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NASA NTRS2015

RACORO Continental Boundary Layer Cloud Investigations: 3. Separation of Parameterization Biases in Single-Column Model CAM5 Simulations of Shallow Cumulus

Climatically important low-level clouds are commonly misrepresented in climate models. The FAst-physics System TEstbed and Research (FASTER) Project has constructed case studies from the Atmospheric Radiation Measurement Climate Research Facility's Southern Great Plain site during the RACORO aircraft campaign to facilitate research on model representation of boundary-layer clouds. This paper focuses on using the single-column Community Atmosphere Model version 5 (SCAM5) simulations of a multi-day continental shallow cumulus case to identify specific parameterization causes of low-cloud biases. Consistent model biases among the simulations driven by a set of alternative forcings suggest that uncertainty in the forcing plays only a relatively minor role. In-depth analysis reveals that the model's shallow cumulus convection scheme tends to significantly under-produce clouds during the times when shallow cumuli exist in the observations, while the deep convective and stratiform cloud schemes significantly over-produce low-level clouds throughout the day. The links between model biases and the underlying assumptions of the shallow cumulus scheme are further diagnosed with the aid of large-eddy simulations and aircraft measurements, and by suppressing the triggering of the deep convection scheme. It is found that the weak boundary layer turbulence simulated is directly responsible for the weak cumulus activity and the simulated boundary layer stratiform clouds. Increased vertical and temporal resolutions are shown to lead to stronger boundary layer turbulence and reduction of low-cloud biases.

parameterization↗

NASA NTRS2004

Single-column Model Intercomparison for a Stably Stratified Atmospheric Boundary Layer

The parameterization of the stably stratified atmospheric boundary layer is a difficult issue, which has a large impact on the medium-range weather forecasts and on climate integrations. A non-strongly stratified arctic case is simulated by nineteen single-column turbulence schemes. The statistics from the Large-eddy simulation (LES) intercomparison made for the same case by eight different models are used as a guiding reference. The single-column parameterizations include research schemes and operational schemes from major forecast and climate research centres. First order schemes, a large number of turbulence kinetic energy closures, and other proposals have submitted results. There is a large spread in the results; in general, the operational schemes mix more efficiently than the research ones, and the TKE and other higher order closures give results closer to the LES statistics. The sensitivities of the schemes to the parameters of their turbulence closures are partially explored.

Cuxart, J.↗

NASA NTRS2020

Clouds and Convective Self-Aggregation in a Multi-Model Ensemble of Radiative-Convective Equilibrium Simulations

The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative-convective56equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climatecsensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique amongst intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud-resolving models (CRMs), large eddy simulations (LES), and global cloud-resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self-aggregation in large domains and agree that self-aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self-aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than un-aggregated simulations. Plain Language Summary This study investigates tropical clouds and climate using results from more than 30 different numerical models set up in a simplified framework. The dataset of model simulations is unique in that it includes a wide range of model types configured in a consistent manner. We address some of the biggest open questions in climate science, including how cloud properties change with warming and the role that the tendency of clouds to form clusters plays in determining the average climate and how climate changes. While there are large differences in how the different models simulate average temperature, humidity, and cloudiness, in a majority of models, the amount of high clouds decreases as climate warms. Nearly all models simulate a tendency for clouds to cluster together. There is agreement that when the clouds are clustered, the atmosphere is drier with fewer clouds overall. We don’t find a conclusive result for how cloud clustering changes as the climate warms.

Allison A. Wing↗

NASA NTRS2013

Evaluation of Intercomparisons of Four Different Types of Model Simulating TWP-ICE

Four model intercomparisons were run and evaluated using the TWP-ICE field campaign, each involving different types of atmospheric model. Here we highlight what can be learnt from having single-column model (SCM), cloud-resolving model (CRM), global atmosphere model (GAM) and limited-area model (LAM) intercomparisons all based around the same field campaign. We also make recommendations for anyone planning further large multi-model intercomparisons to ensure they are of maximum value to the model development community. CRMs tended to match observations better than other model types, although there were exceptions such as outgoing long-wave radiation. All SCMs grew large temperature and moisture biases and performed worse than other model types for many diagnostics. The GAMs produced a delayed and significantly reduced peak in domain-average rain rate when compared to the observations. While it was shown that this was in part due to the analysis used to drive these models, the LAMs were also driven by this analysis and did not have the problem to the same extent. Based on differences between the models with parametrized convection (SCMs and GAMs) and those without (CRMs and LAMs), we speculate that that having explicit convection helps to constrain liquid water whereas the ice contents are controlled more by the representation of the microphysics.

