Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “cloud parameterization”

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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

A Coupled GCM-Cloud Resolving Modeling System, and a Regional Scale Model to Study Precipitation Processes

Recent GEWEX Cloud System Study (GCSS) model comparison projects have indicated that cloud-resolving models (CRMs) agree with observations better than traditional single-column models in simulating various types of clouds and cloud systems from different geographic locations. Current and future NASA satellite programs can provide cloud, precipitation, aerosol and other data at very fine spatial and temporal scales. It requires a coupled global circulation model (GCM) and cloud-scale model (termed a superparameterization or multi-scale modeling framework, MMF) to use these satellite data to improve the understanding of the physical processes that are responsible for the variation in global and regional climate and hydrological systems. The use of a GCM will enable global coverage, and the use of a CRM will allow for better and more sophisticated physical parameterization. NASA satellite and field campaign cloud related datasets can provide initial conditions as well as validation for both the MMF and CRMs. The Goddard MMF is based on the 2D Goddard Cumulus Ensemble (GCE) model and the Goddard finite volume general circulation model (fvGCM), and it has started production runs with two years results (1998 and 1999). Also, at Goddard, we have implemented several Goddard microphysical schemes (2ICE, several 31CE), Goddard radiation (including explicitly calculated cloud optical properties), and Goddard Land Information (LIS, that includes the CLM and NOAH land surface models) into a next generatio11 regional scale model, WRF. In this talk, I will present: (1) A brief review on GCE model and its applications on precipitation processes (microphysical and land processes), (2) The Goddard MMF and the major difference between two existing MMFs (CSU MMF and Goddard MMF), and preliminary results (the comparison with traditional GCMs), and (3) A discussion on the Goddard WRF version (its developments and applications).

Tao, Wei-Kuo↗
NASA NTRS1994

Evaluation of a GCM cirrus parameterization using satellite observations

This study applies a simple yet effective methodology to validate a general circulation model parameterization of cirrus ice water path. The methodology combines large-scale dynamic and thermodynamic fields from operational analyses with prescribed occurrence of cirrus clouds from satellite observations to simulate a global distribution of ice water path. The predicted cloud properties are then compared with the corresponding satellite measurements of visible optical depth and infrared cloud emissivity to evaluate the reliability of the parameterization. This methodology enables the validation to focus strictly on the water loading side of the parameterization by eliminating uncertainties involved in predicting the occurrence of cirrus internally within the parameterization. Overall the parameterization performs remarkably well in capturing the observed spatial patterns of cirrus optical properties. Spatial correlations between the observed and the predicted optical depths are typically greater than 0.7 for the tropics and northern hemisphere midlatitudes. The good spatial agreement largely stems from the strong dependence of the ice water path upon the temperature of the environment in which the clouds form. Poorer correlations (r approximately 0.3) are noted over the southern hemisphere midlatitudes, suggesting that additional processes not accounted for by the parameterization may be important there. Quantitative evaluation of the parameterization is hindered by the present uncertainty in the size distribution of cirrus ice particles. Consequently, it is difficult to determine if discrepancies between the observed and the predicted optical properties are attributable to errors in the parameterized ice water path or to geographic variations in effective radii.

Soden, B. J.↗
NASA NTRS2013

Characteristics of Mesoscale Organization in WRF Simulations of Convection during TWP-ICE

Compared to satellite-derived heating profiles, the Goddard Institute for Space Studies general circulation model (GCM) convective heating is too deep and its stratiform upper-level heating is too weak. This deficiency highlights the need for GCMs to parameterize the mesoscale organization of convection. Cloud-resolving model simulations of convection near Darwin, Australia, in weak wind shear environments of different humidities are used to characterize mesoscale organization processes and to provide parameterization guidance. Downdraft cold pools appear to stimulate further deep convection both through their effect on eddy size and vertical velocity. Anomalously humid air surrounds updrafts, reducing the efficacy of entrainment. Recovery of cold pool properties to ambient conditions over 5-6 h proceeds differently over land and ocean. Over ocean increased surface fluxes restore the cold pool to prestorm conditions. Over land surface fluxes are suppressed in the cold pool region; temperature decreases and humidity increases, and both then remain nearly constant, while the undisturbed environment cools diurnally. The upper-troposphere stratiform rain region area lags convection by 5-6 h under humid active monsoon conditions but by only 1-2 h during drier break periods, suggesting that mesoscale organization is more readily sustained in a humid environment. Stratiform region hydrometeor mixing ratio lags convection by 0-2 h, suggesting that it is strongly influenced by detrainment from convective updrafts. Small stratiform region temperature anomalies suggest that a mesoscale updraft parameterization initialized with properties of buoyant detrained air and evolving to a balance between diabatic heating and adiabatic cooling might be a plausible approach for GCMs.

