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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Cloud Detection Using Measured and Modeled State Parameters

In this study, hourly RUC analyses were used to examine the differences between RH and temperature values from RUC reanalysis data and from radiosonde atmospheric profiles obtained at the ARM SCF. The results show that the temperature observations from the SONDE and RUC are highly correlated. The RHs are also well-correlated, but the SONDE values generally exceed those from RUC. Inside cloud layers, the RH from RUC is 2-14% lower than the RH from SONDE for all RUC layers. Although the layer mean RH within clouds is much greater than the layer mean RH outside cloud or in the clear-sky, RH thresholds chosen as a function of temperature can more accurately diagnose cloud occurrence for either dataset. For overcast clouds, it was found that the 50% probability RH threshold for diagnosing a cloud, within a given upper tropospheric layer is roughly 90% for the Vaisala RS80-15LH radisonde and 80% for RUC data. While for the partial cloud (cloud amount is less than 90%), the RH thresholds of SONDE are close to RUC for a given probability in upper tropospheric layers. The probabilities of detecting clouds at a given RH and temperature should be useful for a variety of application such as the development of new cloud parameterizations or for estimating the vertical profile of cloudiness underneath a given cloud observed from the satellite to construct a 3-D cloud data set for computing atmospheric radiative heating profiles or determining potential aircraft icing conditions.

Yi, Y.↗
NASA NTRS2001

A Study of the Response of Deep Tropical Clouds to Mesoscale Processes: Modeling Strategies and Simulations of TOGA-COARE Convective Systems - Part 1

Interactions between deep tropical clouds over the western Pacific warm pool and the larger-scale environment are key to understanding climate change. Cloud models are an extremely useful tool in simulating and providing statistical information on heat and moisture transfer processes between cloud systems and the environment, and can therefore be utilized to substantially improve cloud parameterizations in climate models. In this paper, the Goddard Cumulus Ensemble (GCE) cloud-resolving model is used in multi-day simulations of deep tropical convective activity over the Tropical Ocean-Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE). Large-scale temperature and moisture advective tendencies, and horizontal momentum from the TOGA-COARE Intensive Flux Array (IFA) region, are applied to the GCE version which incorporates cyclical boundary conditions. Sensitivity experiments show that grid domain size produces the largest response to domain-mean temperature and moisture deviations, as well as cloudiness, when compared to grid horizontal or vertical resolution, and advection scheme. It is found that a minimum grid-domain size of 500 km is needed to adequately resolve the convective cloud features. The control experiment shows that the atmospheric heating and moistening is primarily a response to cloud latent processes of condensation/evaporation, and deposition/sublimation, and to a lesser extent, melting of ice particles. Air-sea exchange of heat and moisture is found to be significant, but of secondary importance, while the radiational response is small. The simulated rainfall and atmospheric heating and moistening, agrees well with observations, and performs favorably to other models simulating this case.

Johnson, Daniel E.↗
DOE OSTI2024

Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains

Abstract. Based on long-term observations at the Southern Great Plains site by the Atmospheric Radiation Measurement (ARM) program for training and validation, a deep-learning model is developed to simulate the daytime evolution of boundary layer clouds (BLCs) from the perspective of land–atmosphere coupling. The model takes ARM measurements (including early-morning soundings and diurnally varying surface meteorological conditions and heat fluxes) as inputs and predicts hourly estimates (including cloud occurrence, the positions of cloud boundaries, and the vertical profile of the cloud fraction) as outputs. The deep-learning model offers good agreement with the observed cloud fields, especially in the accuracy with which cloud occurrence and base height are reproduced. When the inputs are substituted by reanalysis data from ERA5 and MERRA-2, the outputs of the deep-learning model provide a better agreement with observation than the cloud fields extracted from ERA5 and MERRA-2 themselves. Thus, the deep-learning model shows great potential to serve as a diagnostic tool for the performance of physics-based models in simulating stratiform and cumulus clouds. By quantifying biases in clouds and attributing them to the simulated atmospheric state variables versus the model-parameterized cloud processes, this observation-based deep-learning model may offer insights into the directions needed to improve the simulation of BLCs in physics-based models for weather forecasting and climate prediction.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Normalized vertical ice mass flux profiles from vertically pointing 8-mm-wavelength Doppler radar