convection↗

NASA NTRS1975

Water vapor absorption spectra of the upper atmosphere /45-185 per cm/

The far IR nighttime absorption spectrum of the earth's atmosphere above 14 km is determined from observations of the bright moon. The spectra were obtained using a Michelson interferometer attached to a 30-cm telescope aboard a high-altitude jet aircraft. Comparison with a single-layer model atmosphere implies a vertical column of 3.4 plus or minus 0.4 microns of precipitable water on 30 August 1971 and 2.4 plus or minus 0.3 microns of precipitable water on 6 January 1972.-

Augason, G. C.↗

NASA NTRS2005

Cloud Properties Simulated by a Single-Column Model. Part II: Evaluation of Cumulus Detrainment and Ice-phase Microphysics Using a Cloud Resolving Model

This paper is the second in a series in which kilometer-scale-resolving observations from the Atmospheric Radiation Measurement program and a cloud-resolving model (CRM) are used to evaluate the single-column model (SCM) version of the National Centers for Environmental Prediction Global Forecast System model. Part I demonstrated that kilometer-scale cirrus properties simulated by the SCM significantly differ from the cloud radar observations while the CRM simulation reproduced most of the cirrus properties as revealed by the observations. The present study describes an evaluation, through a comparison with the CRM, of the SCM's representation of detrainment from deep cumulus and ice-phase microphysics in an effort to better understand the findings of Part I. It is found that detrainment occurs too infrequently at a single level at a time in the SCM, although the detrainment rate averaged over the entire simulation period is somewhat comparable to that of the CRM simulation. Relatively too much detrained ice is sublimated when first detrained. Snow falls over too deep of a layer due to the assumption that snow source and sink terms exactly balance within one time step in the SCM. These characteristics in the SCM parameterizations may explain many of the differences in the cirrus properties between the SCM and the observations (or between the SCM and the CRM). A possible improvement for the SCM consists of the inclusion of multiple cumulus cloud types as in the original Arakawa-Schubert scheme, prognostically determining the stratiform cloud fraction and snow mixing ratio. This would allow better representation of the detrainment from deep convection, better coupling of the volume of detrained air with cloud fraction, and better representation of snow field.

Luo, Yali↗

NASA NTRS1997

Cloud Radiation Forcings and Feedbacks: General Circulation Model Tests and Observational Validation

Using an atmospheric general circulation model (the National Center for Atmospheric Research Community Climate Model: CCM2), the effects on climate sensitivity of several different cloud radiation parameterizations have been investigated. In addition to the original cloud radiation scheme of CCM2, four parameterizations incorporating prognostic cloud water were tested: one version with prescribed cloud radiative properties and three other versions with interactive cloud radiative properties. The authors' numerical experiments employ perpetual July integrations driven by globally constant sea surface temperature forcings of two degrees, both positive and negative. A diagnostic radiation calculation has been applied to investigate the partial contributions of high, middle, and low cloud to the total cloud radiative forcing, as well as the contributions of water vapor, temperature, and cloud to the net climate feedback. The high cloud net radiative forcing is positive, and the middle and low cloud net radiative forcings are negative. The total net cloud forcing is negative in all of the model versions. The effect of interactive cloud radiative properties on global climate sensitivity is significant. The net cloud radiative feedbacks consist of quite different shortwave and longwave components between the schemes with interactive cloud radiative properties and the schemes with specified properties. The increase in cloud water content in the warmer climate leads to optically thicker middle- and low-level clouds and in turn to negative shortwave feedbacks for the interactive radiative schemes, while the decrease in cloud amount simply produces a positive shortwave feedback for the schemes with a specified cloud water path. For the longwave feedbacks, the decrease in high effective cloudiness for the schemes without interactive radiative properties leads to a negative feedback, while for the other cases, the longwave feedback is positive. These cloud radiation parameterizations are empirically validated by using a single-column diagnostic model. together with measurements from the Atmospheric Radiation Measurement program and from the Tropical Ocean Global Atmosphere Combined Ocean-Atmosphere Response Experiment. The inclusion of prognostic cloud water produces a notable improvement in the realism of the parameterizations, as judged by these observations. Furthermore, the observational evidence suggests that deriving cloud radiative properties from cloud water content and microphysical characteristics is a promising route to further improvement.

