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NASA NTRS2023

Surface Shortwave Radiative Fluxes Derived from the U.S. Air Force Cloud Depiction Forecast System World-Wide Merged Cloud Analysis

We present a global-scale evaluation of surface shortwave (SW↓) radiative fluxes as derived with cloud amount information from the U.S. Air Force (USAF) Cloud Depiction Forecast System (CDFS) II World-Wide Merged Cloud Analysis (WWMCA) and implemented in the framework of the NASA Land Information System (LIS). Evaluation of this product is done against ground observations, a satellite-based product from the Moderate Resolution Imaging Spectroradiometer (MODIS), and several reanalysis outputs. While the LIS/USAF product tends to overestimate the SW↓ fluxes when compared to ground observations and satellite estimates, its performance is comparable or better than the following reanalysis products: ERA5, CFSR, and MERRA-2. Results are presented using all available observations over the globe and independently for several regional domains of interest. When evaluated against ground observations over the globe, the bias in the LIS/USAF product at daily time scale was about 9.34 W m −2 and the RMS was 29.20 W m −2 while over the United States the bias was about 10.65 W m −2 and the RMS was 35.31 W m −2 . The sample sizes used were not uniform over the different regions, and the quality of both ground truth and the outputs of the other products may vary regionally. It is important to note that the LIS/USAF is a near-real-time (NRT) product of interest for potential users and as such fills a need that is not met by most products. Due to latency issues, the level of observational inputs in the NRT product is less than in the reanalysis data.

Cloud radiative effects↗

NASA NTRS2017

Archive Management of NASA Earth Observation Data to Support Cloud Analysis

NASA collects, processes and distributes petabytes of Earth Observation (EO) data from satellites, aircraft, in situ instruments and model output, with an order of magnitude increase expected by 2024. Cloud-based web object storage (WOS) of these data can simplify the execution of such an increase. More importantly, it can also facilitate user analysis of those volumes by making the data available to the massively parallel computing power in the cloud. However, storing EO data in cloud WOS has a ripple effect throughout the NASA archive system with unexpected challenges and opportunities. One challenge is modifying data servicing software (such as Web Coverage Service servers) to access and subset data that are no longer on a directly accessible file system, but rather in cloud WOS. Opportunities include refactoring of the archive software to a cloud-native architecture; virtualizing data products by computing on demand; and reorganizing data to be more analysis-friendly.

Lynnes, Christopher↗

NASA NTRS2017

Archive Management of NASA Earth Observation Data to Support Cloud Analysis

Lynnes, Christopher↗

NASA NTRS1975

Stereographic cloud analysis from Apollo 6 photographs over a cold front

Explore the source record for details and available documents.

Shenk, W. E.↗

NASA NTRS1978

Cloud microphysics - Analysis of the clouds of Earth, Venus, Mars, and Jupiter

The probable microphysics of a cloud is deduced by a described method which requires information on cloud particle mean size, composition, number density, and atmospheric structure, but does not require any additional information. The analysis is applied to the sulfuric acid clouds of Venus, the water ice and dust clouds of Mars, and the ammonia-water and ammonia ice clouds of Jupiter. The Venus cloud layer is found to have some resemblance to smog and haze layers on earth. The water ice clouds on Mars resemble tenuous nonprecipitating cirrus clouds on earth. Precipitation, vertical distribution of gases, and vertical transport of heat in the clouds on Jupiter are considered.

Rossow, W. B.↗

NASA NTRS2004

MODIS Measurements of the Effect of Aerosol on Clouds

Analysis of MODIS data of aerosol and clouds over the Atlantic Ocean, the Pacific and Indian Ocean show that the different types of aerosol affect not only the cloud microphysics but also the cloud fraction to a large extend. Analysis of detailed measurements and multiple regression analysis will be shown.

Kaufman, Y.↗

NASA NTRS2014

Sport Transition of JPSS VIIRS Imagery for Night-time Applications

The NASA/Short‐term Prediction, Research, and Transition (SPoRT) Program and NOAA/Cooperative Institute for Research in the Atmosphere (CIRA) work within the NOAA/Joint Polar Satellite System (JPSS) Proving Ground to demonstrate the unique capabilities of the VIIRS instrument. Very similar to MODIS, the VIIRS instrument provides many high‐resolution visible and infrared channels in a broad spectrum. In addition, VIIRS is equipped with a low‐light sensor that is able to detect light emissions from the land and atmosphere as well as reflected sunlight by the lunar surface. This band is referred to as the Day‐Night Band due to the sunlight being used at night to see cloud and topographic features just as one would typically see in day‐time visible imagery. NWS forecast offices that collaborate with SPoRT and CIRA have utilized MODIS imagery in operations, but have longed for more frequent passes of polar‐orbiting data. The VIIRS instrument enhances SPoRT collaborations with WFOs by providing another day and night‐time pass, and at times two additional passes due to its large swath width. This means that multi‐spectral, RGB imagery composites are more readily available to prepare users for their use in GOES‐R era and high‐resolution imagery for use in high‐latitudes is more frequently able to supplement standard GOES imagery within the SPoRT Hybrid GEO‐LEO product. The transition of VIIRS also introduces the new Day‐Night Band capability to forecast operations. An Intensive Evaluation Period (IEP) was conducted in Summer 2013 with a group of "Front Range" NWS offices related to VIIRS night‐time imagery. VIIRS single‐channel imagery is able to better analyze the specific location of fire hotspots and other land features, as well as provide a more true measurement of various cloud and aerosol properties than geostationary measurements, especially at night. Viewed within the SPoRT Hybrid imagery, the VIIRS data allows forecasters to better interpret the more frequent, but coarse GOES Imagery. Night‐time Microphysics and Dust RGB Imagery provides cloud analysis of cloud height, thickness, and composition in order for operational applications such as separating fog from low clouds, dust plume detection, and determining precipitating clouds in radar-void/ blocked regions. The Day‐Night Band has a particular benefit to seeing light from cities, fires, or other emissions as well as the reflection of moonlight off of clouds and smoke plumes, given the right lunar phase and angle. Examples from the VIIRS transition and IEP will be presented.

