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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
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↗
NASA NTRS1990

A climatology of satellite-derived cloud properties over marine stratocumulus regions

Satellite data on cloud properties obtained during the First ISCCP Regional Experiment (FIRE) (where ISCCP stands for Interantional Satellite Cloud Climatology Project) were analyzed using a methodology used in the 1987 FIRE Intensive Field Observations (IFO) measurements, in order to link the small-scale IFO results to the ISCCP large-scale bulk cloud properties and to provide a detailed climatology for climate modeling. The preliminary results of the analysis delineated some of the features common to all four major subtropical marine stratus areas analyzed. The differences observed between the areas were greatest for the values of cloud optical depths.

Heck, Patrick W.↗
NASA NTRS2001

Consistency Study of Enhanced Shortwave Cloud Absorption Using GEBA Data

Under the support of this project, we have obtained the following results:(1) Shortwave radiative fluxes in current atmospheric general circulation models (GCMs) cannot simultaneously match Earth Radiation Budget Experiment (ERBE) at the top of the atmosphere (TOA) and Global Energy Balance Archive (GEBA) at the surface. This inconsistency of model results with observation is a result of insufficient absorption of solar radiation in the model atmosphere; (2) Current state-of-the art global datasets describing the energy balance of the atmosphere cannot close the atmospheric energy budget if algorithm-derived surface shortwave radiative fluxes are used. The deficient amount of 20 W/sq m is similar to the recently reported enhanced absorption of solar radiation in the atmosphere; (3) We have clarified several sampling problems in the analysis of the collocated monthly GEBA/ERBE data sets which are germane to the interpretation of the clear-sky absorption of shortwave radiation in the atmosphere. As a result, the collocated monthly ERBE/GEBA data can be effectively used to infer enhanced absorption of atmospheric radiation in measurements relative to models, but it cannot be unambiguously used to answer whether the enhanced absorption is in clouds or in clear sky. Other field data are needed to resolve this issue; and (4) Analysis of aircraft measurements during Atmospheric Radiation Measurement Enhanced Shortwave Experiment (ARESE) field campaign supports the enhanced absorption of solar radiation in clouds.

Zhang, Ming-Hua↗
DOE OSTI2016

Designing of smart home automation system based on Raspberry Pi

Locally networked or remotely controlled home automation system becomes a popular paradigm because of the numerous advantages and is suitable for academic research. This paper proposes a method for an implementation of Raspberry Pi based home automation system presented with an android phone access interface. The power consumption profile across the connected load is measured accurately through programming. Users can access the graph of total power consumption with respect to time worldwide using their Dropbox account. An android application has been developed to channelize the monitoring and controlling operation of home appliances remotely. This application facilitates controlling of operating pins of Raspberry Pi by pressing the corresponding key for turning “on” and “off” of any desired appliance. Systems can range from the simple room lighting control to smart microcontroller based hybrid systems incorporating several other additional features. Smart home automation systems are being adopted to achieve flexibility, scalability, security in the sense of data protection through the cloud-based data storage protocol, reliability, energy efficiency, etc.

42 ENGINEERING↗
DOE OSTI2019

A novel approach for characterizing the variability in mass–dimension relationships: results from MC3E