During the FIRE 2 (First International Satellite Cloud Climatology Project Regional Experiment) project, NOAA's Wave Propagation Laboratory (WPL) operated its 8-mm wavelength Doppler radar extensively in the vertically pointing mode. This allowed for the calculation of a number of important cirrus cloud parameters, including cloud boundary statistics, cloud particle characteristic sizes and concentrations, and ice mass content (imc). The flux of imc, or, alternatively, ice mass flux (imf), is also an important parameter of a cirrus cloud system. Ice mass flux is important in the vertical redistribution of water substance and thus, in part, determines the cloud evolution. It is important for the development of cloud parameterizations to be able to define the essential physical characteristics of large populations of clouds in the simplest possible way. One method would be to normalize profiles of observed cloud properties, such as those mentioned above, in ways similar to those used in the convective boundary layer. The height then scales from 0.0 at cloud base to 1.0 at cloud top, and the measured cloud parameter scales by its maximum value so that all normalized profiles have 1.0 as their maximum value. The goal is that there will be a 'universal' shape to profiles of the normalized data. This idea was applied to estimates of imf calculated from data obtained by the WPL cloud radar during FIRE II. Other quantities such as median particle diameter, concentration, and ice mass content can also be estimated with this radar, and we expect to also examine normalized profiles of these quantities in time for the 1993 FIRE II meeting.

Orr, Brad W.↗
NASA NTRS1978

Numerical experiments on the climatic sensitivity of an atmospheric hydrologic cycle

It is shown for an intermediate numerical model that fractional cloudiness and relative humidity decrease with increasing temperature. The fractional cloudiness decreases at a rate about 1 per deg K. This occurs in spite of an increase in the evaporation, water transport, condensation, precipitation and cloud water content with increasing temperature. These results are quite similar to those found from models with more highly parameterized clouds, notably the NCAR model. The fractional cloudiness in this model is measured by the fractional coverage of total cloud water and the fractional coverage of positive condensation, in addition to the relative humidity. It is also shown that some of the characteristics of a temperate climate can be simulated in an intermediate numerical model with periodic, antisymmetric and symmetric boundary conditions on an f plane. Intermediate models of this sort may therefore be useful to investigate general questions about the earth's hydrologic cycle on climatic space and time scales

Roads, J. O.↗
NASA NTRS1993

Automated cloud classification with a fuzzy logic expert system

An unresolved problem in current cloud retrieval algorithms concerns the analysis of scenes containing overlapping cloud layers. Cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget. Most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. One promising method uses fuzzy logic to determine whether mixed cloud and/or surface types exist within a group of pixels, such as cirrus, land, and water, or cirrus and stratus. When two or more class types are present, fuzzy logic uses membership values to assign the group of pixels partially to the different class types. The strength of fuzzy logic lies in its ability to work with patterns that may include more than one class, facilitating greater information extraction from satellite radiometric data. The development of the fuzzy logic rule-based expert system involves training the fuzzy classifier with spectral and textural features calculated from accurately labeled 32x32 regions of Advanced Very High Resolution Radiometer (AVHRR) 1.1-km data. The spectral data consists of AVHRR channels 1 (0.55-0.68 mu m), 2 (0.725-1.1 mu m), 3 (3.55-3.93 mu m), 4 (10.5-11.5 mu m), and 5 (11.5-12.5 mu m), which include visible, near-infrared, and infrared window regions. The textural features are based on the gray level difference vector (GLDV) method. A sophisticated new interactive visual image Classification System (IVICS) is used to label samples chosen from scenes collected during the FIRE IFO II. The training samples are chosen from predefined classes, chosen to be ocean, land, unbroken stratiform, broken stratiform, and cirrus. The November 28, 1991 NOAA overpasses contain complex multilevel cloud situations ideal for training and validating the fuzzy logic expert system.

