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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Calculation of clear-sky outgoing longwave radiation using ECMWF gridded fields and ISCCP C1 cloud data

Clear-Sky OLR (outgoing longwave radiation) has a variety of definitions. Clear-sky OLR can be measured from satellites by examining the data to detect clouds, eliminating those observations that are contaminated with clouds, and averaging the remaining data. The disadvantage of this method is that satellites do not provide time continuous coverage, requiring many observations and careful analysis to remove sampling biases. Alternatively, clear-sky OLR can be found continuously using a radiative flux code, combined with analyzed fields of temperature, humidity and clouds, either from assimilated observations or a GCM simulation. The disadvantage is that the cloud field is typically very low resolution, on the order of 200 km, so they lack important small scale cloud detail. Even if fractional clouds data is available, it is not obvious how GCM and ERBE results should be compared. The purpose of this study is to assess clear-sky OLR calculated from these methods using available data. Cloud data from ISCCP provides the fractional cloud coverage data necessary to test these methods against calculations based on observed data. Clear-sky OLR is calculated using the ECMWF/TOGA archive. Monthly averages are made in the manner of the several methods listed above and compared to each other and to ERBE.

Ramsey, Perry G.↗
NASA NTRS2023

Do Citizen Science Intense Observation Periods Increase Data Usability? A Deep Dive of the NASA GLOBE Clouds Data Set With Satellite Comparisons

The Global Learning and Observations to Benefit the Environment (GLOBE) citizen science program has recently conducted a series of month-long intensive observation periods (IOPs), asking the public to submit daily reports on cloud and sky conditions from all regions of Earth. This provides a wealth of crowdsourced observations from the ground, which complements other conventional scientific cloud data. In addition, the GLOBE reports are matched in space and time with geostationary and low Earth orbit satellites, which allows for a straightforward comparison of cloud properties, and minimizes the biases associated with mismatched sampling between participants and satellites. The matched GLOBE dataset is used to calculate the mean observed cloud cover by atmospheric level both worldwide and by region. The overall magnitudes of cloud cover between the GLOBE participants and the matched satellites agree within 10%, which is notable given the distinctly different natures of the data sources. The mean vertical cloud profiles show GLOBE reporting more low-level clouds and fewer high-level clouds than satellites. The low cloud disagreement is likely related to satellites missing low clouds when high clouds block their view. Conversely, the high cloud disagreement is related primarily to cloud opacity, as satellites may miss some optically thin clouds. Monte Carlo testing shows the results to be robust, and the tripled amount of IOP data reduces uncertainty by half. These findings also highlight ways in which citizen science IOP data may be used to support scientific research while accounting for their unique properties. Plain Language Summary: Citizen science is becoming an increasingly prominent aspect of scientific research, and so it important to study how citizen science data can be used effectively. For example, The GLOBE Program has recently conducted a series of special data-collecting events, or “challenges”, which gathered large numbers of reports on cloud and sky conditions. Because NASA GLOBE Clouds matches the participant reports with cloud observations from satellites, we can use these data to get a combined view of clouds from above and below. When looking at the average cloud cover for different atmospheric levels across Earth, we find that the GLOBE participants and the satellites agree quite closely. This is a surprising and fascinating find, given how different in nature volunteer ground reports are to satellite measurements. However, there are some small but notable disagreements between GLOBE participants and satellites about the distribution of cloud cover at different levels. In addition, by testing the data for uncertainty, we show that the results from the GLOBE data are reliable, and that more public participation improves the reliability. So, by carefully designing the analysis methodology, and by testing for the uncertainty of the data, citizen science can make a meaningful contribution to scientific research.

J. Brant Dodson↗
NASA NTRS1984

A modeling analysis program for the JPL Table Mountain Io sodium cloud data

The abundant Io sodium cloud data obtained at JPL Table Mountain was reviewed. Images of the sodium cloud important to this modeling analysis program are contained in the 1976-1979 data set and the 1981 data set. A preliminary assessment of the 263 images in the 1981 data set for Region B/C was initiated. The spatial morphology of some of these images revealed the presence of the forward sodium cloud (Region B) and the directional features (Region C) as expected. Plans for the second quarter to initiate preliminary modeling analysis and to define further data processing are discussed.

