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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
DOE OSTI2025

Infrared Cloud Imager Instrument Intercomparison Report

The Infrared Cloud Imager Instrument Intercomparison was a guest instrument deployment by NWB Sensors to the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) User Facility observatory on the Southern Great Plains (SGP) between May 18 and December 12, 2023. NWB Sensors is a company that has developed a commercially available infrared cloud imager (ICI). The ICI provides radiometrically calibrated, full-sky images of the downwelling infrared radiance in the 7.3-14 µm band. In addition, it provides cloud radiance as the residual between the observed radiance and the modeled cloud-free radiance as well as derived cloud products. The instrument is used in applications that require consistent detection of clouds across day and night. For more information, consult the instrument's webpage. The primary goal of the deployment was to validate the radiometric accuracy of the ICI. The ICI uses a proprietary calibration method to convert the raw data from its infrared camera into downwelling radiance. Unlike similar instruments, the system does not have an onboard blackbody calibration standard. Instead, NWB Sensors characterizes each ICI camera individually in an environmental chamber while looking at a blackbody standard. The resulting (proprietary) calibration is used operationally in the instrument and has been demonstrated to be stable over long periods. To validate the radiometric products from the ICI, an intercomparison between the ICI data products and those from ARM’s atmospheric emitted radiance interferometer (AERI) was made. The AERI is a best-in-class instrument for measuring downwelling infrared radiance (Gero et al. 2025). A weighted integration of the AERI’s spectral radiances across the ICI’s camera response was performed. The resulting radiance (herein called the AERI radiance) was directly compared to the zenith radiance concurrently observed by the ICI. The results of these comparisons are reported in the next section of this report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Search for a Cloud Phase Feedback in the Arctic Climate System

This project was motivated by a hypothesis involving the transition in lower troposphere temperatures across the freezing point of water. Specifically, at temperatures just below to about ten degrees below freezing, Arctic clouds should be in a mixed-phase, with strong influences from secondary ice production (e.g., the Hallett-Mossop process). At temperatures just above freezing, clouds should become entirely deglaciated. We hypothesized that, with multiple years of ARM data, a statistically significant change could be detected in cloud radiative properties and surface radiative fluxes that could be directly attributed to this phase change. Furthermore, in a gradually warming climate, these phase change-related responses in surface radiation would represent a cloud phase feedback as part of Arctic amplification. We designed this research project to coincide with a new ARM Arctic cloud radar product developed by Ed Luke and collaborators at BNL (Luke et al., 2021: PNAS, doi:10.1073/pnas.2021387118) that explicitly contains SIP cloud properties retrieved from radar data. This research project has successfully concluded after analysis of a much larger North Slope of Alaska (NSA) data sample than originally anticipated, and the results are quite different than originally hypothesized.

58 GEOSCIENCES↗
DOE OSTI2021

Pisces VII: discovery of a possible satellite of Messier 33 in the DESI legacy imaging surveys

ABSTRACT We report deep imaging observations with DOLoRes@TNG of an ultra-faint dwarf satellite candidate of the Triangulum galaxy (M33) found by visual inspection of the public imaging data release of the DESI Legacy Imaging Surveys. Pisces VII/Triangulum (Tri) III is found at a projected distance of $72\, {\rm kpc}$ from M33, and using the tip of the red giant branch method, we estimate a distance of $D=1.0^{+0.3}_{-0.2}\, {\rm Mpc}$, meaning the galaxy could either be an isolated ultra-faint or the second known satellite of M33. We estimate an absolute magnitude of MV = −6.1 ± 0.2 if Pisces VII/Tri II is at the distance of M33, or as bright as MV = −6.8 ± 0.2 if the galaxy is isolated. At the isolated distance, it has a physical half-light radius of $r_{\rm h}=131\pm 61\, {\rm pc}$ consistent with similarly faint galaxies around the Milky Way. As the tip of the red giant branch is sparsely populated, constraining a precision distance is not possible, but if Pisces VII/Tri III can be confirmed as a true satellite of M33, it is a significant finding. With only one potential satellite detected around M33 previously (Andromeda XXII/Tri I), it lacks a significant satellite population in stark contrast to the similarly massive Large Magellanic Cloud. The detection of more satellites in the outskirts of M33 could help to better illuminate if this discrepancy between expectation and observations is due to a poor understanding of the galaxy formation process, or if it is due to the low luminosity and surface brightness of the M33 satellite population which has thus far fallen below the detection limits of previous surveys. If it is truly isolated, it would be the faintest known field dwarf detected to date.