Tovinkere, Vasanth↗
NASA NTRS1996

Global Single and Multiple Cloud Classification with a Fuzzy Logic Expert System

An unresolved problem in remote sensing concerns the analysis of satellite imagery containing both single and multiple cloud layers. While cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget, most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. Coakley (1983) used a spatial coherence method to determine whether a region contained more than one cloud layer. Baum et al. (1995) developed a scheme for detection and analysis of daytime multiple cloud layers using merged AVHRR (Advanced Very High Resolution Radiometer) and HIRS (High-resolution Infrared Radiometer Sounder) data collected during the First ISCCP Regional Experiment (FIRE) Cirrus 2 field campaign. Baum et al. (1995) explored the use of a cloud classification technique based on AVHRR data. This study examines the feasibility of applying the cloud classifier to global satellite imagery.

Welch, Ronald M.↗
NASA NTRS1997

Anthropogenic Sulfate, Clouds, and Climate Forcing

This research work is a joint effort between research groups at the Battelle Pacific Northwest Laboratory, Virginia Tech University, Georgia Institute of Technology, Brookhaven National Laboratory, and Texas A&M University. It has been jointly sponsored by the National Aeronautics and Space Administration, the U.S. Department of Energy, and the U.S. Environmental Protection Agency. In this research, a detailed tropospheric aerosol-chemistry model that predicts oxidant concentrations as well as concentrations of sulfur dioxide and sulfate aerosols has been coupled to a general circulation model that distinguishes between cloud water mass and cloud droplet number. The coupled model system has been first validated and then used to estimate the radiative impact of anthropogenic sulfur emissions. Both the direct radiative impact of the aerosols and their indirect impact through their influence on cloud droplet number are represented by distinguishing between sulfuric acid vapor and fresh and aged sulfate aerosols, and by parameterizing cloud droplet nucleation in terms of vertical velocity and the number concentration of aged sulfur aerosols. Natural sulfate aerosols, dust, and carbonaceous and nitrate aerosols and their influence on the radiative impact of anthropogenic sulfate aerosols, through competition as cloud condensation nuclei, will also be simulated. Parallel simulations with and without anthropogenic sulfur emissions are performed for a global domain. The objectives of the research are: To couple a state-of-the-art tropospheric aerosol-chemistry model with a global climate model. To use field and satellite measurements to evaluate the treatment of tropospheric chemistry and aerosol physics in the coupled model. To use the coupled model to simulate the radiative (and ultimately climatic) impacts of anthropogenic sulfur emissions.

Ghan, Steven J.↗
NASA NTRS2010

Characterization of Cloud Water-Content Distribution

The development of realistic cloud parameterizations for climate models requires accurate characterizations of subgrid distributions of thermodynamic variables. To this end, a software tool was developed to characterize cloud water-content distributions in climate-model sub-grid scales. This software characterizes distributions of cloud water content with respect to cloud phase, cloud type, precipitation occurrence, and geo-location using CloudSat radar measurements. It uses a statistical method called maximum likelihood estimation to estimate the probability density function of the cloud water content.

Lee, Seungwon↗
NASA NTRS2011

The Impacts of an Observationally-Based Cloud Fraction and Condensate Overlap Parameterization on a GCM's Cloud Radiative Effect

