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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
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NASA NTRS2002

Variability of Clouds Over a Solar Cycle

One of the most controversial aspects of climate studies is the debate over the natural and anthropogenic causes of climate change. Historical data strongly suggest that the Little Ice Age (from 1550 to 1850 AD when the mean temperature was colder by about 1 C) was most likely caused by variability of the sun and not greenhouse molecules (e.g., CO2). However, the known variability in solar irradiance and modulation of cosmic rays provides too little energy, by many orders of magnitude, to lead to climate changes in the troposphere. The conjecture is that there is a 'trigger mechanism'. This idea may now be subjected to a quantitative test using recent global datasets. Using the best available modern cloud data from International Satellite Cloud Climatology Project (ISCCP), Svensmark and Friis-Christensen found a correlation of a large variation (3-4%) in global cloud cover with the solar cycle. The work has been extended by Svensmark and Marsh and Svensmark. The implied forcing on climate is an order of magnitude greater than any previous claims. Are clouds the long sought trigger mechanism? This discovery is potentially so important that it should be corroborated by an independent database, and, furthermore, it must be shown that alternative explanations (i.e., El Nino) can be ruled out. We used the ISCCP data in conjunction with the Total Ozone Mapping Spectrometer (TOMS) data to carry out in in depth study of the cloud trigger mechanism.

Yung, Yuk L.↗
DOE OSTI2022

Long- and short-term temporal variability in cloud condensation nuclei spectra over a wide supersaturation range in the Southern Great Plains site

Abstract. When aerosol particles seed the formation of liquid water droplets in the atmosphere, they are called cloud condensation nuclei (CCN). Different aerosols will act as CCN under different degrees of water supersaturation (relative humidity above 100 %), depending on their size and composition. In this work, we build and analyze a best-estimate CCN spectrum product, tabulated at ∼ 45 min resolution, generated using high quality data from seven independent instruments at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site. The data product spans a large supersaturation range, from 0.0001 % to ∼ 30 %, and time period of 5 years, from 2009–2013, and is available on the ARM data archive. We leverage this added statistical power to examine relationships that are unclear in smaller datasets. Our analysis is performed in three main areas. First, probability distributions of many aerosol and CCN metrics are found to exhibit skewed log-normal distribution shapes. Second, clustering analyses of CCN spectra reveal that the primary drivers of CCN differences are aerosol number size distributions, rather than hygroscopicity or composition, especially at supersaturations above 0.2 %, while also allowing for a simplified understanding of seasonal and diurnal variations in CCN behavior. The predictive ability of using limited hygroscopicity data with accurate number size distributions to estimate CCN spectra is investigated, and the uncertainties of this approach are estimated. Third, the dynamics of CCN spectral clusters and concentrations are examined with cross-correlation and autocorrelation analyses. We find that CCN concentrations change rapidly on the timescale of 1–3 h, with some conservation beyond that which is greatest for the lower supersaturation region of the spectrum.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1986

A laboratory investigation of the variability of cloud reflected radiance fields

A method to determine the radiative properties of complex cloud fields was developed. A Cloud field optical simulator (CFOS) was constructed to simulate the interaction of cloud fields with visible radiation. The CFOS was verified by comparing experimental results from it with calculations performed with a Monte Carlo radiative transfer model. A software library was developed to process, reduce, and display CFOS data. The CFSOS was utilized to study the reflected radiane patterns from simulated cloud fields.

Mckee, T. B.↗
DOE OSTI2021

Quasi-Two-Day and Diurnal Cloud Variation Timescales over Convectively Active Regions

