Cloud Library

Three modeling systems (GCE, WRF, and MMF) described in the "Models" section have been used in many different experiments, and produced a large number of simulated cloud data sets during the past decade. Some of simulated data sets have been archived in the Cloud Library, which is accesible to public users for model evaluation, development of large-scale cloud parameterization (microphysics, subgrid cloud distributions, overlapping, convection, downdraft), or studying remote sensing singnals through instrumental simulators.

All database are archived in NetCDF format, which describes parameters, units, dimensions, and time. The data volume for each instanteneous simulated unit data set mainly includes 12 atmospheric 3-D parameters (temperature, water vapor, horizontal and vertical velocity, pressure, long-wave and short-wave radiation rates, and five different cloud species, cloud water, rain, cloud ice, snow and graupel, and not limited) and various 2-D parameters (surface rain rate, skin temperature, near-surface wind speed, land characterisics, elevation). Generally, GCE is idealized CRM so that it contains 3D atmospheric parameters and homegeneous surface character, WRF is mesoscale weather model with output of 3D atmospheric parameters and more complete sets of land surface characteristics, MMF contains the GEOS atmospheric/land parameters and 2D GCE atmospehric parametre 

The viewgraphs listed below show some examples from these modeling systems. If you are interested in using these data sets, click on "Access Protocol", it will take you to the cloud library dataportal.


GCE-Cloud Resolving Model:

Coupling of Clouds and Precipitation to Land Surface

Clouds and precipitation are highly coupled with land surface on the timescales of days to months, which challenges current weather and climate prediction models. High-resolution cloud models, coupled with land surface models, can address this process explicitly. Recently, the GCE (Goddard Cumulus Ensemble) model is coupled with LIS (Land Information System), and model results are evaluated with observations.

Oklahoma 2002 Image 1

ARM observed cloud amount

Oklahoma 2002 Image 2

Modeled cloud amount with ARM surface fluxes as input

Oklahoma 2002 Image 3

Modeled cloud amount with LIS surface fluxes as input

Zeng, X., W.-K. Tao, M. Zhang, C. Peters-Lidard, S. Lang, J. Simpson, S. Kumar, S. Xie, J. L. Eastman, C.-L. Shie and J. V. Geiger, 2007: Evaluating clouds in long-term cloud-resolving model simulations with observational data. J. Atmos. Sci. (in press).

Improving the Simulation of Convective Cloud Systems: Higher resolution and more realistic ice physics

The Goddard Cumulus Ensemble (GCE) model is a cloud-resolving model developed at NASA Goddard by Dr. W.-K. Tao to simulate convective cloud systems.

TRIM LBA - February 1999

High resolution simulation of 23 Feb 1999 TRMM LBA case Image by J. Williams (Scientific Visualization Studio)

Hydrometeor Profiles

Improvements to the cloud microphysics results in less high-density ice and more realistic hydrometeor profiles for use in satellite retrievals

Horizontal Model Resolution

Higher horizontal model resolution leads to a more realistic, gradual transition from shallow to deep convection

Lang, S., W.-K. Tao, R. Cifelli, W. Olson, J. Halverson, S. Rutledge, and J. Simpson, 2007: Improving simulations of convective systems from TRMM LBA: Easterly and westerly regimes. J. Atmos. Sci., 64, 1141-1164.

Impact of Aerosol on Precipitation Processes


Use Goddard Cloud Ensemble Model with spectral-bin microphys to asses the impact of atmospheric aerosol concentration on deep convections.

Hydrometeor Profiles

Dirty (or high) CCN can either suppress or enhance precipitation processes, depending on environmental conditions and cloud dynamics/microphysics interactions; Clean (Low) CCN produces earlier rain onset and enhances surface rain only at initial stages;
CCN variations can modulate surface rainfall characteristics, e.g. stratiform area and intensity.

Tao, W.-K., X. Li, A. Khain, T. Matsui, S. Lang, and J. Simpson, 2007: The role of atmospheric aerosol concentration on deep convective precipitation: Cloud-resolving model simulations. J. Geophys. Res., submitted.