Lee,Wan-Ho↗

NASA NTRSDate not supplied

Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation

Global climate models typically operate at a grid resolution of hundreds of kilometers and fail to resolve atmospheric mesoscale processes, e.g., clouds, precipitation, and gravity waves (GWs).Model representation of these processes and their sources is essential to the global circulation and planetary energy budget, but subgrid scale contributions from these processes are often only approximately represented in models using parameterizations. These parameterizations are subject to approximations and idealizations, which limit their capability and accuracy. The most drastic of these approximations is the “single-column approximation” which completely neglects the horizontal evolution of these processes, resulting in key biases in current climate models. With a focus on atmospheric GWs, we present the first-ever global simulation of atmospheric GW fluxes using machine learning (ML) models trained on the WINDSET dataset to emulate global GW emulation in the atmosphere, as an alternative to traditional single-column parameterizations. Using an Attention U-Net-based architecture trained on globally resolved GW momentum fluxes, we illustrate the importance and effectiveness of global nonlocality, when simulating GWs using data-driven schemes.

Aman Gupta↗

NASA NTRS2002

Confronting Models with Data: The GEWEX Cloud Systems Study

The GEWEX Cloud System Study (GCSS; GEWEX is the Global Energy and Water Cycle Experiment) was organized to promote development of improved parameterizations of cloud systems for use in climate and numerical weather prediction models, with an emphasis on the climate applications. The strategy of GCSS is to use two distinct kinds of models to analyze and understand observations of the behavior of several different types of clouds systems. Cloud-system-resolving models (CSRMs) have high enough spatial and temporal resolutions to represent individual cloud elements, but cover a wide enough range of space and time scales to permit statistical analysis of simulated cloud systems. Results from CSRMs are compared with detailed observations, representing specific cases based on field experiments, and also with statistical composites obtained from satellite and meteorological analyses. Single-column models (SCMs) are the surgically extracted column physics of atmospheric general circulation models. SCMs are used to test cloud parameterizations in an un-coupled mode, by comparison with field data and statistical composites. In the original GCSS strategy, data is collected in various field programs and provided to the CSRM Community, which uses the data to "certify" the CSRMs as reliable tools for the simulation of particular cloud regimes, and then uses the CSRMs to develop parameterizations, which are provided to the GCM Community. We report here the results of a re-thinking of the scientific strategy of GCSS, which takes into account the practical issues that arise in confronting models with data. The main elements of the proposed new strategy are a more active role for the large-scale modeling community, and an explicit recognition of the importance of data integration.

Randall, David↗

NASA NTRS2003

Seasonal Atmospheric and Oceanic Predictions

Several projects associated with dynamical, statistical, single column, and ocean models are presented. The projects include: 1) Regional Climate Modeling; 2) Statistical Downscaling; 3) Evaluation of SCM and NSIPP AGCM Results at the ARM Program Sites; and 4) Ocean Forecasts.

Roads, John↗

NASA NTRS2013

Evaluation of Precipitation Simulated by Seven SCMs against the ARM Observations at the SGP Site

This study evaluates the performances of seven single-column models (SCMs) by comparing simulated surface precipitation with observations at the Atmospheric Radiation Measurement Program Southern Great Plains (SGP) site from January 1999 to December 2001. Results show that although most SCMs can reproduce the observed precipitation reasonably well, there are significant and interesting differences in their details. In the cold season, the model-observation differences in the frequency and mean intensity of rain events tend to compensate each other for most SCMs. In the warm season, most SCMs produce more rain events in daytime than in nighttime, whereas the observations have more rain events in nighttime. The mean intensities of rain events in these SCMs are much stronger in daytime, but weaker in nighttime, than the observations. The higher frequency of rain events during warm-season daytime in most SCMs is related to the fact that most SCMs produce a spurious precipitation peak around the regime of weak vertical motions but rich in moisture content. The models also show distinct biases between nighttime and daytime in simulating significant rain events. In nighttime, all the SCMs have a lower frequency of moderate-to-strong rain events than the observations for both seasons. In daytime, most SCMs have a higher frequency of moderate-to-strong rain events than the observations, especially in the warm season. Further analysis reveals distinct meteorological backgrounds for large underestimation and overestimation events. The former occur in the strong ascending regimes with negative low-level horizontal heat and moisture advection, whereas the latter occur in the weak or moderate ascending regimes with positive low-level horizontal heat and moisture advection.