Climatological features of the cloud variability on quasi-two-day (Q2D) and diurnal cycle (DC) timescales are investigated by utilizing the high-resolution satellite infrared brightness temperature (IRBT) observations from January 1998 to December 2019. A distinct land-sea contrast between the distributions of Q2D and DC signals is evident. Diurnally driven cloud activity mainly occurs over land and mountainous regions, and the Q2D timescale is more prominent over tropical ocean basins and land where organized convection is usually observed, e.g., Congo and Amazon Rainforests, the U.S. and subtropical South America during warm seasons. The long-term relationship between the Q2D variability and sea surface temperature (SST) shows that the clouds are more active on Q2D timescales over higher SST environments. The Q2D variability correlates well with both the Indian Ocean Dipole (IOD) and El Niño/Southern Oscillation (ENSO) from 1998 to 2019. The cloud variability associated with a range of convective available potential energy (CAPE) values is analyzed. The result over land shows that increased Q2D cloud variability emerges with higher CAPE, suggesting the coincidence of Q2D and organized convection, particularly given that this effect is strongest over regions with frequent mesoscale convective systems (MCSs) around the world. The cloud variability and the Q2D timescale analyses provide an alternative perspective to understand the global features of mesoscale convective systems. Overall, this study objectively examines the global variability of convective timescales related to the diurnal cycle and longer-lived convective systems to provide a greater understanding of how the global convection population varies in space and time.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

A Polar Specific 20-year Data Set of Cloud Fraction and Height Derived from Satellite Radiances

This is a final report to fulfill reporting requirements on NASA grant NASA NAG5-11800. Jennifer Francis, PI at Rutgers University is currently continuing work on this project under a no-cost extension. Work at the University of Washington portion of the project is completed and reported here. Major accomplishments and results from this portion of the project include: 1) Extension and reprocessing of TOVS Polar Pathfinder (Path-P) data set; 2) Analysis of Arctic cloud variability; 3) Validation of Southern Hemisphere ocean cloud retrievals; 4) Intercompared cloud height information from AVHRR retrievals and surface-based cloud radar information.

Francis, Jennifer↗
NASA NTRS2021

Cloud Detection over Sunglint Regions with Observations from the Earth Polychromatic Imaging Camera (EPIC)

With the ability to observe the entire sunlit side of the Earth, EPIC data have become an important resource for studying cloud daily variability. Inaccurate cloud masking is a great source of uncertainty. One main region that is prone to error in cloud masking is the sunglint area over ocean surfaces. Cloud detection over these regions is challenging for the EPIC instrument because of its limited spectral channels. Clear sky ocean surface reflectance from visible channels over sunglint is much larger than that over the non-glint areas and can exceed reflectance from thin clouds. This paper presents an improved EPIC ocean cloud masking algorithm (Version 3). Over sunglint regions (glint angle ≤ 25°), the algorithm utilizes EPIC’s oxygen (O2) A-band ratio (764nm to 780 nm) in addition to the 780 nm reflectance observations in masking tests. Outside the sunglint regions, a dynamic reflectance threshold for the Rayleigh corrected 780 nm reflectance is applied. The thresholds are derived as a function of glint angle. When compared with co-located data from the geosynchronous Earth orbit (GEO) and the low Earth orbit (LEO) observations, the consistency of the new ocean cloud mask algorithm has increased by 4~10% and 4~6% in the glint center and granule edges respectively. The false positive rate is reduced by 10~17%. Overall global ocean cloud detection consistency increases by 2%. This algorithm, along with other improvements to the EPIC cloud masks, has been implemented in the EPIC cloud products Version 3. This algorithm will improve the cloud daily variability analysis by removing the artificial peak at local noon time in the glint center latitudes and reducing biases in the early morning and late afternoon cloud fraction over ocean surfaces.

EPIC↗
NASA NTRS2021

Cirrus Cloud Top-of-the-Atmosphere Net Daytime Forcing in the Alaskan Subarctic from Ground-Based MPLNET Monitoring