Coupled WRF-GCE-LIS (High-resolution Weather Forecast Model):

Importance of the microphysics in high-resolution weather forecast models

Microphysical (cloud) processes developed at NASA Goddard were implemented into a next generation of weather forecast model (e.g. WRF). The explicit and realistic representation of microphysics in the high-resolution numerical weather forecast model is crucial for accurate prediction of the midlatitude mesoscale convective systems and the intensity and track of hurricanes.

Huricane Katrina Paths

Three-day forecast tracks for Hurricane Katrina (2005). The actual track is shown in black.

WRF Simulation from June 12,2002

WRF simulation - accumulated precipitation at 4 resolutions from IHOP June 12 2002 case

Tao, W.-K., J. Shi, S. Chen, S. Lang, S.-Y. Hong, G. Thompson, C. Peters-Lidard, A. Hou, S. Braun, and J. Simpson, 2007: Revised bulk-microphysical schemes for studying precipitation processes: Part I: Comparison with different microphysical schemes, Mon. Wea. Rev., (submitted).

Global Mesoscale Model (fvGCM):

Forecasts of Katrina's Track, Intensity, Structures with a Global Mesoscale Model

It is known that General Circulation Models (GCMs) have insufficient resolution to accurately simulate hurricane near-eye structure and intensity. Their physics packages (e.g., cumulus parameterizations) are also known limiting factors in simulating hurricanes.

Six 5-day simulations of Katrina at both 0.25o and 0.125o show comparable track forecasts, but the higher-resolution (0.125o) runs provide much better intensity forecasts, producing the center pressure with errors of only +- 12 hPa. Realistic near-eye wind distribution and vertical structure are also obtained as cumulus parameterizations are disabled.

Shen, B.-W., R. Atlas, O. Reale, S.-J. Lin, J.-D. Chern, J. Chang, C. Henze, J.-L. Li, 2006: Hurricane Forecasts with a Global Mesoscale-resolving model: Preliminary results with Hurricane Katrina (2005). GRL, 33, L13812, doi:10.1029/2006GL026143.

Huricane Katrina Landfall Errors

Landfall errors:
e32 (1/4o): 50km, g48(1/8o): 14km, g48ncps (1/8o w/o CPs): 30km

High-resolution runs

GFS Analysis (~35km) valid at 08/29/12z 96 h Simulations with no CPS High-resolution runs simulate realistic intensity, RMW (radius of max wind) and warm core (shaded)

Near-eye Wind Distributions

Near-eye Wind Distributions in a 2ox2o box (a) AOML high-resolution surface wind analysis, (b) the 0.25o 99h simulations, (c) the 0.125o 99h simulations, (d) the 0.125o 96h simulations without convection parameterizations (CPs).

Coupled fvGCM-GCE MMF (Multi-scale Modeling Framework):

Diurnal Variation of Precipitation from NASA Satellites and Goddard MMF

The diurnal cycle is a fundamental mode of atmospheric variability and has a major impact on weather and climate prediction. In addition, it provides a robust test of physical processes represented in atmospheric models that are used for studying the water and energy cycles. Most climate models simulate precipitation too early over both land and ocean.

  Land Ocean
MW 1600-1800 0200-0600
MMF 1600-1800 0200-0600
Climate Model 0800-1000 0000-0400

Distribution of the local solar time (LST)
of precipitation frequency maximum over land and ocean

NASA satellite retrievals in general show that precipitation occurs most frequently in the late afternoon and early morning over major continents and oceans, respectively. The Goddard Multi-scale Modeling Framework (MMF) that replaces the sub-grid cloud parameterization with an explicit cloud-resolving model, is superior to the Goddard fvGCM (a convectional climate model) in reproducing the correct timing of the diurnal cycle of precipitation frequency both over lands and oceans.

General Precipitation

Yellow: Late Afternoon Rain
Blue: Early Morning Rain