The Goddard Cumulus Ensemble (GCE) model, a cloud resolving model (CRM), has been developed and improved at NASA Goddard Space Flight Center over the past two decades. The development and main features of the GCE model were published in Tao and Simpson (1993) and Tao et al. (2003b). A review of the applications of the GCE model to develop a better understanding of precipitation processes can be found in Simpson and Tao (1993) and Tao (2003), and most recently in Tao et al. (2014). The 3D version of the GCE model is typically run using 256 x 256 up to 1024 x 1024 horizontal grid points at 1-2 km resolution or better. An MPI version of the GCE model was recently developed (Juang et al. 2006). It is well documented and easy to modify and improve. It is also flexible enough to run on many different platforms using any number of CPUs.
A Kessler-type two-category liquid water (cloud water and rain) microphysical formulation is used with a choice of two three-class ice formulations (3ICE), namely that by Lin et al. (1983) and the Lin scheme modified to adopt slower graupel fall speeds as reported by Rutledge and Hobbs (1984). The sedimentation of falling ice crystals was recently included in the GCE scheme based on Heymsfield and Donner (1990) and Heymsfield and Iaquinta (2000) and was discussed in detail in Hong et al. (2004). An improved four-class, multiple-moment ice scheme (4ICE) has been developed and tested for several convective systems in different geographic locations (Ferrier 1994; Ferrier et al. 1995). The 4ICE scheme requires only minimal tuning compared to the 3ICE schemes. In addition to the 4ICE scheme, two detailed, spectral-bin models (Khain et al. 1999, 2000; Chen and Lamb 1999) have been implemented into the GCE model. Atmospheric aerosols are described using number density size-distribution functions. The explicit spectral-bin microphysics can be used to study cloud-aerosol interactions and nucleation scavenging of aerosols as well as the impact of different concentrations and size distributions of aerosol particles upon cloud formation. Please see Appendix A for a more detailed description of the GCE model. These new microphysical schemes require the multidimensional positive definite advection transport algorithm (MPDATA; Smolarkiewicz and Grabowski 1990) to avoid "decoupling" between mass and number concentration. Solar and infrared radiative transfer processes (Chou and Suarez 1999; Chou et al. 1999). Subgrid-scale (turbulent) processes in the GCE model includes the effects of both dry and moist processes on the generation of subgrid-scale turbulent kinetic energy (Klemp and Wilhelmson 1978; Soong and Ogura 1980).
The GCE model has been used to understand the following:
- The role of the water and energy cycles in the tropical climate system,
- The redistribution of ozone and trace constituents by individual clouds and well-organized convective systems over various spatial scales,
- The relationship between the vertical distribution of latent heating (phase changes of water), surface rainfall and the large-scale (pre-storm) environment,
- Testing hypotheses of deep convection feedback related to global warming,
- The precipitation processes (i.e., precipitation efficiency),
- Aerosol impact on cloud and precipitation in different environments,
- Impact of surface process on precipitation and rainfall,
- The assumptions used in the representation of cloud and convective parameterization in climate and global circulation models, and
- The representation of cloud microphysical processes and their interaction with radiative forcing over tropical and mid-latitude regions.
Among them, the GCE model was extensively used to study tropical convection and its relation to the large-scale environment during the past three decades. Typically, the large-scale effects derived from observations are imposed into the models as the main forcing. When the imposed large-scale advective forcing cools and moistens the environment, the model responds by producing clouds through condensation and deposition. The fall out of large precipitation particles produces rainfall at the surface. The larger the advective forcing, the larger the microphysical response (rainfall) the model can produce (Soong and Tao 1980, Soong and Tao 1980; Tao and Simpson 1984). On the other hand, the model will not produce any cloud or rainfall when the imposed large-scale advective forcing stabilize and/or dries the atmosphere. The GCE model need to use cyclic lateral boundary conditions to ensure that there was no additional heat, moisture or momentum forcing inside the domain apart from the large-scale forcing.