Fuell, Kevin↗

NASA NTRS1990

An analysis of cloud property retrieval using HIRS-2 and AVHRR radiometric data

The principal advantage of the HIRS cloud height retrieval is to provide an unbiased estimate of cloud height for optically thin, high cloud. This capability is employed to examine the relationship of cirrus visible reflectance to thermal IR emittance. By combining data from the HIRS-2 and AVHRR instruments on the NOAA operational satellites the proposed method attempts to improve the ability of existing satellite radiometers to recover information on middle-level and high-level clouds. This combination of data sets leads to an improved cloud recovery algorithm for the detection and analysis of cirrus clouds.

Baum, Bryan A.↗

NASA NTRS1984

Search for solar signals in GOES imagery and cloud climatology analysis

The digitization and analysis of 16mm GOES IR imagery to identify spatial and temporal variation in clouds is discussed, along with a cloud climatology analysis. Recently added to the Interactive Graphics Package was a Computer Image Analysis System. This system allows many new imaging techniques. One of the possibilities is the analysis of overall light intensity variations in six 10 degree square boxes across the intertropical convergence zone. This technique is discussed, as is a preliminary analysis of unnormalized data.

Markson, R. J.↗

NASA NTRS1991

Spectral properties of fog over the Malaspina Glacier, Alaska, in comparison to snow, ice, and clouds

Analysis of Landsat 5 Thematic Mapper (TM) data of the Malaspina Glacier in southeastern Alaska has shown that fog overlying the glacier ice has reflectance characteristics similar to the ice below and that the spectral reflectance of fog can be different from other types of clouds. Fog is more reflective in the visible and near-infrared wavelengths compared to snow, ice, and cumulus clouds. The differentiation between clouds, fog, and the ice below can be enhanced by combining TM bands in the visible part of the spectrum.

Ormsby, James P.↗

NASA NTRS2016

Extending "Deep Blue" Aerosol Retrieval Coverage to Cases of Absorbing Aerosols Above Clouds: Sensitivity Analysis and First Case Studies

Cases of absorbing aerosols above clouds (AACs), such as smoke or mineral dust, are omitted from most routinely processed space-based aerosol optical depth (AOD) data products, including those from the Moderate Resolution Imaging Spectroradiometer (MODIS). This study presents a sensitivity analysis and preliminary algorithm to retrieve above-cloud AOD and liquid cloud optical depth (COD) for AAC cases from MODIS or similar sensors, for incorporation into a future version of the "Deep Blue" AOD data product. Detailed retrieval simulations suggest that these sensors should be able to determine AAC AOD with a typical level of uncertainty approximately 25-50 percent (with lower uncertainties for more strongly absorbing aerosol types) and COD with an uncertainty approximately10-20 percent, if an appropriate aerosol optical model is known beforehand. Errors are larger, particularly if the aerosols are only weakly absorbing, if the aerosol optical properties are not known, and the appropriate model to use must also be retrieved. Actual retrieval errors are also compared to uncertainty envelopes obtained through the optimal estimation (OE) technique; OE-based uncertainties are found to be generally reasonable for COD but larger than actual retrieval errors for AOD, due in part to difficulties in quantifying the degree of spectral correlation of forward model error. The algorithm is also applied to two MODIS scenes (one smoke and one dust) for which near-coincident NASA Ames Airborne Tracking Sun photometer (AATS) data were available to use as a ground truth AOD data source, and found to be in good agreement, demonstrating the validity of the technique with real observations.

aerosols above clouds↗

NASA NTRS1964

Cloud pattern analysis by interpreters

Ability of human interpreters to analyze and describe cloud patterns in photographs obtained by Tiros satellites

