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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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NASA NTRS1996

Full-Sky Imaging at Low Frequencies

Exploring the frequency range from a few tens of MHZ down to a few tens of kHz commonly requires multiple antennas operating as an aperture synthesis interferometer to obtain angular resolution. This design must overcome the corrupting effects of interplanetary scintillation, interfering signals and nearly isotropic antennas that see strong sources such as the Sun and Jupiter at all times.

Jones, D. L.↗
DOE OSTI2021

Self-supervised Representation Learning for Astronomical Images

Sky surveys are the largest data generators in astronomy, making automated tools for extracting meaningful scientific information an absolute necessity. We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks. These representations can be directly used as features, or fine-tuned, to outperform supervised methods trained only on labeled data. We apply a contrastive learning framework on multiband galaxy photometry from the Sloan Digital Sky Survey (SDSS), to learn image representations. We then use them for galaxy morphology classification and fine-tune them for photometric redshift estimation, using labels from the Galaxy Zoo 2 data set and SDSS spectroscopy. In both downstream tasks, using the same learned representations, we outperform the supervised state-of-the-art results, and we show that our approach can achieve the accuracy of supervised models while using 2-4 times fewer labels for training. The codes, trained models, and data can be found at https://portal.nersc.gov/project/dasrepo/self-supervised-learning-sdss.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Data associated with the manuscript “Radiative impact of record-breaking wildfires from integrated ground-based data” collected in Richland, Washington in September 2020

This data package is associated with the publication “Radiative impact of record-breaking wildfires from integrated ground-based data” submitted to Nature Scientific Reports (Kassianov et al., 2024). Data from ground-based measurements of shortwave and spectrally resolved irradiance and aerosol optical depth (AOD) in the visible and near-infrared spectral ranges were assessed to quantify the radiative impact of the September 2020 wildfires that occurred in the Western United States. Data were collected in September 2020 by several ground-based instruments at the Atmospheric Measurements Laboratory (AML) located in Richland, Washington (46.3451, -119.2792). These data include (1) Aerosol Optical Depth (AOD); (2) spectrally resolved and shortwave (SW) irradiances; (3) backscatter profiles; (4) total sky images; and (5) near-surface ambient air temperatures.The data package consists of five sub-directories: (1) “AML_Ceilometer_”; (2)” AML_CSPHOT_”; (3) “AML_MFRSR_irradiances_”; (4) “AML_SW_irradiances_and_Temp_”; (5) “AML_TSI_images_”; and 6 files stored at the directory level, including the readme, file-level metadata file, and data dictionary. The file-level metadata file (the file ending in “_flmd.csv”) lists all files contained in this data package and descriptions for each. The data dictionary (the file ending in “_dd.csv”) describes each tabular column header’s unit, definition, and structure. Below are descriptions of each sub-directory:“AML_Ceilometer_” includes ceilometer data collected at the AML. These files contain the corresponding narratives of data. Details related to the ceilometer data can be found in Morris (2016). “AML_CSPHOT_” includes ascii files with high-temporal resolution (about 10-15 min) AML CSPHOT data and their daily-averaged counterparts. These two files contain the corresponding narratives of data. Details related to the CSPHOT data can be found in Gregory (2011). “AML_MFRSR_irradiances_” includes ascii files with the AML MFRSR-measured diffuse, normal, and total spectrally resolved irradiance. Details related to the MFRSR data can be found in Hodges and Michalsky (2016) and Koontz et al. (2013). “AML_SW_irradiances_+_Temp_” includes near-surface ambient air temperature and SW irradiances, namely direct normal, diffuse hemispherical, and total hemispheric (global), measured at the AML. These files also incorporate the corresponding narratives of data. Details related to the SW irradiances can be found in Andreas et al. (2018). “AML_TSI_images_” includes Total Sky Images (TSIs) collected at the AML. Details related to the TSI data can be found in Morris (2005).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Method of solar power prediction

Techniques for estimating direct and diffuse sunlight reaching a location as a function of time using color whole sky images are provided. In one aspect, a method for forecasting solar insolation is provided that includes the steps of: obtaining color whole sky images using a digital camera which are taken at different times, and wherein an image of a solar disk is present in at least one of the color whole sky images; creating sky transparency images from the color whole sky images; determining atmospheric solar scattering based on a width of the solar disk in the color whole sky images; determining cloud motion trajectories from a time series of the sky transparency images; and calculating the solar insolation as a function of time using the sky transparency, the atmospheric solar scattering, and the cloud motion trajectories. A system for forecasting solar insolation is also provided.