Martínez-Delgado, David↗
DOE OSTI2019

pccp.c1

Point Cloud of Cloud Points (PCCP) VAP provides a stereo reconstructed Point Cloud of Cloud Points (PCCP) that correspond to the x,y,z coordinates of cloud features detected from stereo image pairs genrated from a multi-view stereo system deployed to collect exclusive set of data on shallow cumulus clouds overhead.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

pccpa.c1

Point Cloud of Cloud Points (PCCP) VAP provides a stereo reconstructed Point Cloud of Cloud Points (PCCP) that correspond to the x,y,z coordinates of cloud features detected from stereo image pairs genrated from a multi-view stereo system deployed to collect exclusive set of data on shallow cumulus clouds overhead.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

pccpb.c1

Point Cloud of Cloud Points (PCCP) VAP provides a stereo reconstructed Point Cloud of Cloud Points (PCCP) that correspond to the x,y,z coordinates of cloud features detected from stereo image pairs generated from a multi-view stereo system deployed to collect exclusive set of data on shallow cumulus clouds overhead.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The influence of cloud cover on the reliability of satellite-based solar resource data

Satellite-based solar resource data are often developed and validated by using binary cloudiness categories: clear sky or overcast cloudy sky. To investigate the reliability of solar resource data in partially cloudy conditions, we estimate cloud fraction using two distinct algorithms: a physical retrieval model using surface observed global horizontal irradiance (GHI) and direct normal irradiance (DNI) and a temporal average of cloud mask data estimated by the observed DNI. Our analysis reveals a significant presence of scattered clouds, broken clouds, and mismatches between satellite- and surface-based cloud data at 17 surface sites across the contiguous United States, though confidently clear and cloudy conditions collectively account for more than 70 % of the data. Solar radiation is computed using the National Solar Radiation Database (NSRDB) algorithm and validated using surface observations. Here, our findings suggest that, in the presence of scattered clouds, NSRDB data for clear-sky conditions can be subject to significant overestimation. In cloudy-sky conditions classified by satellite data, DNI computed by the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI) can be underestimated when limited clouds are detected by surface observations. The bias observed in several cloudiness categories indicates that the NSRDB is exceptionally accurate in confidently clear conditions. However, clear-sky conditions with scattered clouds and mismatched cloud data contribute significantly to the overall uncertainties in the NSRDB. Therefore, future improvements in solar resource data should involve development and implementation of satellite-derived cloud fraction and should consider a novel radiative transfer model accounting for amplified cloud reflection. The evaluation within cloudiness categories also provides a physical rationale for the superior performance of FARMS-DNI compared to the Direct Insolation Simulation Code (DISC) in both cloudy-sky and all-sky conditions.

14 SOLAR ENERGY↗
DOE OSTI2021

Local limits of detection for anthropogenic aerosol-cloud interactions

Ship tracks are quasi-linear cloud patterns produced from the interaction of ship emissions with low boundary layer clouds. They are visible throughout the diurnal cycle in satellite images from space-borne assets like the Advanced Baseline Imagers (ABI) aboard the National Oceanic and Atmospheric Administration Geostationary Operational Environmental Satellites (GOES-R). However, complex atmospheric dynamics often make it difficult to identify and characterize the formation and evolution of tracks. Ship tracks have the potential to increase a cloud's albedo and reduce the impact of global warming. Thus, it is important to study these patterns to better understand the complex atmospheric interactions between aerosols and clouds to improve our climate models, and examine the efficacy of climate interventions, such as marine cloud brightening. Over the course of this 3-year project, we have developed novel data-driven techniques that advance our ability to assess the effects of ship emissions on marine environments and the risks of future marine cloud brightening efforts. The three main innovative technical contributions we will document here are a method to track aerosol injections using optical flow, a stochastic simulation model for track formations and an automated detection algorithm for efficient identification of ship tracks in large datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Automatic detection of ship-induced cloud features in satellite imagery

