Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “CLOUD DATA”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 937 records · Page 52

Synthetic observations of the evolution of starless cores in a molecular cloud simulation: Comparisons with JCMT data and predictions for alma

Interpreting the nature of starless cores has been a prominent goal in star formation for many years. In order to characterize the evolutionary stages of these objects, we perform synthetic observations of a numerical simulation of a turbulent molecular cloud. We find that nearly all cores that we detect are associated with filaments and eventually form protostars. We conclude that observed starless cores that appear Jeans unstable are only marginally larger than their respective Jeans masses (within a factor of three). We note single dish observations such as those performed with the James Clerk Maxwell Telescope appear to miss significant core structure on small scales because of beam averaging. Finally, we predict that interferometric observations with Atacama Large Millimeter Array Cycle 1 will resolve the important small-scale structure, which has so far been missed by millimeter wavelength observations.

79 ASTRONOMY AND ASTROPHYSICS↗

Cloud fraction and cloud size distributions derived using Landsat Multispectral Scanner data

Initial results are presented on the spatial structure of cumulus cloud fields derived using Landsat digital data. Techniques to determine clear, overcast, and cloud edge pixels are discussed and used to analyze four Landsat scenes. Cloud number densities, cloud cover, cloud aspect ratio, and the ratio of interior to cloud edge pixels are obtained for the four scenes and are discussed. This new application of Landsat digital data should prove useful in developing and testing models of the radiative and dynamical properties of cumulus cloud fields on GCM spatial scales.

Wielicki, B. A.↗

Use of Probability Distribution Functions for Discriminating Between Cloud and Aerosol in Lidar Backscatter Data

In this paper we describe the algorithm hat will be used during the upcoming Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission for discriminating between clouds and aerosols detected in two wavelength backscatter lidar profiles. We first analyze single-test and multiple-test classification approaches based on one-dimensional and multiple-dimensional probability density functions (PDFs) in the context of a two-class feature identification scheme. From these studies we derive an operational algorithm based on a set of 3-dimensional probability distribution functions characteristic of clouds and aerosols. A dataset acquired by the Cloud Physics Lidar (CPL) is used to test the algorithm. Comparisons are conducted between the CALIPSO algorithm results and the CPL data product. The results obtained show generally good agreement between the two methods. However, of a total of 228,264 layers analyzed, approximately 5.7% are classified as different types by the CALIPSO and CPL algorithm. This disparity is shown to be due largely to the misclassification of clouds as aerosols by the CPL algorithm. The use of 3-dimensional PDFs in the CALIPSO algorithm is found to significantly reduce this type of error. Dust presents a special case. Because the intrinsic scattering properties of dust layers can be very similar to those of clouds, additional algorithm testing was performed using an optically dense layer of Saharan dust measured during the Lidar In-space Technology Experiment (LITE). In general, the method is shown to distinguish reliably between dust layers and clouds. The relatively few erroneous classifications occurred most often in the LITE data, in those regions of the Saharan dust layer where the optical thickness was the highest.

Liu, Zhaoyan↗

Potential impact of aerosols on convective clouds revealed by Himawari-8 observations over different terrain types in eastern China

Convective clouds are common and play a major role in Earth's water cycle and energy balance; they may even develop into storms and cause severe rainfall events. To understand the convective cloud development process, this study investigates the impact of aerosols on convective clouds by considering the influence of both topography and diurnal variation in radiation. By combining texture analysis, clustering, and thresholding methods, we identify all convective clouds in two warm seasons (May–September, 2016/17) in eastern China based on Himawari-8 Level 1 data. Having large diurnally resolved cloud data together with surface meteorological and environmental measurements, we investigate convective cloud properties and their variation, stratified by elevation and diurnal change. We then analyze the potential impact of aerosol on convective clouds under different meteorological conditions and topographies. In general, convective clouds tend to occur preferentially under polluted conditions in the morning, which reverses in the afternoon. Convective cloud fraction first increases then decreases with aerosol loading, which may contribute to this phenomenon. Topography and diurnal meteorological variations may affect the strength of aerosol microphysical and radiative effects. Updraft is always stronger along the windward slopes of mountains and plateaus, especially in northern China. The prevailing southerly wind near the foothills of mountains and plateaus is likely to contribute to this windward strengthening of updraft and to bring more pollutant into the mountains, thereby strengthening the microphysical effect, invigorating convective clouds. By comparison, over plain, aerosols decrease surface heating and suppress convection by blocking solar radiation reaching the surface.