Abstract. Mass–dimension (m–D) relationships determining bulkmicrophysical properties such as total water content (TWC) and radarreflectivity factor (Z) from particle size distributions are used in bothnumerical models and remote sensing retrievals. The a and b coefficientsrepresenting m=aD b relationships, however, can vary significantlydepending on meteorological conditions, particle habits, the definition ofparticle maximum dimension, the probes used to obtain the data, techniquesused to process the cloud probe data, and other unknown reasons. Thus,considering a range of a,b coefficients may be more applicable for use innumerical models and remote sensing retrievals. Microphysical data collectedby two-dimensional optical array probes (OAPs) installed on the University ofNorth Dakota (UND) Citation aircraft during the Mid-latitude Continental ConvectiveClouds Experiment (MC3E) were used in conjunction with TWC data from aNevzorov probe and ground-based S-band radar data to determine a and busing a technique that minimizes the chi-square difference between the TWC andZ derived from the OAPs and those directly measured by a TWC probe andradar. All a and b values within a specified tolerance were regarded as equallyplausible solutions. Of the 16 near-constant-temperature flight legs analyzedduring the 25 April, 20 May, and 23 May 2011 events, the derived surfaces ofsolutions on the first 2 days where the aircraft-sampled stratiform cloudhad a larger range in a and b for lower temperature environments thatcorrespond to less variability in N(D), TWC, and Z for a flight leg.Because different regions of the storm were sampled on 23 May, differences inthe variability in N(D), TWC, and Z influenced the distribution ofchi-square values in the (a,b) phase space and the specified tolerance in away that yielded 2.8 times fewer plausible solutions compared to the flightlegs on the other dates. These findings show the importance of representingthe variability in a,b coefficients for numerical modeling and remotesensing studies, rather than assuming fixed values, as well as the need tofurther explore how these surfaces depend on environmental conditions inclouds containing ice hydrometeors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

MSD CoP Webinar: "Advances in MSD-LIVE to Support the MSD Community of Practice"

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Advances in MSD-LIVE to Support the MSD Community of Practice Presenters: Casey Burleyson and Zoe Guillen (Pacific Northwest National Laboratory) Abstract: The MultiSector Dynamics Living, Intuitive, Value-adding, Environment (MSD-LIVE; msdlive.org) is a cloud-based data management system and advanced computing platform that enables MSD researchers to document and archive their data, run their models and analysis tools, and share their data, software, and workflows within the MSD Community of Practice. Recently, several high-profile datasets have attracted many new users to MSD-LIVE. This webinar has two goals: 1) To refamiliarize the MSD community and new users with the components of the platform (e.g., the data repository, model training notebooks, and data dashboards) and to highlight examples of how these components are advancing MSD science and 2) To demonstrate new features in v3 of the platform, released in late 2025. The main new feature in v3 is the ability to interactively explore data in MSD-LIVE without downloading it. MSD-LIVE users can now click a button in our data repository and launch a blank Jupyter notebook with access to the underlying data on AWS. Users can use the notebook to write analysis, visualization, or subsetting routines that process the data directly on the AWS cloud. We also added a GitHub integration feature that allows users to share analysis or visualization code they develop with the community of MSD-LIVE users. The webinar will wrap up with a look at what's coming next for MSD-LIVE in 2026. Moderator: Patrick M. Reed (MSD CoP Facilitation Team) This webinar was held on: May 12th, 2026 from 1-2 PM EST.

Open Science↗
NASA NTRS1982

Study of atmospheric diffusion using LANDSAT

The parameters of diffusion patterns of atmospheric pollutants under different conditions were investigated for use in the Gaussian model for calculation of pollution concentration. Value for the divergence pattern of concentration distribution along the Y axis were determined using LANDSAT images. Multispectral scanner images of a point source plume having known characteristics, wind and temperature data, and cloud cover and solar elevation data provided by LANDSAT, were analyzed using the 1-100 system for image analysis. These measured values are compared with pollution transport as predicted by the Pasquill-Gifford, Juelich, and Hoegstroem atmospheric models.