Cirrus cloud daytime top-of-the-atmosphere radiative forcing (TOA CRF) is estimated for a two-year NASA Micro-Pulse Lidar Network (532 nm; MPLNET) dataset collected at Fairbanks, Alaska. Two-year averaged daytime TOA CRF is estimated at between -1.08 and 0.78 W·m-2 (-0.49 to 1.10 W·m-2 in 2017, and -1.67 to 0.47 W·m-2 in 2018). This subarctic study completes a now trilogy of MPLNET ground-based cloud forcing investigations, following midlatitude and tropical studies by Campbell et al. (2016; C16) at Greenbelt, Maryland and Lolli et al. (2017) at Singapore. C16 hypothesize a global meridional daytime TOA CRF gradient that begins positive at the equator (2.20 – 2.59 W·m-2 over land and -0.46 – 0.42 W·m-2 over ocean at Singapore), becomes neutral in the midlatitudes (0.03 – 0.27 W·m-2 over land in Maryland) and turns negative moving poleward. This study does not completely confirm C16, as values are not found as exclusively negative. Evidence in historical reanalysis data suggests that daytime cirrus forcing in and around the subarctic likely once was exclusively negative. Increasing tropopause heights, inducing higher and colder cirrus, have likely increased regional forcing over the last forty years. We hypothesize that subarctic inter-annual cloud variability is likely a considerable influence on global cirrus cloud forcing sensitivity, given the irregularity of polar versus midlatitude synoptic weather intrusions. This study and hypothesis lays basis for an extrapolation of these MPLNET experiments to satellite-based lidar cirrus cloud datasets.

James R Campbell↗
NASA NTRS1985

The albedo field and cloud radiative forcing produced by a general circulation model with internally generated cloud optics

A general circulation model (GCM) study is presented in which cloud radiative properties are computed from cloud liquid water content inferred from the GCM hydrological cycle. Model-generated and satellite albedos are in rough agreement. Analysis of the cloud radiative forcing indicates that cloud albedo effects overcome cloud infrared opacity effects in most regions. Both computed and observed albedo of clouds decrease from low to high altitudes. The model with variable cloud optics produces significantly different regional albedos from the same one with fixed cloud optics, especially over the tropics. The cloud droplet size distribution also has a significant impact on the model albedos. The temperature of the tropical upper troposphere is somewhat sensitive to the microphysical characteristics of the model cirrus clouds.

Charlock, T. P.↗
DOE OSTI2021

Evaluating the Lagrangian Evolution of Subtropical Low Clouds in GCMs Using Observations: Mean Evolution, Time Scales, and Responses to Predictors

In this work, a Lagrangian framework is developed to show the daily-scale time evolution of low clouds over the eastern subtropical oceans. An identical framework is applied to two general circulation models (GCMs): the CAM5 and UKMET and a set of satellite observations. This approach follows thousands of parcels as they advect downwind in the subtropical trade winds, comparing cloud evolution in time and space. This study tracks cloud cover, in-cloud liquid water path (CLWP), droplet concentration $N_d$, planetary boundary layer (PBL) depth, and rain rate as clouds transition from regions with predominately stratiform clouds to regions containing mostly trade cumulus. The two models generate fewer clouds with greater $N_d$ relative to observations. Models show stronger Lagrangian cloud cover decline and greater PBL deepening when compared with observations. In comparing frequency distributions of cloud variables over time, it is seen that models generate increasing frequencies of nearly clear conditions at the expense of overcast conditions, whereas observations show transitions from overcast to cloud amounts between 50% and 90%. Lagrangian decorrelation time scales ($\mathcal{e}$-folding time $\tau$) of cloud cover and CLWP are between 11 and 19 h for models and observations, although they are a bit shorter for models. A Lagrangian framework applied here resolves and compares the time evolution of cloud systems as they adjust to environmental perturbations in models and observations. Increasing subsidence in the overlying troposphere leads to declining cloud cover, CLWP, PBL depth, and rain rates in models and observations. Modeled cloud responses to other meteorological variables are less consistent with observations, suggesting a need for continuing mechanical improvements in GCMs.

58 GEOSCIENCES↗
NASA NTRS1982

Temporal variability of ultraviolet cloud features in the Venus stratosphere

The observed temporal variability of ultraviolet cloud brightness in over two months of Pioneer Venus Orbiter Cloud Photopolarimeter imaging is considered. It is found that the longitudinal mean brightness record shows evidence of two planetary-scale waves. One appears to be centered on the equator but has significant amplitude at all latitudes. The other is centered at midlatitudes and is more confined latitudinally. Visually, the two waves correspond to the longitudinal motion of the dark equatorial band and the oscillation in latitude of the bright polar bands, respectively. The slope of the longitudinal cloud brightness power spectrum at intermediate wavenumbers fluctuates with a periodicity of approximately 2-3 weeks in southern midlatitudes. The polar regions in both hemispheres vary in brightness on a time scale of approximately two months. The disk-integrated brightness increased with time throughout the nominal mission.