clouds↗

A Path to Improving Simulated Properties of Low Clouds over the Beaufort Sea using Airborne In Situ Observations of Subgrid-Scale Variability

Arctic low clouds influence the evolution of the Arctic system through their effects on radiative fluxes, boundary layer mixing, stability, turbulence, humidity, and precipitation. Unfortunately, atmospheric models and retrospective analysis (reanalysis) products struggle to accurately simulate the occurrence and properties of low clouds in the Arctic. One of the main reasons for this problem are the possible unrealistic assumptions that models/reanalyses make about the subgrid-scale (SGS) variability of meteorological properties, as well as the relationship between SGS variability and grid-scale (GS) cloud properties. We utilize cloud and thermodynamic data of low level (primarily) liquid clouds collected from two aircraft campaigns conducted over the Beaufort Sea to better understand and characterize this problem. Examining data from the September 2014 Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) airborne campaign and the 1998 First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE)–Arctic Cloud Experiment (ACE) reveals that GS cloud water variability is closely related with SGS distribution of total water (i.e. water vapor + cloud water). We investigate two related approaches to prediction of GS cloud properties from SGS variability: the critical saturation ratio method, and the critical relative humidity method. We find significant correlation between GS cloud water and SGS supersaturation when the critical saturation ratio is set at 100%, as well as a notable relationship between GS cloud water and the width of the SGS total water distribution. Critical relative humidity also compares well with GS cloud water. However, we also find that the assumptions of a static critical saturation ratio of 100% to be unrealistic, as well as a fixed SGS distribution width. Empirical calculations from the ARISE data show a large sensitivity of these SGS variables to GS relative humidity, and so a SGS parameterization allowing them to vary according to GS thermodynamic properties may result in more realistic GS cloud water values.

J. Brant Dodson↗

Determination of stack plume properties from satellite imagery

LANDSAT imagery data were analyzed to determine the quantitative properties of a stack plume emitted from a moderate-sized pulp mill. Overlapping, consecutive-day MSS data provided plume/no plume radiances upwelling from the area of interest. These values from both the plume and its shadow were used to evaluate plume radius, height, particle concentration and scattering function, and total particle loading. Imagery data from a 10 by 10 km region in the vicinity of the mill were normalized to correct for minor atmospheric, solar and viewing angle changes for the two observation days, and cloud shadow data were used to evaluate sky radiance. The effects of the Sun angle, surface reflectance, SNR and spatial resolution are treated in the paper.

Staylor, W. F.↗

Comparison of stack plume properties obtained from aircraft and spacecraft measurements

Landsat II imagery data and aircraft nephelometer measurements were analyzed to determine the quantitative properties of a stack plume emitted from a moderate-sized pulpmill. Aircraft measurements were obtained at several heights across the plume at 1.0, 3.0, and 6.5 km downwind from the stack, and for comparative purposes, the Landsat data were also analyzed at these same three locations and at 0.5 and 10.0 km. Overlapping, consecutive-day MSS data provided plume/no-plume radiances upwelling from the stack site. Imagery data from a 10- by 10 km region in the vicinity of the mill were normalized to correct for atmospheric, solar, and viewing angle differences for the 2 observation days, and cloud-shadow data were used to evaluate sky radiance. Particle concentrations, vertical and lateral dispersions, and plume heights determined from both the aircraft and spacecraft measurement techniques are in good agreement.