Torsani, J. A.↗
NASA NTRS2018

Generalizing a Data Analysis Pipeline in the Cloud to Handle Diverse Use Cases in NASA's EOSDIS

NASA's Earth Observing System Data and Information System (EOSDIS) is tasked with archiving and distributing Earth Observation data across a range of disciplines, including atmospheric science, oceanography, land processes, natural hazards, solar radiance and even socioeconomic aspects relating to the environment. Driven by rapidly rising data volumes, EOSDIS is migrating to a cloud computing based archive over the next few years. Although this simplifies data management somewhat, the main aim is to provide the data in an environment where end users can bring their analysis to the data rather than attempting to download and manage ever-increasing volumes. To that end, a cloud-based analysis platform is being constructed to enable data transformations, analyses and visualization without egressing the data from the cloud. In this endeavor, we expect a wide variety of users, algorithms and use cases. Consequently, the architecture of this cloud analytics platform is expressly designed to be based on open services, thus fostering an ecosystem that enables the efficient combination of common components with data-specific or analysis-specific components. Reviewed and approved by Andrew Mitchell, ESDIS project manager.

Cloud computing↗
NASA NTRS2006

Seasonal and Interannual Variations of Top-of-Atmosphere Irradiance and Cloud Cover over Polar Regions Derived from the CERES Data Set

The semi-direct effects of dust aerosols are analyzed over eastern Asia using 2 years (June 2002 to June 2004) of data from the Clouds and the Earth s Radiant Energy System (CERES) scanning radiometer and MODerate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite, and 18 years (1984 to 2001) of International Satellite Cloud Climatology Project (ISCCP) data. The results show that the water path of dust-contaminated clouds is considerably smaller than that of dust-free clouds. The mean ice water path (IWP) and liquid water path (LWP) of dusty clouds are less than their dust-free counterparts by 23.7% and 49.8%, respectively. The long-term statistical relationship derived from ISCCP also confirms that there is significant negative correlation between dust storm index and ISCCP cloud water path. These results suggest that dust aerosols warm clouds, increase the evaporation of cloud droplets and further reduce cloud water path, the so-called semi-direct effect. The semi-direct effect may play a role in cloud development over arid and semi-arid areas of East Asia and contribute to the reduction of precipitation.

Kato, Seiji↗
NASA NTRS1983

Diurnal and seasonal variations of clouds from geostationary satellite data

Hourly 8-km visible (0.55-0.75 micron) and infrared (10.5-12.5 microns) data from the eastern Geostationary Operational Environmental Satellite were analyzed with a hybrid bispectral threshold method to yield total, low, middle, and high level cloud fractions and cloud radiative parameters for 250 x 250 sq km regions. These data were analyzed for the area between 45 deg N and 45 deg S, and longitudes 30 deg W and 125 deg W for the autumn of 1978 and the winter of 1979. Preliminary results for winter are presented in the present study. It is shown that diurnal variations of clouds may reveal diurnal variability in regional and large-scale circulation patterns. These diurnal cloud cycles may also have substantial effects on regional and large-scale radiation budgets and, therefore, influence circulation patterns at various spatial scales.

Minnis, P.↗
NASA NTRS1986

Zonal and geographical distributions of cirrus clouds determined from SAGE data

Stratospheric Aerosol and Gas Experiment (SAGE) data obtained from February 1979-November 1981 are analyzed in order to evaluate the spatial extent and frequency occurrence of cirrus clouds. The capabilities of the SAGE measurement system which has a field of view of 100 sq km are discussed. The frequency of occurrence of the cirrus clouds and the frequency penetration of the clouds to fixed altitudes of 5, 7, and 9 km, and to altitudes of 1, 3 and 5 km below the troposphere are examined. It is observed that optically thick cirrus clouds form most frequently in the midlatitudes over the equator, with distinct minima near latitude bands of 20-30 deg north and south; height penetrations to 7 km occur 60 percent of the time in upper latitudes and drop to 30 percent over the tropics. The SAGE data are compared with selective chopper radiometer data and good correlation in shape and seasonal movement is displayed. The seasonal geographical distributions of cirrus clouds in regions of rising moist air associated with low-level convergence zones are described.