Model comparison↗

NASA NTRS2000

Influence of the Biosphere on Precipitation: July 1995 Studies with the ARM-CART Data

Ensemble sets of simulation experiments were conducted with a single column model (SCM) using the Goddard GEOS II GCM physics containing a recent version of the Cumulus Scheme (McRAS) and a biosphere based land-fluxes scheme (SSiB). The study used the 18 July to 5 August 1995 ARM-CART (Atmospheric Radiation Measurement-Cloud Atmospheric Radiation Test-bed) data, which was collected at the ARM-CART site in the mid-western United States and analyzed for single column modeling (SCM) studies. The new findings affirm the earlier findings that the vegetation, which increases the solar energy absorption at the surface together with soil and soil-moisture dependent processes, which modulate the surface, fluxes (particularly evapotranspiration) together help to increase the local rainfall. In addition, the results also show that for the particular study period roughly 50% of the increased evaporation over the ARM-CART site would be converted into rainfall with the Column, while the remainder would be advected out to the large-scale. Notwithstanding the limitations of only one-way interaction (i.e., the large-scale influencing the regional physics and not vice versa), the current SCM simulations show a very robust relationship. The evaporation-precipitation relationship turns out to be independent of the soil types, and soil moisture; however, it is weakly dependent on the vegetation cover because of its surface-albedo effect. Clearly, these inferences are prone to weaknesses of the SCM physics, the assumptions of the large-scale being unaffected by gridscale (SCM-scale) changes in moist processes, and other limitations of the evaluation procedures.

Sud, Y. C.↗

NASA NTRS2003

Performance of Goddard Earth Observing System GCM Column Radiation Models under Heterogeneous Cloud Conditions

We test the performance of the shortwave (SW) and longwave (LW) Column Radiation Models (CORAMs) of Chou and collaborators with heterogeneous cloud fields from a global single-day dataset produced by NCAR's Community Atmospheric Model with a 2-D CRM installed in each gridbox. The original SW version of the CORAM performs quite well compared to reference Independent Column Approximation (ICA) calculations for boundary fluxes, largely due to the success of a combined overlap and cloud scaling parameterization scheme. The absolute magnitude of errors relative to ICA are even smaller for the LW CORAM which applies similar overlap. The vertical distribution of heating and cooling within the atmosphere is also simulated quite well with daily-averaged zonal errors always below 0.3 K/d for SW heating rates and 0.6 K/d for LW cooling rates. The SW CORAM's performance improves by introducing a scheme that accounts for cloud inhomogeneity. These results suggest that previous studies demonstrating the inaccuracy of plane-parallel models may have unfairly focused on worst scenario cases, and that current radiative transfer algorithms of General Circulation Models (GCMs) may be more capable than previously thought in estimating realistic spatial and temporal averages of radiative fluxes, as long as they are provided with correct mean cloud profiles. However, even if the errors of the particular CORAMs are small, they seem to be systematic, and the impact of the biases can be fully assessed only with GCM climate simulations.

Oreopoulos, L.↗

NASA NTRS2004

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

Recently Grabowski and Khairoutdinov and Randall have proposed and demonstrated, the feasibility of using 2D CRMs (these did not include detailed land processes and used periodic lateral boundary conditions) as 'super-parameterizations or a multi-scale modeling framework MMF' for cloud processes within atmospheric generation circulation models (GCMs). In the MMF, a fine-resolution 2D CRM takes the place of the single-column parameterization that is used in conventional GCMs. An overview of this approach is given by Randall et al.. Since a CRM can explicitly simulate cloud processes at the natural space and time scales of clouddynamical processes, it's possible to compute statistics of cloud fields, including radiances and radar backscatters, directly simulated/produced by the CRM. A seed fund is available at NASA Goddard to build a MMF based on a 2D GCE model and the Goddard fvGCM. A prototype MMF will be developed at end of 2004. The major objectives are to test and evaluate its performance using the GCE model as the super parameterization and compare its results with other super parameterizations, conventional GCMs and observations. Recently, a detailed spectral-bin microphysical scheme was implemented into the Goddard Cumulus Ensemble (WE) model. The GCE model is a CRM and it has been improved and applied to study the precipitation processes in the past two decades. Atmospheric aerosols are also described using number density size-distribution functions. The model is tested by studying the evolution of deep tropical clouds in the west Pacific warm pool region and summertime convection over a mid-latitude continent with different concentrations of CCN: a low 'clean' concentration and a high 'dirty' concentration. The impact of atmospheric aerosol concentration on cloud and precipitation will be investigated. In addition, the similarities and differences between bulk- and spectral-bin microphysics will be presented,

Tao, Wei-Kuo↗

NASA NTRS2013

The NASA-Goddard Multi-Scale Modeling Framework - Land Information System: Global Land/atmosphere Interaction with Resolved Convection