Smyth, W. H.↗
DOE OSTI2021

Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O 2 –O 2 , MODIS, and Suomi-NPP VIIRS

Accurate knowledge of cloud properties is essential to the measurement of atmospheric composition from space. In this work we assess the quality of the cloud data from three Copernicus Sentinel-5 Precursor (S5P) TROPOMI cloud products: (i) S5P OCRA/ROCINN_CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks;Clouds-As-Layers), (ii) S5P OCRA/ROCINN_CRB (Clouds-as-Reflecting Boundaries), and (iii) S5P FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands – Sentinel). Target properties of this work are cloud-top height and cloud optical thickness (OCRA/ROCINN_CAL), cloud height (OCRA/ROCINN_CRB and FRESCO-S), and radiometric cloud fraction (all three algorithms). The analysis combines (i) the examination of cloud maps for artificial geographical patterns, (ii) the comparison to other satellite cloud data (MODIS, NPP-VIIRS, and OMI O2–O2), and (iii) ground-based validation with respect to correlative observations (30 April 2018 to 27 February 2020) from the Cloudnet network of ceilometers, lidars, and radars. Zonal mean latitudinal variation of S5P cloud properties is similar to that of other satellite data. S5P OCRA/ROCINN_CAL agrees well with NPP VIIRS cloud-top height and cloud optical thickness and with Cloudnet cloud-top height, especially for the low (mostly liquid) clouds. For the high clouds, S5P OCRA/ROCINN_CAL cloud-top height is below the cloud-top height of VIIRS and of Cloudnet, while its cloud optical thickness is higher than that of VIIRS. S5P OCRA/ROCINN_CRB and S5P FRESCO cloud height are well below the Cloudnet cloud mean height for the low clouds but match on average better with the Cloudnet cloud mean height for the higher clouds. As opposed to S5P OCRA/ROCINN_CRB and S5P FRESCO, S5P OCRA/ROCINN_CAL is well able to match the lowest CTH mode of the Cloudnet observations. Peculiar geographical patterns are identified in the cloud products and will be mitigated in future releases of the cloud data products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Automatic Segmentation of Building Envelope Point Cloud Data Using Machine Learning

About 50% of buildings in the US were constructed before energy codes were introduced. Modular overclad panel retrofits, in which a new envelope is constructed over the existing building, are a promising solution given that it minimizes occupant disruption and shortens construction time at the jobsite. Current state-of-the-art retrofit panel layout and dimensioning consists of three steps: 1) 3D point cloud data generation of the building envelope using commonly available surveying equipment, 2) manual segmentation of 3D point cloud data by a trained professional to identify and dimension window openings, door openings, and other architectural features, and 3) modular panel layout optimization and dimensioning by an architect or engineer. Among these steps, the second one remains the most difficult and costly because it is very labor-intensive. We propose a methodology to automatically label 3D point cloud data to reduce the time and expense spent in manual segmentation. Machine learning methods were employed to classify the point cloud data into distinct groups, each of which corresponds to different features of the building envelope. After classification, a segmentation algorithm was developed to perform boundary detection and separate the components of the façade. Finally, the algorithm returns the relative positions and dimensions of the features in the building envelope. The measurements obtained with the proposed automated method were compared against the actual dimensions to determine the overall algorithm accuracy. The proposed algorithm can then be used to reduce manual efforts for 3D point cloud labeling before modular panel layout optimization is performed.

Maldonado Puente, Bryan↗
DOE OSTI2021

LiDAR-Based Point Cloud Data Processing

The presentation includes the procedures of utilizing LiDAR-based point cloud data in WebGL applications, such as capturing data, processing data, importing point cloud into Unity software, and improving application performance. It uses IRC BLD 603 Lab B-12 as an example. It'll be presented at Viz n Learn workshop which is a collaboration series between INL's Applied Visualization Laboratory (AVL) and University of Wyoming (UW)'s Shell 3D Visualization center. Each session will highlight 2 projects from each institution monthly, followed by discussion on areas of collaborative interest and action.

3D↗
DOE OSTI2026

From Points to Planes: A Workflow for Converting Three‐Dimensional Point Cloud Data Into Discrete Fracture Network Flow and Transport Models

We present the Point cLoud Algorithm for NEtwork Extraction of Discrete Fracture Networks (PLANE-DFN), a point cloud–based algorithm for automatic fracture network extraction designed to support discrete fracture network (DFN) modeling workflows. PLANE-DFN segments three-dimensional fracture planes from raw point cloud data using RANdom SAmple Consensus coupled with statistical outlier removal and density-based clustering to isolate individual fracture features. Each candidate plane is constrained against site-specific structural constraints based on strike and dip. After segmentation, each fracture is converted into a 2-D convex polygon suitable for meshing and simulation. The PLANE-DFN algorithm is validated by comparing geometric and flow and transport data against data from dfnWorks simulations with ensembles of plane-fit networks. We find that the flow and transport in plane-fit networks are comparable to dfnWorks-generated networks when realistic network geometry is maintained. The PLANE-DFN algorithm provides an automated and streamlined workflow to transform point clouds of data into DFN network geometry.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