Del Genio, Anthony D.↗
NASA NTRS2012

On the Ice Nucleation Spectrum

This work presents a novel formulation of the ice nucleation spectrum, i.e. the function relating the ice crystal concentration to cloud formation conditions and aerosol properties. The new formulation is physically-based and explicitly accounts for the dependency of the ice crystal concentration on temperature, supersaturation, cooling rate, and particle size, surface area and composition. This is achieved by introducing the concepts of ice nucleation coefficient (the number of ice germs present in a particle) and nucleation probability dispersion function (the distribution of ice nucleation coefficients within the aerosol population). The new formulation is used to generate ice nucleation parameterizations for the homogeneous freezing of cloud droplets and the heterogeneous deposition ice nucleation on dust and soot ice nuclei. For homogeneous freezing, it was found that by increasing the dispersion in the droplet volume distribution the fraction of supercooled droplets in the population increases. For heterogeneous ice nucleation the new formulation consistently describes singular and stochastic behavior within a single framework. Using a fundamentally stochastic approach, both cooling rate independence and constancy of the ice nucleation fraction over time, features typically associated with singular behavior, were reproduced. Analysis of the temporal dependency of the ice nucleation spectrum suggested that experimental methods that measure the ice nucleation fraction over few seconds would tend to underestimate the ice nuclei concentration. It is shown that inferring the aerosol heterogeneous ice nucleation properties from measurements of the onset supersaturation and temperature may carry significant error as the variability in ice nucleation properties within the aerosol population is not accounted for. This work provides a simple and rigorous ice nucleation framework where theoretical predictions, laboratory measurements and field campaign data can be reconciled, and that is suitable for application in atmospheric modeling studies.

Barahona, D.↗
NASA NTRS2005

Applications and Improvement of a Coupled, Global and Cloud-Resolving Modeling System

Recently Grabowski (2001) and Khairoutdinov and Randall (2001) have proposed the use of 2D CFWs as a "super parameterization" [or multi-scale modeling framework (MMF)] to represent cloud processes within atmospheric general circulation models (GCMs). In the MMF, a fine-resolution 2D CRM takes the place of the single-column parameterization used in conventional GCMs. A prototype Goddard MMF based on the 2D Goddard Cumulus Ensemble (GCE) model and the Goddard finite volume general circulation model (fvGCM) is now being developed. The prototype includes the fvGCM run at 2.50 x 20 horizontal resolution with 32 vertical layers from the surface to 1 mb and the 2D (x-z) GCE using 64 horizontal and 32 vertical grid points with 4 km horizontal resolution and a cyclic lateral boundary. The time step for the 2D GCE would be 15 seconds, and the fvGCM-GCE coupling frequency would be 30 minutes (i.e. the fvGCM physical time step). We have successfully developed an fvGCM-GCE coupler for this prototype. Because the vertical coordinate of the fvGCM (a terrain-following floating Lagrangian coordinate) is different from that of the GCE (a z coordinate), vertical interpolations between the two coordinates are needed in the coupler. In interpolating fields from the GCE to fvGCM, we use an existing fvGCM finite- volume piecewise parabolic mapping (PPM) algorithm, which conserves the mass, momentum, and total energy. A new finite-volume PPM algorithm, which conserves the mass, momentum and moist static energy in the z coordinate, is being developed for interpolating fields from the fvGCM to the GCE. In the meeting, we will discuss the major differences between the two MMFs (i.e., the CSU MMF and the Goddard MMF). We will also present performance and critical issues related to the MMFs. In addition, we will present multi-dimensional cloud datasets (i.e., a cloud data library) generated by the Goddard MMF that will be provided to the global modeling community to help improve the representation and performance of moist processes in climate models and to improve our understanding of cloud processes globally (the software tools needed to produce cloud statistics and to identify various types of clouds and cloud systems from both high-resolution satellite and model data will be also presented).

Tao, W.-K.↗
NASA NTRS2019

Progress and Challenges in Quantifying Wildfire Smoke Emissions, Their Properties, Transport, and Atmospheric Impacts

Wildfire is a natural and integral ecosystem process that is necessary to maintain species composition, structure, and ecosystem function. Extreme fires have been increasing over the last decades, which have a substantial impact on air quality, human health, the environment, and climate systems. Smoke aerosols can be transported over large distances, acting as pollutants that affect adjacent and distant downwind communities and environments. Fire emissions are a complicated mixture of trace gases and aerosols, many of which are short‐lived and chemically reactive, and this mixture affects atmospheric composition in complex ways that are not completely understood. We present a review of the current state of knowledge of smoke aerosol emissions originating from wildfires. Satellite observations, from both passive and active instruments, are critical to providing the ability to view the large‐scale influence of fire, smoke, and their impacts. Progress in the development of fire emission estimates to regional and global chemical transport models has advanced, although significant challenges remain, such as connecting ecosystems and fuels burned with dependent atmospheric chemistry. Knowledge of the impact of smoke on radiation, clouds, and precipitation has progressed and is an essential topical research area. However, current measurements and parameterizations are not adequate to describe the impacts on clouds of smoke particles (e.g., CNN, INP) from fire emissions in the range of representative environmental conditions necessary to advance science or modeling. We conclude by providing recommendations to the community that we believe will advance the science and understanding of the impact of fire smoke emissions on human and environmental health, as well as feedback with climate system.