Ships crossing the ocean are known to produce long, curvilinear features called ship tracks visible in satellite imagery via the Twomey effect; however, there has been little exploitation of satellite imagery for broad atmospheric studies or global monitoring of ship emissions due to the difficulty of automated ship track detection. Prior studies are either proof-of-concept, qualitatively assessed, or restricted to a certain time of day. We propose a statistical method for the automated identification of ship tracks and demonstrate using GOES-West ABI data. We first present a human-assisted segmentation method, which we use to generate a ground truth data set of 529 annotated ship tracks in GOES-West ABI products. We then describe a two-stage automated approach comprising a detection stage to generate ship track proposals and a classification stage to reduce false positives. For detection, we present a novel pipeline based around a z-score filtering technique, and for classification, we demonstrate several classifiers from literature. In a final experiment, we quantitatively tune the detection parameters and train the classifier using the ground truth dataset, then test on a sequestered set of images; the detect-then-classify system had an overall Pd of 0.68 and 0.80 for daytime and nighttime data, respectively, and the classifier reduced false positive detections by 67% and 75%.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Machine-learning-based investigation of the variables affecting summertime lightning occurrence over the Southern Great Plains

Lightning is affected by many factors, many of which are not routinely measured, well understood, or accounted for in physical models. Several commonly used machine learning (ML) models have been applied to analyze the relationship between Atmospheric Radiation Measurement (ARM) data and lightning data from the Earth Networks Total Lightning Network (ENTLN) in order to identify important variables affecting lightning occurrence in the vicinity of the Southern Great Plains (SGP) ARM site during the summer months (June, July, August and September) of 2012 to 2020. Testing various ML models, we found that the random forest model is the best predictor among common classifiers. When convective clouds were detected, it predicts lightning occurrence with an accuracy of 76.9 % and an area under the curve (AUC) of 0.850. Using this model, we further ranked the variables in terms of their effectiveness in nowcasting lightning and identified geometric cloud thickness, rain rate and convective available potential energy (CAPE) as the most effective predictors. The contrast in meteorological variables between no-lightning and frequent-lightning periods was examined for hours with CAPE values conducive to thunderstorm formation. Besides the variables considered for the ML models, surface variables and mid-altitude variables (e.g., equivalent potential temperature and minimum equivalent potential temperature, respectively) have statistically significant contrasts between no-lightning and frequent-lightning hours. For example, the minimum equivalent potential temperature from 700 to 500 hPa is significantly lower during frequent-lightning hours compared with no-lightning hours. Finally, a notable positive relationship between the intracloud (IC) flash fraction and the square root of CAPE ($\sqrt{CAPE}$) was found, suggesting that stronger updrafts increase the height of the electrification zone, resulting in fewer flashes reaching the surface and consequently a greater IC flash fraction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Automated construction of clear-sky dictionary from all-sky imager data

All-sky imagers (ASIs) have significant promise as scalable sensors for short-term solar irradiance forecasting. Many of the current computational techniques that use ASIs for this purpose rely on collections of clear-sky images indexed by time of day, solar angle, or both, called clear-sky dictionaries (CSDs). These CSDs act as baselines against which images can be compared to locate and classify clouds within the image frame. CSDs are often compiled by hand, where individuals visually inspect collections of images one at a time to find clear-sky images. This process is not scalable, and it is prone to error. This paper proposes an automated, nonparametric alternative that uses the principles of digital image processing to find clear-sky images within a set of images taken over several days. We use ground-truth measurements of the clearness index to assess the performance of our method, and we show that the images it selects accurately correspond to clear-sky images. We also compare our proposal, which is nonparametric, with a state-of-the-art parametric method. The numerical results indicate that the performance of the method proposed here is superior.

14 SOLAR ENERGY↗
DOE OSTI2022

Lightning Flash Detection System (LFDS) [Poster]

The Electromagnetics Group wanted another system for detecting lightning flashes near/around the Pantex Plant. The current system in use detects lightning by detecting RF signals at a particular frequency. LFDS detects lightning by detecting flashes of light in the visual and near IR spectrums. This work is primarily focused on detecting cloud-to-ground lightning.

42 ENGINEERING↗
DOE OSTI2025

ARMing the Edge: Demonstration of Edge Computing Field Campaign Report

Edge computing enables “next-to-instrument” control and intelligent data volume reduction and the potential for autonomous, adaptive measurement strategies such as for automated control of scan strategies for Doppler lidar (DL). Instruments with narrow bandwidth connections (e.g., ship and remote sites) can do scene determination and save phenomenon-appropriate data. For example, Doppler spectrum can be saved when clouds are detected by the instrument or automatic moment detection can take place in camera images and only preserve spectrum when non-monomodal spectra are detected. Automated control at the edge involves changing the sampling (temporal or scanning strategy) of an instrument to suit the phenomena both present and being studied (Jackson et al. 2020). Both data processing and instrument control introduces the possibility of a software-defined instrument.

54 ENVIRONMENTAL SCIENCES↗