The present generation of general circulation models (GCM) use parameterized cumulus schemes and run at hydrostatic grid resolutions. To improve the representation of cloud-scale moist processes and landeatmosphere interactions, a global, Multi-scale Modeling Framework (MMF) coupled to the Land Information System (LIS) has been developed at NASA-Goddard Space Flight Center. The MMFeLIS has three components, a finite-volume (fv) GCM (Goddard Earth Observing System Ver. 4, GEOS-4), a 2D cloud-resolving model (Goddard Cumulus Ensemble, GCE), and the LIS, representing the large-scale atmospheric circulation, cloud processes, and land surface processes, respectively. The non-hydrostatic GCE model replaces the single-column cumulus parameterization of fvGCM. The model grid is composed of an array of fvGCM gridcells each with a series of embedded GCE models. A horizontal coupling strategy, GCE4fvGCM4Coupler4LIS, offered significant computational efficiency, with the scalability and I/O capabilities of LIS permitting landeatmosphere interactions at cloud-scale. Global simulations of 2007e2008 and comparisons to observations and reanalysis products were conducted. Using two different versions of the same land surface model but the same initial conditions, divergence in regional, synoptic-scale surface pressure patterns emerged within two weeks. The sensitivity of largescale circulations to land surface model physics revealed significant functional value to using a scalable, multi-model land surface modeling system in global weather and climate prediction.

cumulus schemes↗

NASA NTRS2021

Understanding the Impacts of Land Surface and PBL Observations on the Terrestrial and Atmospheric Legs of Land-Atmosphere Coupling

Accurately representing land-atmosphere (L-A) interactions and coupling in NWP systems remains a challenge. New observations, incorporated into models via assimilation or calibration, hold the promise of improved forecast skill, but erroneous model coupling can hinder the benefits of such activities. To better understand model representation of coupled interactions and feedbacks, this study demonstrates a novel framework for coupled calibration of the Single Column Model (SCM) capability of the NASA Unified Weather Research and Forecasting (NU-WRF) system coupled to NASA’s Land Information System (LIS). The local land-atmosphere coupling (LoCo) process chain paradigm is used to assess the processes and connections revealed by calibration experiments. Two summer case studies in the U. S. Southern Great Plains are simulated in which LSM parameters are calibrated to diurnal observations of LoCo process chain components including 2-meter temperature, 2-meter humidity, surface fluxes (Bowen ratio), and PBL height. Results show a wide range of soil moisture and hydraulic parameter solutions depending on which L-A variable (i.e. observation) is used for calibration, highlighting that improvement in either SHP or ISM when not in tandem with the other can provide undesirable results. Overall, this work demonstrates that a process chain calibration approach can be used to assess L-A connections, feedbacks, strengths, and deficiencies in coupled models, as well as quantify the potential impact of new sources of observations of land-PBL variables on coupled prediction.

Patricia Lawston-Parker↗

NASA NTRS2006

A Fast Infrared Radiative Transfer Model for Overlapping Clouds

A fast infrared radiative transfer model (FIRTM2) appropriate for application to both single-layered and overlapping cloud situations is developed for simulating the outgoing infrared spectral radiance at the top of the atmosphere (TOA). In FIRTM2 a pre-computed library of cloud reflectance and transmittance values is employed to account for one or two cloud layers, whereas the background atmospheric optical thickness due to gaseous absorption can be computed from a clear-sky radiative transfer model. FIRTM2 is applicable to three atmospheric conditions: 1) clear-sky, 2) single-layered ice or water cloud, and 3) two simultaneous cloud layers in a column (e.g., ice cloud overlying water cloud). Moreover, FIRTM2 outputs the derivatives (i.e., Jacobians) of the TOA brightness temperature with respect to cloud optical thickness and effective particle size. Sensitivity analyses have been carried out to assess the performance of FIRTM2 for two spectral regions, namely the longwave (LW) band (587.3 - 1179.5/cm) and the short-to-medium wave (SMW) band (1180.1 - 2228.9/cm). The assessment is carried out in terms of brightness temperature differences (BTD) between FIRTM2 and the well-known discrete ordinates radiative transfer model (DISORT), henceforth referred to as BTD (F-D). The BTD (F-D) values for single-layered clouds are generally less than 0.8 K. For the case of two cloud layers (specifically ice cloud over water cloud), the BTD(F-D) values are also generally less than 0.8 K except for the SMW band for the case of a very high altitude (>15 km) cloud comprised of small ice particles. Note that for clear-sky atmospheres, FIRTM2 reduces to the clear-sky radiative transfer model that is incorporated into FIRTM2, and the errors in this case are essentially those of the clear-sky radiative transfer model.

Niu, Jianguo↗

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.↗