Del Genio, A. D.↗
DOE OSTI2021

Improving Prediction of Surface Solar Irradiance Variability by Integrating Observed Cloud Characteristics and Machine Learning

A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.

Riihimaki, Laura↗
NASA NTRS2001

Influence of 3D Radiative Effects on Satellite Retrievals of Cloud Properties

When cloud properties are retrieved from satellite observations, the calculations apply 1D theory to the 3D world: they only consider vertical structures and ignore horizontal cloud variability. This presentation discusses how big the resulting errors can be in the operational retrievals of cloud optical thickness. A new technique was developed to estimate the magnitude of potential errors by analyzing the spatial patterns of visible and infrared images. The proposed technique was used to set error bars for optical depths retrieved from new MODIS measurements. Initial results indicate that the 1 km resolution retrievals are subject to abundant uncertainties. Averaging over 50 by 50 km areas reduces the errors, but does not remove them completely; even in the relatively simple case of high sun (30 degree zenith angle), about a fifth of the examined areas had biases larger than ten percent. As expected, errors increase substantially for more oblique illumination.

Varnai, Tamas↗
NASA NTRS1993

Inference of cirrus cloud properties using satellite-observed visible and infrared radiances. I - Parameterization of radiance fields

The impact of using phase functions for spherical droplets and hexagonal ice crystals to analyze radiances from cirrus is examined. Adding-doubling radiative transfer calculations are employed to compute radiances for different cloud thicknesses and heights over various backgrounds. These radiances are used to develop parameterizations of top-of-the-atmosphere visible reflectance and IR emittance using tables of reflectances as a function of cloud optical depth, viewing and illumination angles, and microphysics. This parameterization, which includes Rayleigh scattering, ozone absorption, variable cloud height, and an anisotropic surface reflectance, reproduces the computed top-of-the-atmosphere reflectances with an accruacy of +/- 6 percent for four microphysical models: 10-micron water droplet, small symmetric crystal, cirrostratus, and cirrus uncinus. The accuracy is twice that of previous models.

Minnis, Patrick↗
NASA NTRS2003

Extraction of Profile Information from Cloud Contaminated Radiances

Clouds act to reduce the signal level and may produce noise dependence on the complexity of the cloud properties and the manner in which they are treated in the profile retrieval process. There are essentially three ways to extract profile information from cloud contaminated radiances: (1) cloud-clearing using spatially adjacent cloud contaminated radiance measurements, (2) retrieval based upon the assumption of opaque cloud conditions, and (3) retrieval or radiance assimilation using a physically correct cloud radiative transfer model which accounts for the absorption and scattering of the radiance observed. Cloud clearing extracts the radiance arising from the clear air portion of partly clouded fields of view permitting soundings to the surface or the assimilation of radiances as in the clear field of view case. However, the accuracy of the clear air radiance signal depends upon the cloud height and optical property uniformity across the two fields of view used in the cloud clearing process. The assumption of opaque clouds within the field of view permits relatively accurate profiles to be retrieved down to near cloud top levels, the accuracy near the cloud top level being dependent upon the actual microphysical properties of the cloud. The use of a physically correct cloud radiative transfer model enables accurate retrievals down to cloud top levels and below semi-transparent cloud layers (e.g., cirrus). It should also be possible to assimilate cloudy radiances directly into the model given a physically correct cloud radiative transfer model using geometric and microphysical cloud parameters retrieved from the radiance spectra as initial cloud variables in the radiance assimilation process. This presentation reviews the above three ways to extract profile information from cloud contaminated radiances. NPOESS Airborne Sounder Testbed-Interferometer radiance spectra and Aqua satellite AIRS radiance spectra are used to illustrate how cloudy radiances can be used in the profile retrieval process.

Smith, W. L.↗