Staylor, W. F.↗

Technical Challenges and Lessons from the Migration of the GLOBE Data and Information System to Utilize Cloud Computing Service

The Global Learning and Observation to Benefit the Environment (GLOBE) Data and Information System supports an international science and education program with capabilities to accept local environment observations, archive, display and visualize them along with global satellite observations. Since its inception twenty years ago, the Web and database system has been upgraded periodically to accommodate the changes in technology and the steady growth of GLOBEs education community and collection of observations. Recently, near the end-of-life of the system hardware, new commercial computer platform options were explored and a decision made to utilize Cloud services. Now the GLOBE DIS has been fully deployed and maintained using Amazon Cloud services for over two years now. This paper reviews the early risks, actual challenges, and some unexpected findings as a result of the GLOBE DIS migration. We describe the plans, cost drivers and estimates, highlight adjustments that were made and suggest improvements. We present the trade studies for provisioning, for load balancing, networks, processing, storage, as well as production, staging and backup systems. We outline the migration teams skills and required level of effort for transition, and resulting changes in the overall maintenance and operations activities. Examples include incremental adjustments to processing capacity and frequency of backups, and efforts previously expended on hardware maintenance that were refocused onto application-specific enhancements.

GLOBE Data↗

Computer simulation for attitude disturbance torques for the Tiros-series.

Description of the Tiros-M Magnetic Attitude Prediction Program (TMMGAP), a simulation program designed to assist in the rotational motion control of the Tiros-series spacecraft. The subsystems of the program include input, attitude simulation, executive, attitude control and output components, simulating solar radiation pressure torque, gravity gradient torque, residual magnetic moment torque, and magnetic attitude control torques. The program provides data for prelaunch studies for launch window selection and mission planning, facilitates spacecraft spin axis reorientation after launch, provides spin axis control recommendations to meet mission constraints, and assists in the interpretation of cloud cover data. The program is characterized as effective in controlling spin axis and in processing picture data.

Sugai, I.↗

Technical Challenges and Lessons from the Migration of the GLOBE Data and Information System to Utilize Cloud Computing Service

The Global Learning and Observation to Benefit the Environment (GLOBE) Data and Information System supports an international science and education program with capabilities to accept local environment observations, archive, display and visualize them along with global satellite observations. Since its inception twenty years ago, the Web and database system has been upgraded periodically to accommodate the changes in technology and the steady growth of GLOBEs education community and collection of observations. Recently, near the end-of-life of the system hardware, new commercial computer platform options were explored and a decision made to utilize Cloud services. Now the GLOBE DIS has been fully deployed and maintained using Amazon Cloud services for over two years now. This paper reviews the early risks, actual challenges, and some unexpected findings as a result of the GLOBE DIS migration. We describe the plans, cost drivers and estimates, highlight adjustments that were made and suggest improvements. We present the trade studies for provisioning, for load balancing, networks, processing, storage, as well as production, staging and backup systems. We outline the migration teams skills and required level of effort for transition, and resulting changes in the overall maintenance and operations activities. Examples include incremental adjustments to processing capacity and frequency of backups, and efforts previously expended on hardware maintenance that were refocused onto application-specific enhancements.

GLOBE↗

Multilevel cloud retrieval using multispectral HIRS and AVHRR data: Nighttime oceanic analysis

A multispectral, multiresolution (MSMR) method is developed for analyzing scenes of overlapping cloud layers. The MSMR method is applied to data from the NOAA 11 advanced very high resolution radiometer (AVHRR) and the high-resolution infrared radiometer sounder (HIRS-2). The data are from a nighttime oceanic scene in which a semitransparent cirrus veil overlays a large-scale stratus cloud. Low-cloud and clear-sky radiances are determined using a spatial coherence technique. Middle to upper level cloud pressures and radiances are estimated from HIRS-2 15 micrometer CO2 band radiometric data. The MSMR method improves the interpretation of a nighttime, oceanic scene containing thin cirrus over a large-scale stratiform cloud. If, for example, the same scene is analyzed using only the AVHRR 10.8 micrometer channel, the accompanying retrieved cloud heights are found to be between the cirrus and stratus cloud heights and are incorrectly identified as midlevel altostratus clouds. Theoretical radiative transfer model results for both water droplet spheres and randomly oriented hexagonal ice crystals are compared to observed AVHRR brightness temperature differences (BTD) between the 3.7- and 10.8 micrometer channels (BTD(sup 34)) and between the 10.8- and 12- micrometer channels (BTD(sup 45)) to distinguish among the effects of cloud optical depth, particle size, and phase for both single-layer clouds and overlapping two-layer clouds. Theoretical BTD calculations are used to estimate the range of effective particle sizes for eac h cloud layer. The data for the cirrus in the case study region near Bermuda are consistent with theoretical results for relatively small randomly oriented hexagonal ice crystals. The observed BTD(sup 34) and BTD(sup 45) values are lower for the cirrus above a lower-level cloud than for single-level cirrus with no underlying cloud. In certain cases the BTD analysis provides a way to distinguish between clouds composed of supercooled water droplets rather than ice particles. Analysis of nighttime data permits determination of stratus infrared optical depths smaller than 4.