I N Sokolik↗
NASA NTRS2017

Remote Sensing of Multiple Cloud Layer Heights Using Multi-Angular Measurements

Cloud top height (CTH) affects the radiative properties of clouds. Improved CTH observations will allow for improved parameterizations in large-scale models and accurate information on CTH is also important when studying variations in freezing point and cloud microphysics. NASAs airborne Research Scanning Polarimeter (RSP) is able to measure cloud top height using a novel multi-angular contrast approach. For the determination of CTH, a set of consecutive nadir reflectances is selected and the cross-correlations between this set and co-located sets at other viewing angles are calculated for a range of assumed cloud top heights, yielding a correlation profile. Under the assumption that cloud reflectances are isotropic, local peaks in the correlation profile indicate cloud layers. This technique can be applied to every RSP footprint and we demonstrate that detection of multiple peaks in the correlation profile allow retrieval of heights of multiple cloud layers within single RSP footprints. This paper provides an in-depth description of the architecture and performance of the RSPs CTH retrieval technique using data obtained during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC(exp. 4)RS) campaign. RSP retrieved cloud heights are evaluated using collocated data from the Cloud Physics Lidar (CPL). The method's accuracy associated with the magnitude of correlation, optical thickness, cloud thickness and cloud height are explored. The technique is applied to measurements at a wavelength of 670 nm and 1880 nm and their combination. The 1880-nm band is virtually insensitive to the lower troposphere due to strong water vapor absorption.

Sinclair, Kenneth↗
NASA NTRS2005

A New Approach to using a Cloud-Resolving Model to Study the Interactions between Clouds, Precipitation and Aerosols

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 kilometers, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-kilometer scales are resolved in horizontal domains as large as 10,000 kilometers in two dimensions, and 1,000 x 1,000 square kilometers in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically-based parameterizations for climate models and numerical weather prediction models. A review of developments and applications of cloud models in the past, present and future will be presented in this talk. In particular, a new approach to using cloud-resolving models to study the interactions between clouds, precipitation and aerosols will be presented.

Tao, Wei-Kuo↗
NASA NTRS2005

A New Approach to Using a Cloud-resolving Model to Study the Interactions Between Clouds, Precipitation and Aerosols

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 km, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as l0,OOO km in two-dimensions, and 1,OOO x 1,OOO km2 in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically-based parameterizations for climate models and numerical weather prediction models. A review of developments and applications of cloud models in the past, present and future will be presented in this talk. In particular, a new approach to using cloud-resolving models to study the interactions between clouds, precipitation and aerosols will be presented.

Tao, Wei-Kuo↗
NASA NTRS2005

A New Approach to using a Cloud-Resolving Model to Study the Interactions between Clouds, Precipitation and Aerosols

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 km, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 square kilometers in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically-based parameterizations for climate models and numerical weather prediction models. A review of developments and applications of cloud models in the past, present and future will be presented in this talk. In particular, a new approach to using cloud-resolving models to study the interactions between clouds, precipitation and aerosols will be presented.

Tao, Wei-Kuo↗
NASA NTRS2008

A Multi-Scale Modeling System with Unified Physics

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 km, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. In recent years, exponentially increasing computer power has extended cloud-resolving-mode1 integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 km2 in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique.