The GCE model also requires a large horizontal domain to allow for the existence of an ensemble of clouds/cloud systems of different sizes in various stages of their lifecycles. This approach also allows the GCE model to conduct multi-days up to multi-weeks integration (e.g., Zeng et al. 2007; Lee et al. 2009; Tao et al. 2010). In these multi-day to multi-week integrations, the model has performed reasonably well in terms of rainfall, LH and moisture budget structure compared to observations, when it is driven by observed large-scale forcing derived from sounding networks. In addition, the model results can provide cloud statistics that represent different types of clouds/cloud systems during their life cycle. This type of cloud-resolving modeling was used for many different field campaigns, including the GATE, the TOGA COARE, the DOE/ARM, the SCSMEX and other campaigns (see a review by Tao 2003 and Tao and Moncrieff 2009).
Evolution of apparent heat source (Q1) averaged over TOGA COARE IFA for 8-day period 19-27 December 1992: (a) derived diagnostically from soundings (Lin and Johnson 1996); and simulated from GCE-CRM model over: (b) entire region, (c) convective region, and (d) stratiform region. [Red contours -- positive; blue contrours -- negative. Contour interval is 5ºC day-1.]
Vertical profiles of accumulated domain-time averaged LH components over (a) convective region and (b) stratiform region consisting of condensation (solid red), evaporation (solid blue), deposition (dashed red), sublimation (dashed blue), freezing (solid brown), melting (solid turquoise), and total (solid black).
Three left-most pairs of diagrams illustrate isometric projections of volume hydrometeor distributions (upper panels) and plan-view near-surface rain rates (lower panels) for instantaneous realizations from GCE-CRM simulations of SCSMEX, KWAJEX, and DOE-ARM MCS storm cases. Upper panel iso-surface color scheme assigns: (i) white for cloud droplets and ice crystals, (ii) blue for snow, (iii) red for graupel and hail, and (iv) green for rain. Right-most diagram pair shows near-surface, forward-modeled radar reflectivities for TRMM-LBA easterly (upper panel) and westerly (lower panel) regime MCS cases.
KWAJEX rainfall time series consisting of measured and modeled rain rates. Both KPOL-Radar measurements and TRMM-PR retrievals are given for entire IOP, while GCE-CRM simulated estimates are provided from 29 August through 12 September (both 2D and 3D CRM designs are used for simulated rainfall). [Top diagram from Yuter et al. (2004).]
KWAJEX Q1 heating time series consisting of diagnostically-calculated and GCE-CRM simulated profiles for three different time series during 1999 IOP: (a) 7-12 August (5-day), (b) 18-21 August (3-day), and (c) 29 August - 12 September (15-day). Green and red contours indicate positive and negative apparent heating regions, respectively.
Tao, W.-K., and J. Simpson, 1993: The Goddard Cumulus Ensemble Model. Part I: Model description. Terrestrial, Atmospheric and Oceanic Sciences, 4, 35-72.
Simpson, J. and W.-K. Tao, 1993: The Goddard Cumulus Ensemble Model. Part II:Applications for studying cloud precipitating processes and for NASA TRMM.Terrestrial, Atmospheric and Oceanic Sciences, 4, 73-116.
Tao, W.-K., J. Simpson, D. Baker, S. Braun, M.-D. Chou, B. Ferrier, D. Johnson, A. Khain, S. Lang, B. Lynn, C.-L. Shie, D. Starr, C.-H. Sui, Y. Wang and P. Wetzel, 2003: Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model, A Special Issue on Non-hydrostatic Mesoscale Modeling, Meteorology and Atmospheric Physics, 82, 97-137.
Tao, W.-K., 2003: Goddard Cumulus Ensemble (GCE) model: Application for understanding precipitation processes, AMS Meteorological Monographs – Cloud Systems, Hurricanes and TRMM. 107-138.
Tao, W.-K., S. Lang, X. Zeng, X. Li, T. Matsui, K. Mohr, D. Posselt, J. Chern, C. Peters-Lidard, P. M. Norris, I.-S. Kang, I. Choi, A. Hou, K.-M. Lau, and Y.-M. Yang, (2014), The Goddard Cumulus Ensemble model (GCE): improvements and applications for studying precipitation processes, Atmospheric Research, 143, 392-424. doi: http://dx.doi.org/10.1016/j.atmosres.2014.03.005
Juang, H. M., W.-K. Tao, X. Zeng, C.-L. Shie and J. Simpson, 2007: Parallelization of a cloud-resolving model for massively
More than 140 refereed papers using the GCE model have been published in the last three decades.