It has been shown that the details of how cloud fraction overlap is treated in GCMs has substantial impact on shortwave and longwave fluxes. Because cloud condensate is also horizontally heterogeneous at GCM grid scales, another aspect of cloud overlap should in principle also be assessed, namely the vertical overlap of hydrometeor distributions. This type of overlap is usually examined in terms of rank correlations, i.e., linear correlations between hydrometeor amount ranks of the overlapping parts of cloud layers at specific separation distances. The cloud fraction overlap parameter and the rank correlation of hydrometeor amounts can be both expressed as inverse exponential functions of separation distance characterized by their respective decorrelation lengths (e-folding distances). Larger decorrelation lengths mean that hydrometeor fractions and probability distribution functions have high levels of vertical alignment. An analysis of CloudSat and CALIPSO data reveals that the two aspects of cloud overlap are related and their respective decorrelation lengths have a distinct dependence on latitude that can be parameterized and included in a GCM. In our presentation we will contrast the Cloud Radiative Effect (CRE) of the GEOS-5 atmospheric GCM (AGCM) when the observationally-based parameterization of decorrelation lengths is used to represent overlap versus the simpler cases of maximum-random overlap and globally constant decorrelation lengths. The effects of specific overlap representations will be examined for both diagnostic and interactive radiation runs in GEOS-5 and comparisons will be made with observed CREs from CERES and CloudSat (2B-FLXHR product). Since the radiative effects of overlap depend on the cloud property distributions of the AGCM, the availability of two different cloud schemes in GEOS-5 will give us the opportunity to assess a wide range of potential cloud overlap consequences on the model's climate.

Oreopoulos, Lazaros↗
NASA NTRS1988

Shortwave radiative parameterization of large atmospheric aerosols - Dust and water clouds

A parameterization of the single scattering properties of an aerosol for a dust layer in the solar spectral region is presented. The parameterization scheme accounts for the microphysical changes of an aerosol layer and is applied to dust and water clouds. A scaling parameter is presented which describes, simply and effectively, the optical properties of an aerosol layer with a large effective size parameter.

Ackerman, Steven A.↗
DOE OSTI2022

Investigation on cloud vertical structures based on Ka-band cloud radar observations at Wuhan in Central China

Based on vertically pointing observations of Ka-band frequency-modulated continuous millimeter wave cloud radar (MMCR) in 2020, we investigate cloud vertical structures at Wuhan in central China. Case comparison indicates that the cloud top locations in the MMCR observation are largely consistent with those in satellite data. The precipitation has the occurrence frequency of 9.5% over the year, with the maximum value of 16.3% in July and the submaximal value of 14.7% in January, showing the significant influences of monsoons on precipitation at Wuhan. As the humid, cloudy and rainy climate, the probability of precipitation and non-precipitation clouds increases gradually from 53.9% in January to a large value of 71.5% in July. The non-precipitation clouds have the probability of 44.5% in the year, with two prominent peaks at the reflectivities of -35 to -30 dBZ around 12 km in summer and - 37– -32 dBZ around 10 km in spring. The cloud top can reach the heights of 14.49, 16.62, 13.38 and 11.73 km from spring to winter, respectively. The monthly mean cloud layer thickness is the largest (2.18 km) in May and September, and the smallest (1.36 km) in November. The seasonally averaged base and top heights and thickness of clouds have small diurnal variations. The high cloud is predominant in the warm months from May to September, and in the other cool months, the middle cloud has the most fraction. The quantitative investigation of clouds is helpful in improving the cloud parameterization in climate models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2000

Comparison of Cirrus Cloud Models: A Project of the GEWEX Cloud System Study (GCSS) Working Group on Cirrus Cloud Systems

The GEWEX Cloud System Study (GCSS, GEWEX is the Global Energy and Water Cycle Experiment) is a community activity aiming to promote development of improved cloud parameterizations for application in the large-scale general circulation models (GCMs) used for climate research and for numerical weather prediction. The GCSS strategy is founded upon the use of cloud-system models (CSMs). These are "process" models with sufficient spatial and temporal resolution to represent individual cloud elements, but spanning a wide range of space and time scales to enable statistical analysis of simulated cloud systems. GCSS also employs single-column versions of the parametric cloud models (SCMs) used in GCMs. GCSS has working groups on boundary-layer clouds, cirrus clouds, extratropical layer cloud systems, precipitating deep convective cloud systems, and polar clouds.