Hamann, Hendrik F.↗
NASA NTRS1990

Workstation-based preprocessing of IRAS sky-flux images

Computer algorithms to remove two types of degradations in IRAS sky-flux images, namely slowly varying background illumination (strongly effected by the presence of zodiacal light) and periodic stripes, are presented. This paper discusses both algorithms in detail and shows results of its use on various sky-flux images. Focus of the work was on the implementation within a workstation environment, and its value as a preprocessing tool for researchers. Speed of the process, usability of the programs, and correctness of the results were the main goals in developing these tools.

Domik, Gitta↗
DOE OSTI2017

Image phase shift invariance based cloud motion displacement vector calculation method for ultra-short-term solar PV power forecasting

Irradiance received on the earth's surface is the main factor that affects the output power of solar PV plants, and is chiefly determined by the cloud distribution seen in a ground-based sky image at the corresponding moment in time. It is the foundation for those linear extrapolation-based ultra-short-term solar PV power forecasting approaches to obtain the cloud distribution in future sky images from the accurate calculation of cloud motion displacement vectors (CMDVs) by using historical sky images. Theoretically, the CMDV can be obtained from the coordinate of the peak pulse calculated from a Fourier phase correlation theory (FPCT) method through the frequency domain information of sky images. The peak pulse is significant and unique only when the cloud deformation between two consecutive sky images is slight enough, which is likely possible for a very short time interval (such as 1?min or shorter) with common changes in the speed of cloud. Sometimes, there will be more than one pulse with similar values when the deformation of the clouds between two consecutive sky images is comparatively obvious under fast changing cloud speeds. This would probably lead to significant errors if the CMDVs were still only obtained from the single coordinate of the peak value pulse. However, the deformation estimation of clouds between two images and its influence on FPCT-based CMDV calculations are terrifically complex and difficult because the motion of clouds is complicated to describe and model. Therefore, to improve the accuracy and reliability under these circumstances in a simple manner, an image-phase-shift-invariance (IPSI) based CMDV calculation method using FPCT is proposed for minute time scale solar power forecasting. First, multiple different CMDVs are calculated from the corresponding consecutive images pairs obtained through different synchronous rotation angles compared to the original images by using the FPCT method. Second, the final CMDV is generated from all of the calculated CMDVs through a centroid iteration strategy based on its density and distance distribution. Third, the influence of different rotation angle resolution on the final CMDV is analyzed as a means of parameter estimation. Simulations under various scenarios including both thick and thin clouds conditions indicated that the proposed IPSI-based CMDV calculation method using FPCT is more accurate and reliable than the original FPCT method, optimal flow (OF) method, and particle image velocimetry (PIV) method.

14 SOLAR ENERGY↗
NASA NTRS1994

Development of a Mars Surface Imager

The Mars Surface Imager (MSI) is a multispectral, stereoscopic, panoramic imager that allows imaging of the full scene around a Mars lander from the lander body to the zenith. It has two functional components: panoramic imaging and sky imaging. In the most recent version of the MSI, called PIDDP-cam, a very long multi-line color CCD, an innovative high-performance drive system, and a state-of-the-art wavelet image compression code have been integrated into a single package. The requirements for the flight version of the MSI and the current design are presented.

Squyres, Steve W.↗
NASA NTRS2017

All-Sky Microwave Imager Data Assimilation at NASA GMAO

Efforts in all-sky satellite data assimilation at the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center have been focused on the development of GSI configurations to assimilate all-sky data from microwave imagers such as the GPM Microwave Imager (GMI) and Global Change Observation Mission-Water (GCOM-W) Advanced Microwave Scanning Radiometer 2 (AMSR-2). Electromagnetic characteristics associated with their wavelengths allow microwave imager data to be relatively transparent to atmospheric gases and thin ice clouds, and highly sensitive to precipitation. Therefore, GMAOs all-sky data assimilation efforts are primarily focused on utilizing these data in precipitating regions. The all-sky framework being tested at GMAO employs the GSI in a hybrid 4D-EnVar configuration of the Goddard Earth Observing System (GEOS) data assimilation system, which will be included in the next formal update of GEOS. This article provides an overview of the development of all-sky radiance assimilation in GEOS, including some performance metrics. In addition, various projects underway at GMAO designed to enhance the all-sky implementation will be introduced.