INTERPRETATION↗

NASA NTRS1985

A new NASA/MSFC mission analysis global cloud cover data base

A global cloud cover data set, derived from the USAF 3D NEPH Analysis, was developed for use in climate studies and for Earth viewing applications. This data set contains a single parameter - total sky cover - separated in time by 3 or 6 hr intervals and in space by approximately 50 n.mi. Cloud cover amount is recorded for each grid point (of a square grid) by a single alphanumeric character representing each 5 percent increment of sky cover. The data are arranged in both quarterly and monthly formats. The data base currently provides daily, 3-hr observed total sky cover for the Northern Hemisphere from 1972 through 1977 less 1976. For the Southern Hemisphere, there are data at 6-hr intervals for 1976 through 1978 and at 3-hr intervals for 1979 and 1980. More years of data are being added. To validate the data base, the percent frequency of or = 0.3 and or = 0.8 cloud cover was compared with ground observed cloud amounts at several locations with generally good agreement. Mean or other desired cloud amounts can be calculated for any time period and any size area from a single grid point to a hemisphere. The data base is especially useful in evaluating the consequence of cloud cover on Earth viewing space missions. The temporal and spatial frequency of the data allow simulations that closely approximate any projected viewing mission. No adjustments are required to account for cloud continuity.

Brown, S. C.↗

NASA NTRS1984

Towards the objective analysis of clouds from satellite imagery data

It is suspected that clouds play a major role in climate dynamics. However, conclusive studies regarding the effects related to the cloud cover appear difficult because there is a lack of objective data. The present investigation is concerned with an objective scheme for deriving clouds and their properties from satellite imagery data for the oceans. The objective analysis makes use of the spatial coherence method for retrieving cloud cover from satellite imagery data. This method has advantages over other techniques often applied to imagery data. It is not necessary that clouds fill completely the observing instrument's field-of-view, and a priori or satellite derived knowledge of the cloud radiative properties is not needed.

Coakley, J. A., Jr.↗

NASA NTRS1990

Cloud cover analysis with Arctic Advanced Very High Resolution Radiometer data. II - Classification with spectral and textural measures

The spectral and textural characteristics of polar clouds and surfaces for a 7-day summer series of AVHRR data in two Arctic locations are examined, and the results used in the development of a cloud classification procedure for polar satellite data. Since spatial coherence and texture sensitivity tests indicate that a joint spectral-textural analysis based on the same cell size is inappropriate, cloud detection with AVHRR data and surface identification with passive microwave data are first done on the pixel level as described by Key and Barry (1989). Next, cloud patterns within 250-sq-km regions are described, then the spectral and local textural characteristics of cloud patterns in the image are determined and each cloud pixel is classified by statistical methods. Results indicate that both spectral and textural features can be utilized in the classification of cloudy pixels, although spectral features are most useful for the discrimination between cloud classes.

Key, J.↗

NASA NTRS2011

Performance Analysis of Cloud Computing Architectures Using Discrete Event Simulation

Cloud computing offers the economic benefit of on-demand resource allocation to meet changing enterprise computing needs. However, the flexibility of cloud computing is disadvantaged when compared to traditional hosting in providing predictable application and service performance. Cloud computing relies on resource scheduling in a virtualized network-centric server environment, which makes static performance analysis infeasible. We developed a discrete event simulation model to evaluate the overall effectiveness of organizations in executing their workflow in traditional and cloud computing architectures. The two part model framework characterizes both the demand using a probability distribution for each type of service request as well as enterprise computing resource constraints. Our simulations provide quantitative analysis to design and provision computing architectures that maximize overall mission effectiveness. We share our analysis of key resource constraints in cloud computing architectures and findings on the appropriateness of cloud computing in various applications.

Stocker, John C.↗

NASA NTRS1990

A cloud classification scheme applied to the breakup region of marine stratocumulus

A major goal of the marine stratocumulus (MSc) segment of FIRE is to describe and explain the temporal and spatial variability in fractional cloud cover. The challenge from a theoretical standpoint is to correctly represent the mechanisms leading to the transitions between solid stratus, stratocumulus and trade wind cumulus. The development and testing of models accounting for fractional cloudiness require an observational data base that will come primarily from satellites. This, of course, is one of the missions of the ISCCP. There are a number of satellite cloud analysis programs that are being undertaken as part of FIRE. One that has already produced data from the FIRE MSc experiment is the spatial coherence method (COAKLEY and Baldwin, 1984). This method produces information on fractional cloud coverage and cloud heights. It may be possible, however, to extract more information on cloud structure from satellite data that might be of use in describing the transitions in the marine stratocumulus cloud deck. Potential applications are explored of a cloud analysis scheme relying on more detailed analysis of visible and infrared cloud radiance statistics. For this preliminary study, data is examined from three days during the 1987 FIRE MSc field work. These case studies provide a basis for comparison and evaluation of the technique.

Khalsa, Siri Jodha Singh↗

NASA NTRS1975

Analysis of cloud polarization measurements

The polarization of sunlight reflected by various cloud types was measured in the near-infrared with a polarimeter flown aboard the NASA Convair 990. The data reduction has been completed and detailed comparisons have been made between the observations and theoretical computations. The analysis presented shows that the polarization in the near-infrared can be used on a routine basis to reliably yield the cloud particle phase (water or ice) and the average particle size in the top part of water clouds (usually within + or - 25%). In some cases the polarization also yields the cloud optical thickness (or a lower limit) and a measure of the width of the particle size distribution in the top of the water clouds.

Hansen, J. E.↗