Baum, Bryan A.↗

The Amazon River‐Breeze Circulation Limits Detection of Aerosol‐Cloud Interactions in Warm Clouds

Increased aerosol concentrations can brighten low-level clouds and extend their lifetimes, but aerosol–cloud interactions (ACI) remain highly uncertain and difficult to quantify. We show that part of this uncertainty is caused by topographical influences on clouds, that is, those arising from land–water contrasts. This is demonstrated using satellite retrievals in regions with extensive river networks, such as the Amazon Basin. 15 years of MODerate resolution Imaging Spectroradiometer (MODIS) satellite data show cloud formation over the Amazon River basin is suppressed by 26% with warm low clouds above the river exhibiting a 22% smaller droplet effective radius and 18% higher droplet concentration ($N_d$) compared to adjacent land clouds. Thus, clouds above the river may appear polluted but are actually influenced by river-breeze circulations driven by the thermal contrast between the river and the surrounding land. These responses are robust in both wet and dry seasons, and tests using an improved MODIS retrieval product show cloud differences are unlikely due to retrieval artifacts. In situ measurements from the Green Ocean Amazon Experiment (GoAmazon) confirm that $N_d$ is elevated above rivers and are also higher when carbon monoxide concentrations are elevated near the large city of Manaus. Lagrangian airmass tracking over Manaus shows that regional-scale river-breeze circulations impact $N_d$ as much as the urban aerosol plume, complicating ACI attribution and highlighting the need to isolate land-surface effects to assess ACI in continental regions.

Aerosol-Cloud Interactions↗

MISR Level 1B2 Terrain Data (MI1B2T_V1)

The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally gaussian, centered at 443, 555, 670, and 865 nm. The Terrain data are re-projected to the terrain altitude. In this product, surface data from all cameras will appear in the same geographic location. Thus, this product is the primary input to Level 2 aerosol/surface processing, which requires co-registration of the L1B2 imagery at the surface. Clouds will still be displaced due to their elevation above the surface, but this time with respect to the terrain rather than the ellipsoid. (The mountain location T is now assigned the geographic location at T, and the Cloud at F appears at the geographic location T.) In Level 2 aerosol/surface processing, algorithms are applied to screen out the clouds. Terrain data only exist for MISR blocks containing some land. [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=563.2 km (cross-track); Longitude_Resolution=140.8 km (along-track).; Temporal_Resolution=about 15 orbits/day; Temporal_Resolution_Range=about 15 orbits/day].

NEAR IR WAVELENGTHS↗

MISR Level 1B2 Terrain Data (MI1B2T_V2)

The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally gaussian, centered at 443, 555, 670, and 865 nm. The Terrain data are re-projected to the terrain altitude. In this product, surface data from all cameras will appear in the same geographic location. Thus, this product is the primary input to Level 2 aerosol/surface processing, which requires co-registration of the L1B2 imagery at the surface. Clouds will still be displaced due to their elevation above the surface, but this time with respect to the terrain rather than the ellipsoid. (The mountain location T is now assigned the geographic location at T, and the Cloud at F appears at the geographic location T.) In Level 2 aerosol/surface processing, algorithms are applied to screen out the clouds. Terrain data only exist for MISR blocks containing some land. [Location=GLOBAL LAND] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=563.2 km (cross-track); Longitude_Resolution=140.8 km (along-track).; Temporal_Resolution=about 15 orbits/day; Temporal_Resolution_Range=about 15 orbits/day].