NWP↗
DOE OSTI2025

Applying Deep Learning for Wildfire Identification: Economical and Accessible Solutions Leveraging Small Datasets

Wildfires significantly impact human health, air quality, visibility, weather, and climate change and cause substantial economic losses. While state and county-operated air quality monitors provide critical insights during wildfires, they are not available in all regions. This highlights the need for affordable, accessible tools that allow the general public to assess air quality impacts. In this study, we apply machine learning with deep neural networks to diagnose air quality rapidly from sky images taken at the Pacific Northwest National Laboratory in Richland, WA, USA. Using a convolutional neural network (CNN) framework, we trained a deep learning model to classify air quality indices based on sky images. By leveraging transfer learning, our approach fine-tunes a pre-trained model on a small dataset of sky images, significantly reducing training time while maintaining high accuracy. Our results demonstrate the potential of deep learning to provide rapid air quality diagnostics during wildfire episodes, offering early warnings to the public and enabling timely mitigation strategies, particularly for vulnerable populations. Additionally, we show that lower respiratory infections pose the highest health risk during acute smoke exposures. Reactive oxygen species (ROS) from wildfire particles further exacerbate health risks by triggering inflammation and other adverse effects.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Automatic detection of low surface brightness galaxies from Sloan Digital Sky Survey images

ABSTRACT Low surface brightness (LSB) galaxies are galaxies with central surface brightness fainter than the night sky. Due to the faint nature of LSB galaxies and the comparable sky background, it is difficult to search LSB galaxies automatically and efficiently from large sky survey. In this study, we established the low surface brightness galaxies autodetect (LSBG-AD) model, which is a data-driven model for end-to-end detection of LSB galaxies from Sloan Digital Sky Survey (SDSS) images. Object-detection techniques based on deep learning are applied to the SDSS field images to identify LSB galaxies and estimate their coordinates at the same time. Applying LSBG-AD to 1120 SDSS images, we detected 1197 LSB galaxy candidates, of which 1081 samples are already known and 116 samples are newly found candidates. The B-band central surface brightness of the candidates searched by the model ranges from 22 to 24 mag arcsec−2, quite consistent with the surface brightness distribution of the standard sample. A total of 96.46 per cent of LSB galaxy candidates have an axial ratio (b/a) greater than 0.3, and 92.04 per cent of them have $fracDev\_r$ < 0.4, which is also consistent with the standard sample. The results show that the LSBG-AD model learns the features of LSB galaxies of the training samples well, and can be used to search LSB galaxies without using photometric parameters. Next, this method will be used to develop efficient algorithms to detect LSB galaxies from massive images of the next-generation observatories.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Let’s Unleash the Network Judgment: A Self-Supervised Approach for Cloud Image Analysis

Accurate cloud type identification and coverage analysis are crucial in understanding the Earth’s radiative budget. Traditional computer vision methods rely on low-level visual features of clouds for estimating cloud coverage or sky conditions. Several handcrafted approaches have been proposed; however, scope for improvement still exists. Newer deep neural networks (DNNs) have demonstrated superior performance for cloud segmentation and categorization. These methods, however, need expert engineering intervention in the preprocessing steps—in the traditional methods—or human assistance in assigning cloud or clear sky labels to a pixel for training DNNs. Such human mediation imposes considerable time and labor costs. We present the application of a new self-supervised learning approach to autonomously extract relevant features from sky images captured by ground-based cameras, for the classification and segmentation of clouds. We evaluate a joint embedding architecture that uses self-knowledge distillation plus regularization. We use two datasets to demonstrate the network’s ability to classify and segment sky images—one with ~85,000 images collected from our ground-based camera and another with 400 labeled images from the WSISEG database. We find that this approach can discriminate full-sky images based on cloud coverage, diurnal variation, and cloud base height. Additionally, it semantically segments the cloud areas without labels. The approach shows competitive performance in all tested tasks, suggesting a new alternative for cloud characterization.

54 ENVIRONMENTAL SCIENCES↗