we propose Context-Aware Region rEpresentation with Weather, Environment, and Location Learning (CareWELL). CareWELL leverages large language models to encode seasonal variability in weather, employs contrastive learning to align geo-coordinate and weather representations, and introduces a context-aware objective that integrates socio-demographic factors while preserving spatial correlations. We evaluate CareWELL by predicting six urban health outcomes in Manhattan, New York City, and demonstrate that CareWELL consistently outperforms state-of-the-art baselines as well as a traditional spatial computing method. These results suggest the importance of context-aware pretraining objectives for learning health-relevant region representations.

Namgung, Min [ORNL]↗

NASA NTRS2006

Five Millennium Canon of Solar Eclipses: -1999 to +3000 (2000 BCE to 3000 CE)

During 5,000-year period from -1999 to +3000 (2000BCE to 3000CE), Earth will experience 11,898 eclipses of the Sun. The statistical distribution of eclipse types for this interval is as follows: 4,200 partial eclipses, 3956 annular eclipses, 3173 total eclipses,and 569 hybrid eclipses. Detailed global maps for each of the 11,898 eclipses delineate the geographic regions of visibility for both the penumbral (partial) and umbral or antumbral (total, annular, or hybrid) phases of every event. Modern political borders are plotted to assist in the determination of eclipse visibility. The uncertainty in Earth's rotational period expressed in the parameter (delta)T and its impact on the geographic visibility of eclipses in the past and future is discussed.

Espenak, Fred↗

DOE OSTI2026

Optimizing HAARP Beam Pattern for Generation of Strong F‐Region Field‐Aligned Irregularities

Field-aligned irregularities (FAIs) are the signatures of plasma turbulence and convection in the mid- and high-latitude F-region ionosphere, and also provide coherent backscatter targets for HF radars. To serve irregularity generation and characterization studies, we conducted experiments at the High-frequency Active Auroral Research Program (HAARP) in August 2023 with the goal of identifying the optimal HAARP beam pattern for reliable generation of intense FAIs over a large geographic region. The HAARP beam patterns we tested were the commonly-used narrow beam, also known as L0, as well as the wider L1 and L2 “twisted” beam patterns. The size and intensity of the FAI region generated by each HAARP beam pattern was quantified using the Kodiak Island Super Dual Auroral Radar Network (SuperDARN) radar. Stimulated electromagnetic emissions (SEE) from heater wave-FAI scattering were also recorded using a receiver located near HAARP. The L1 beam pattern was found to produce the strongest SuperDARN backscatter over the largest region. Although the heater frequency was intended to be tuned a few hundred kHz below the F-region critical frequency (foF2) during each experiment, difficulty in estimating foF2 during the campaign likely resulted in HAARP heating at significantly different frequency ranges around foF2 during each experiment. Although this additional free parameter complicated data analysis for this study, the SuperDARN and SEE measurements have led to further inquiry into the role heater frequency plays in the artificial generation of FAIs.

58 GEOSCIENCES↗