LiDAR Point Cloud Data from the 2018 NGEE Arctic UAS Campaign at the Teller 47 Field Site, Seward Peninsula, Alaska

Airborne remote sensing data collected from Los Alamos National Laboratory’s (LANL) heavy-lift unoccupied aerial system (UAS) hexacopter platform operated by NGEE Arctic scientists from the EES-14 group at Los Alamos National Laboratory. These data were collected in July 2018 at a field site near mile marker 47 along the Teller Road between Nome, Alaska and Teller, Alaska. A DJI Matrice 600 Pro Airframe and Routescene UAV LiDAR System was used to collect LiDAR data. The LiDAR data has undergone basic post-processing using Routescene LidarViewer Pro software to create point cloud data (.laz files). This data package contains point clouds (.laz), processing metadata files (json.lvp), and post-processed kinematic files (.csv). Ancillary aircraft data, flight mission parameters, weather conditions, and lidar data and imagery can be found in NGA281 (https://doi.org/10.5440/1671794).The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

LiDAR Point Cloud Data from the 2018 NGEE Arctic UAS Campaign at the Kougarok 64 Field Site, Seward Peninsula, Alaska

Airborne remote sensing data collected from Los Alamos National Laboratory's (LANL) heavy-lift unoccupied aerial system (UAS) hexacopter platform operated by NGEE Arctic scientists from the EES-14 group at Los Alamos National Laboratory. These data were collected in July 2018 at a field site near mile marker 64 along the Kougarok road (Nome-Taylor Highway) between Nome, Alaska and Taylor, Alaska. A DJI Matrice 600 Pro Airframe and Routescene UAV LiDARSystem was used to collect LiDAR data. The LiDAR data has undergone basic post-processing using Routescene LidarViewer Pro software to create point cloud data (.laz files). This data package contains point clouds (.laz), processing metadata files (json.lvp), and post-processed kinematic files (.csv). Ancillary aircraft data, flight mission parameters, weather conditions, raw LiDAR data, and RGB imagery can be found in NGA298.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

A Synopsis of the 8+ Year CALIOP Polar Stratospheric Cloud Data Record

Polar stratospheric clouds (PSCs) play a crucial role in the springtime chemical depletion of ozone at high latitudes. PSC particles (primarily supercooled ternary solution, or STS droplets) provide sites for heterogeneous chemical reactions that transform stable chlorine and bromine reservoir species into highly reactive ozone-destructive forms. Furthermore, large nitric acid trihydrate (NAT) PSC particles can irreversibly redistribute odd nitrogen through gravitational sedimentation (a process commonly known as denitrification), which prolongs the ozone depletion process by slowing the reformation of the stable chlorine reservoirs. Spaceborne observations from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar on the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite are providing a rich new dataset for studying PSCs. CALIPSO is an excellent platform for studying polar processes with CALIOP acquiring, on average, over 300,000 backscatter profiles daily at latitudes between 55o and 82o in both hemispheres. PSCs are detected in the CALIOP backscatter profiles using a successive horizontal averaging scheme that enables detection of strongly scattering PSCs (e.g., ice) at the finest possible spatial resolution (5 km), while enhancing the detection of very tenuous PSCs (e.g., low number density NAT) at larger spatial scales (up to 135 km). CALIOP PSCs are separated into composition classes (STS; liquid/NAT mixtures; and ice) based on the ensemble 532-nm scattering ratio (the ratio of total-to-molecular backscatter) and 532-nm particulate depolarization ratio (which is sensitive to the presence of non-spherical, i.e. NAT and ice particles). In this paper, we will provide an overview of the CALIOP PSC detection and composition classification algorithm and then examine the vertical and spatial distribution of PSCs in the Arctic and Antarctic on vortex-wide scales for entire PSC seasons over the more than eight-year data record.