SPECTRAL BANDS↗

MISR Level 1B2 Terrain Data (MI1B2T_V3)

The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally gaussian, centered at 443, 555, 670, and 865 nm. The Terrain data are re-projected to the terrain altitude. In this product, surface data from all cameras will appear in the same geographic location. Thus, this product is the primary input to Level 2 aerosol/surface processing, which requires co-registration of the L1B2 imagery at the surface. Clouds will still be displaced due to their elevation above the surface, but this time with respect to the terrain rather than the ellipsoid. (The mountain location T is now assigned the geographic location at T, and the Cloud at F appears at the geographic location T.) In Level 2 aerosol/surface processing, algorithms are applied to screen out the clouds. Terrain data only exist for MISR blocks containing some land. [Location=GLOBAL LAND] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=563.2 km (cross-track); Longitude_Resolution=140.8 km (along-track).; Temporal_Resolution=about 15 orbits/day; Temporal_Resolution_Range=about 15 orbits/day].

IMAGERY↗

Influence of Transient Atmospheric Circulation on the Surface Heating of the Pacific Warm Pool

Analyses of data on clouds, winds, and surface heat fluxes show that the transient behavior of basin-wide large-scale circulation has a significant influence on the warm pool sea surface temperature (SST). Trade winds converge to regions of the highest SST in the equatorial western Pacific. These regions have the largest cloud cover and smallest wind speed. Both surface solar heating and evaporative cooling are weak. The reduced evaporative cooling due to weakened winds exceeds the reduced solar heating due to enhanced cloudiness. The result is a maximum surface heating in the strong convective and high SST regions. Data also show that the maximum surface heating in strong convective regions is interrupted by transient atmospheric and oceanic circulation. Due to the seasonal variation of the insolation at the top of the atmosphere, trade winds and clouds also experience seasonal variations. Regions of high SST and low-level convergence follow the Sun, where the surface heating is a maximum. As the Sun moves away from a convective region, the strong trade winds set in, and the evaporative cooling enhances, resulting in a net cooling of the surface. During an El Nino, the maximum SST and convective region shifts eastward from the maritime continent to the equatorial central Pacific. Following the eastward shift of the maximum SST, the region of maximum cloudiness and surface heating also shift eastward. As the atmospheric and oceanic circulation returns to normal situations, the trade winds increase and the surface heating decreases. We conclude that the evaporative cooling associated with the seasonal and interannual variations of trade winds is one of the major factors that modulate the SST distribution of the Pacific warm pool.

Chou, Ming-Dah↗

Analysis of spatial inhomogeneities in cumulus clouds using high spatial resolution Landsat data

Aircraft observations and high resolution Landsat MSS digital data are used to determine the sizes of spatial inhomogeneities ('holes') in cumulus clouds. The majority of holes are found near cloud edges, but the larger holes tend to be found in cloud interiors. Aircraft measurements show these cloud spatial inhomogeneities in the range of 100 to 500 m, while Landsat data show them in the range of 100 m to 3 km. The number of holes per cloud decreases exponentially with increasing hole diameter. Small clouds not only have smaller holes, but also fewer holes than large clouds. Large clouds have large holes in them, as well as large numbers of the smaller holes. The total cloud area occupied by holes increases with increasing cloud size.

Parker, Lindsay↗

Physics-Guided Machine Learning for Prediction of Cloud Properties in Satellite-Derived Solar Data

With over 20 years of high-resolution surface irradiance data covering most of the western hemisphere, the National Solar Radiation Database (NSRDB) is a vital public data asset. The NSRDB uses a two-step Physical Solar Model (PSM) that explicitly considers the effects of clouds and other atmospheric variables on radiative transfer. High-quality physical and optical cloud properties derived from satellite imagery are perhaps the most important data inputs to the PSM, representing the greatest source of radiation attenuation and scattering. However, traditional methods for cloud property retrieval have their own limitations and are unable to accurately predict cloud properties outside of nominal conditions. We introduce a physics-guided neural network that can accurately predict cloud properties when traditional methods fail or are inaccurate. Using this framework, we show reductions in relative Root Mean Square Error (RMSE) for Global Horizontal Irradiance (GHI) up to 13 percentage points for timesteps that previously had missing or low-quality cloud property data. We expect that this methodology will be effective in improving the quality of cloud property and solar irradiance data in the NSRDB.

cloud properties↗