Starr, David O'C.↗
NASA NTRS2000

Top-of-Atmosphere Albedo Estimation from Angular Distribution Models using Scene Identification from Satellite Cloud Property Retrievals

The next generation of Earth radiation budget satellite instruments will routinely merge estimates of global top-of-atmosphere radiative fluxes with cloud properties. This information will offer many new opportunities for validating radiative transfer models and cloud parameterizations in climate models. In this study, five months of POLarization and Directionality of the Earth's Reflectances (POLDER) 670 nm radiance measurements are considered in order to examine how satellite cloud property retrievals can be used to define empirical Angular Distribution Models (ADMs) for estimating top-of-atmosphere (TOA) albedo. ADMs are defined for 19 scene types defined by satellite retrievals of cloud fraction and cloud optical depth. Two approaches are used to define the ADM scene types: The first assumes there are no biases in the retrieved cloud properties and defines ADMs for fixed discrete intervals of cloud fraction and cloud optical depth (fixed-tau approach). The second approach involves the same cloud fraction intervals, but uses percentile intervals of cloud optical depth instead (percentile-tau approach). Albedos generated using these methods are compared with albedos inferred directly from the mean observed reflectance field. Albedos based on ADMs that assume cloud properties are unbiased (fixed-tau approach) show a strong systematic dependence on viewing geometry. This dependence becomes more pronounced with increasing solar zenith angle, reaching approximately equals 12% (relative) between near-nadir and oblique viewing zenith angles for solar zenith angles between 60 deg and 70 deg. The cause for this bias is shown to be due to biases in the cloud optical depth retrievals. In contrast, albedos based on ADMs built using percentile intervals of cloud optical depth (percentile-tau approach) show very little viewing zenith angle dependence and are in good agreement with albedos obtained by direct integration of the mean observed reflectance field (less than 1% relative error). When the ADMs are applied separately to populations consisting of only liquid water and ice clouds, significant biases in albedo with viewing geometry are observed (particularly at low sun elevations), highlighting the need to account for cloud phase both in cloud optical depth retrievals and in defining ADM scene types. ADM-derived monthly mean albedos determined for all 5 deg x 5 deg latitude/longitude regions over ocean are in good agreement (regional RMS relative errors less than 2%) with those obtained by direct integration when ADM albedos inferred from specific angular bins are averaged together. Albedos inferred from near-nadir and oblique viewing zenith angles are the least accurate, with regional RMS errors reaching approximately 5-10% (relative). Compared to an earlier study involving ERBE ADMs, regional mean albedos based on the 19 scene types considered here show a factor of 4 reduction in bias error and a factor of 3 reduction in RMS error.

Loeb, N. G.↗
NASA NTRS2012

Constraints on the Profiles of Total Water PDF in AGCMs from AIRS and a High-Resolution Model

Atmospheric general circulation model (AGCM) cloud parameterizations generally include an assumption about the subgrid-scale probability distribution function (PDF) of total water and its vertical profile. In the present study, the Atmospheric Infrared Sounder (AIRS) monthly-mean cloud amount and relative humidity fields are used to compute a proxy for the second moment of an AGCM total water PDF called the RH01 diagnostic, which is the AIRS mean relative humidity for cloud fractions of 0.1 or less. The dependence of the second moment on horizontal grid resolution is analyzed using results from a high-resolution global model simulation.The AIRS-derived RH01 diagnostic is generally larger near the surface than aloft, indicating a narrower PDF near the surface, and varies with the type of underlying surface. High-resolution model results show that the vertical structure of profiles of the AGCM PDF second moment is unchanged as the grid resolution changes from 200 to 100 to 50 km, and that the second-moment profiles shift toward higher values with decreasing grid spacing.Several Goddard Earth Observing System, version 5 (GEOS-5), AGCM simulations were performed with several choices for the profile of the PDF second moment. The resulting cloud and relative humidity fields were shown to be quite sensitive to the prescribed profile, and the use of a profile based on the AIRS-derived proxy results in improvements relative to observational estimates. The AIRS-guided total water PDF profiles, including their dependence on underlying surface type and on horizontal resolution, have been implemented in the version of the GEOS-5 AGCM used for publicly released simulations.