Woodbury, G. E.↗
NASA NTRS1981

Cloud retrieval using infrared sounder data - Error analysis

An error analysis is presented for cloud-top pressure and cloud-amount retrieval using infrared sounder data. Rms and bias errors are determined for instrument noise (typical of the HIRS-2 instrument on Tiros-N) and for uncertainties in the temperature profiles and water vapor profiles used to estimate clear-sky radiances. Errors are determined for a range of test cloud amounts (0.1-1.0) and cloud-top pressures (920-100 mb). Rms errors vary by an order of magnitude depending on the cloud height and cloud amount within the satellite's field of view. Large bias errors are found for low-altitude clouds. These bias errors are shown to result from physical constraints placed on retrieved cloud properties, i.e., cloud amounts between 0.0 and 1.0 and cloud-top pressures between the ground and tropopause levels. Middle-level and high-level clouds (above 3-4 km) are retrieved with low bias and rms errors.

Wielicki, B. A.↗
NASA NTRS1986

Empirical and modeled synoptic cloud climatology of the Arctic Ocean

A set of cloud cover data were developed for the Arctic during the climatically important spring/early summer transition months. Parallel with the determination of mean monthly cloud conditions, data for different synoptic pressure patterns were also composited as a means of evaluating the role of synoptic variability on Arctic cloud regimes. In order to carry out this analysis, a synoptic classification scheme was developed for the Arctic using an objective typing procedure. A second major objective was to analyze model output of pressure fields and cloud parameters from a control run of the Goddard Institue for Space Studies climate model for the same area and to intercompare the synoptic climatatology of the model with that based on the observational data.

Barry, R. G.↗
NASA NTRS2011

Enhancing a Simple MODIS Cloud Mask Algorithm for the Landsat Data Continuity Mission

The presence of clouds in images acquired by the Landsat series of satellites is usually an undesirable, but generally unavoidable fact. With the emphasis of the program being on land imaging, the suspended liquid/ice particles of which clouds are made of fully or partially obscure the desired observational target. Knowing the amount and location of clouds in a Landsat scene is therefore valuable information for scene selection, for making clear-sky composites from multiple scenes, and for scheduling future acquisitions. The two instruments in the upcoming Landsat Data Continuity Mission (LDCM) will include new channels that will enhance our ability to detect high clouds which are often also thin in the sense that a large fraction of solar radiation can pass through them. This work studies the potential impact of these new channels on enhancing LDCM's cloud detection capabilities compared to previous Landsat missions. We revisit a previously published scheme for cloud detection and add new tests to capture more of the thin clouds that are harder to detect with the more limited arsenal channels. Since there are no Landsat data yet that include the new LDCM channels, we resort to data from another instrument, MODIS, which has these bands, as well as the other bands of LDCM, to test the capabilities of our new algorithm. By comparing our revised scheme's performance against the performance of the official MODIS cloud detection scheme, we conclude that the new scheme performs better than the earlier scheme which was not very good at thin cloud detection.

Wilson, Michael J.↗
NASA NTRS2019

A Web of Data Analytics Services

Cloud Computing has become the ubiquitous approach to our Big Data challenge. However, one will quickly discover that moving (a.k.a. forklifting) existing on-premise data analytics solutions to the Cloud doesn’t always translate to costing saving and performance boost. The Cloud’s elasticity, its availability, and its wide selection of computing options and selections of costing models making Cloud an attractive environment to tackle our Big Data challenge. The fact is Cloud, on its own, is not the silver bullet to our daunting challenge need for analyze and derive scientific inferences through vast collections of multi-sensor measurements. We would like to have all scientific data in one easy to access environment, but getting the world of scientific data in one analytic system is immensely difficult to achieve. This paper describes the data analytics web architecture NASA is developing by infusing instances of Integrated Data Analytics systems next to the data. The goal is to minimize unnecessary data movement through collection of data access and analytics webservices for researchers to interact with and analyze measurements without have to download data to their local computer. These services are RESTful and provisioned by the data centers with the help from subject matter and science experts. These services encapsulate the physical computing infrastructure, which could local computing cluster, on-premise or public Cloud environment.

Huang, Thomas↗