Tao, Wei-Kuo↗
NASA NTRS2006

Model Intercomparison of Indirect Aerosol Effects

Modeled differences in predicted effects are increasingly used to help quantify the uncertainty of these effects. Here, we examine modeled differences in the aerosol indirect effect in a series of experiments that help to quantify how and why model-predicted aerosol indirect forcing varies between models. The experiments start with an experiment in which aerosol concentrations, the parameterization of droplet concentrations and the autoconversion scheme are all specified and end with an experiment that examines the predicted aerosol indirect forcing when only aerosol sources are specified. Although there are large differences in the predicted liquid water path among the models, the predicted aerosol first indirect effect for the first experiment is rather similar, about -0.6 W/sq m to -0.7 W/sq m. Changes to the autoconversion scheme can lead to large changes in the liquid water path of the models and to the response of the liquid water path to changes in aerosols. Adding an autoconversion scheme that depends on the droplet concentration caused a larger (negative) change in net outgoing shortwave radiation compared to the 1st indirect effect, and the increase varied from only 22% to more than a factor of three. The change in net shortwave forcing in the models due to varying the autoconversion scheme depends on the liquid water content of the clouds as well as their predicted droplet concentrations, and both increases and decreases in the net shortwave forcing can occur when autoconversion schemes are changed. The parameterization of cloud fraction within models is not sensitive to the aerosol concentration, and, therefore, the response of the modeled cloud fraction within the present models appears to be smaller than that which would be associated with model "noise". The prediction of aerosol concentrations, given a fixed set of sources, leads to some of the largest differences in the predicted aerosol indirect radiative forcing among the models, with values of cloud forcing ranging from -0.3 W/sq m to -1.4W/sq m. Thus, this aspect of modeling requires significant improvement in order to improve the prediction of aerosol indirect effects.

Penner, J. E.↗
NASA NTRS2006

Using Multi-scale Modeling System to Study the Interactions between Clouds, Precipitation, Aerosols, Radiation and Land Surface

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 kilometers, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-kilometer scales are resolved in horizontal domains as large as 10,000 kilometers in two-dimensions, and 1,000 x 1,000 square kilometers in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. A review of developments, improvements and applications of cloud models (GCE and WRF) at Goddard will be presented in this talk. In particular, a new approach to using multi-scale modeling system to study the interactions between clouds, precipitation, aerosols and land will be presented.

Tao, Wei-Kuo↗
NASA NTRS2006

Using Multi-scale Modeling System to Study the Interactions between Clouds, Precipitation, Aerosols, Radiation and Land Surface

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 kilometers, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial-lkm scales are resolved in horizontal domains as large as 10,000 kilometers in two-dimensions, and 1,000 x 1,000 square kilometers in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. A review of developments, improvements and applications of cloud models (GCE and WRF) at Goddard wlll be is presented in this talk. In particular, a new approach to using multi-scale modeling system to study the interactions between clouds, precipitation, aerosols and land will be presented.

Tao, Wei-Kuo↗
NASA NTRS2006

Using Multi-scale Modeling System to Study the Interactions between Clouds, Precipitation, Aerosols, Radiation and Land Surface

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 km, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 sq km in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. A review of developments, improvements and applications of cloud models (GCE and WRF) at Goddard will be presented in this talk. In particular, a new approach to using multi-scale modeling system to study the interactions between clouds, precipitation, aerosols and land will be presented.

Tao, Wei-Kuo↗
NASA NTRS1989

The effects of atmospheric cloud radiative forcing on climate

In order to isolate the effects of atmospheric cloud radiative forcing (ACRF) on climate, the general circulation of an ocean-covered earth called 'Seaworld' was simulated using the Colorado State University GCM. Most current climate models, however, do not include an interactive ocean. The key simplifications in 'Seaworld' are the fixed boundary temperature with no land points, the lack of mountains and the zonal uniformity of the boundary conditions. Two 90-day 'perpetual July' simulations were performed and analyzed the last sixty days of each. The first run included all the model's physical parameterizations, while the second omitted the effects of clouds in both the solar and terrestrial radiation parameterizations. Fixed and identical boundary temperatures were set for the two runs, and resulted in differences revealing the direct and indirect effects of the ACRF on the large-scale circulation and the parameterized hydrologic processes.

Randall, David A.↗
NASA NTRS1997

Trade-Wind Cloudiness and Climate

Closed Mesoscale Cellular Convection (MCC) consists of mesoscale cloud patches separated by narrow clear regions. Strong radiative cooling occurs at the cloud top. A dry two-dimensional Bousinesq model is used to study the effects of cloud-top cooling on convection. Wide updrafts and narrow downdrafts are used to indicate the asymmetric circulations associated with the mesoscale cloud patches. Based on the numerical results, a conceptual model was constructed to suggest a mechanism for the formation of closed MCC over cool ocean surfaces. A new method to estimate the radioative and evaporative cooling in the entrainment layer of a stratocumulus-topped boundary layer has been developed. The method was applied to a set of Large-Eddy Simulation (LES) results and to a set of tethered-balloon data obtained during FIRE. We developed a statocumulus-capped marine mixed layer model which includes a parameterization of drizzle based on the use of a predicted Cloud Condensation Nuclei (CCN) number concentration. We have developed, implemented, and tested a very elaborate new stratiform cloudiness parameterization for use in GCMs. Finally, we have developed a new, mechanistic parameterization of the effects of cloud-top cooling on the entrainment rate.

Randall, David A.↗