Michael C Pitts↗
NASA NTRS1985

A modeling analysis program for the JPL Table Mountain Io sodium cloud data

The necessary personnel software base was established to accomplish the data processing requirements for this project. Conversion of software required in reformatting the raw Io sodium cloud data was completed and data processing activities were initiated. Slit spectra data acquired by the JPL Table Mountain Io sodium cloud observing program were reviewed and their scientific value assessed. The lifetime description for sodium atoms in the Io sodium cloud model was improved by incorporating the most recently available temperatures and densities for ions and electrons in the plasma torus.

Smyth, W. H.↗
NASA NTRS1988

A modeling analysis program for the JPL table mountain Io sodium cloud data

Research in the third and final year of this project is divided into three main areas: (1) completion of data processing and calibration for 34 of the 1981 Region B/C images, selected from the massive JPL sodium cloud data set; (2) identification and examination of the basic features and observed changes in the morphological characteristics of the sodium cloud images; and (3) successful physical interpretation of these basic features and observed changes using the highly developed numerical sodium cloud model at AER. The modeling analysis has led to a number of definite conclusions regarding the local structure of Io's atmosphere, the gas escape mechanism at Io, and the presence of an east-west electric field and a System III longitudinal asymmetry in the plasma torus. Large scale stability, as well as some smaller scale time variability for both the sodium cloud and the structure of the plasma torus over a several year time period are also discussed.

Smyth, William H.↗
NASA NTRS1986

Intercomparison between Nimbus-7 cirrus cloud data and ERB measurements

Nimbus-7 and Earth Radiation Budget (ERB) data were compared with the quantity of high, thin cirrus clouds over a 6 yr period. Three separate data analysis techniques are described which were used to derive cloud cover and total upwelling radiance values from the satellite data. Zonal average cirrus clouds amounts are compared with surface observations and SAGE satellite data, and comparisons are made between the cloud amount estimates made with ERB data and data from other Nimbus-7 instruments. All Nimbus-7 instrumental data indicated cloud amounts and frequency of occurrence patterns which were commensurate with surface observations, except in high latitude zones.

Yeh, H. Y. M.↗
NASA NTRS2019

Big Data, Cloud, and Earth Science

Given the reality of a Big Data future, we need to reevaluate our ability to quickly process immense amounts of data while maintaining our responsibility of stewarding our archives. At National Aeronautics and Space Administration (NASA), our Science Missions Directorate and Earth Science Data Systems have been exploring the commercial cloud as a potential mechanism for data ingest, archive, and distribution since 2015. This talk will discuss our timeline and strategies being employed and will discuss the process of introducing commercial cloud entities into a largely on-premise hardware-reliant approach to ingest, archive, and distribution. We will address topics such as vendor lock-in, varying compute and costing strategies, and end-user adoption. Please join us for an open and authentic conversation about the opportunities we are embracing and the risks we are undertaking as a part of this evolution.

Informatics↗
NASA NTRSDate not supplied

NASA Cloud Data Access

Explore the source record for details and available documents.

Cloud Computing↗
DOE OSTI2024

DEM, DSM, and Cleaned LiDAR Point Cloud Data from the NGEE Arctic UAS Campaigns at the Teller 27 Field Site from 2017 and 2018, Seward Peninsula, Alaska

A Digital Elevation Model (DEM) and Digital Surface Model (DSM) were derived from airborne Light Detection and Ranging (LiDAR) data collected from Los Alamos National Laboratory's (LANL) heavy-lift unoccupied aerial system (UAS) quadcopter and hexacopter platforms operated by Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) scientists from the EES-14 group at LANL. These data were collected in August 2017 and July 2018 at the NGEE Arctic field site near mile marker 27 of the Bob Blodgett Nome-Teller Memorial Highway between Nome, Alaska and Teller, Alaska. A Vulcan Raven X8 Airframe (Mitcheldean, Gloucestershire, UK), DJI Matrice 600 Pro Airframe (Shenzhen, China), and Routescene UAV LiDARSystem (Edinburgh, Scotland, UK) were used to collect LiDAR data. Following pre-processing in Routescene LidarViewer Pro software, the LiDAR point clouds were cleaned and processed using CloudCompare software to separate ground and off-ground points. A high resolution DEM and DSM were then created using ArcGIS Pro software. This data package contains fully cleaned point clouds of ground and off-ground points (.las), a 25 cm DEM (.tif), and a 25 cm DSM (.tif) for the Teller 27 field site. Ancillary aircraft data, flight mission parameters, weather conditions, and raw lidar data and imagery can be found in the L0 datasets for these campaigns: NGA299 (2017) and NGA297 (2018). Minimally processed point clouds and auxiliary files can be found in the L1 dataset: NGA304 (2017 and 2018).The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