Molod, Andrea↗
NASA NTRS1999

EAULIQ: The Next Generation

This report summarizes the design of a new version of the stratiform cloud parameterization called Eauliq; the new version is called Eauliq NG. The key features of Eauliq NG are: (1) a prognostic fractional area covered by stratiform cloudiness, following the approach developed by M. Tiedtke for use in the ECMWF model; (2) separate prognostic thermodynamic variables for the clear and cloudy portions of each grid cell; (3) separate vertical velocities for the clear and cloudy portions of each grid cell, allowing the model to represent some aspects of observed mesoscale circulations; (4) cumulus entrainment from both the clear and cloudy portions of a grid cell, and cumulus detrainment into the cloudy portion only; and (5) the effects of the cumulus-induced subsidence in the cloudy portion of a grid cell on the cloud water and ice there. In this paper we present the mathematical framework of Eauliq NG; a discussion of cumulus effects; a new parameterization of lateral mass exchanges between clear and cloudy regions; and a theory to determine the mesoscale mass circulation, based on the hypothesis that the stratiform clouds remain neutrally buoyant through time and that the mesoscale circulations are the mechanism which makes this possible. An appendix also discusses some time-differencing methods.

Randall, David A.↗
DOE OSTI2025

Stable Machine‐Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection‐Permitting Simulations

Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the multiscale modeling framework (MMF), which embeds a kilometer-resolution cloud-resolving model (CRM) within each atmospheric column of a host climate model to replace traditional convection and cloud parameterizations. Machine learning offers a unique opportunity to make MMF more accessible by emulating the embedded CRM and reducing its substantial computational cost. Although many studies have demonstrated proof-of-concept success of achieving stable hybrid simulations, it remains a challenge to achieve near operational-level success with real geography and comprehensive variable emulation that includes, for example, explicit cloud condensate coupling. In this study, we present a stable hybrid model capable of integrating for at least 5 years with near operational-level complexity, including coarse-grid geography, seasonality, explicit cloud condensate and wind predictions, and land coupling. Our model demonstrates skillful online performance, achieving a 5-year zonal mean tropospheric temperature bias within 2 K, water vapor bias within 1 g/kg, and a precipitation root mean square error of 0.96 mm/day. Key factors contributing to our online performance include an expressive U-Net architecture and physical thermodynamic constraints for microphysics. With microphysical constraints mitigating unrealistic cloud formation, our work is the first to demonstrate realistic multi-year cloud condensate climatology under the MMF framework. Despite these advances, online diagnostics reveal persistent biases in certain regions, highlighting the need for innovative strategies to further optimize online performance.

Hu, Zeyuan [NVIDIA Corporation, Santa Clara, CA (U↗
NASA NTRS2000

Comparison of Cirrus Cloud Models: A Project of the GEWEX Cloud System Study (GCSS) Working Group on Cirrus Cloud Systems

The GEWEX Cloud System Study (GCSS, GEWEX is the Global Energy and Water Cycle Experiment) is a community activity aiming to promote development of improved cloud parameterizations for application in the large-scale general circulation models (GCMs) used for climate research and for numerical weather prediction (Browning et al, 1994). The GCSS strategy is founded upon the use of cloud-system models (CSMs). These are "process" models with sufficient spatial and temporal resolution to represent individual cloud elements, but spanning a wide range of space and time scales to enable statistical analysis of simulated cloud systems. GCSS also employs single-column versions of the parametric cloud models (SCMs) used in GCMs. GCSS has working groups on boundary-layer clouds, cirrus clouds, extratropical layer cloud systems, precipitating deep convective cloud systems, and polar clouds.

Starr, David OC.↗