Global Climatological Analysis Toolkit


RAL scientists continue to support the DoD’s National Ground Intelligence Center (NGIC) in its mission of assessing the consequences of the transport and dispersion of accidental and intentional releases of hazardous materials into the atmosphere. This is done by providing the agency with access to the RAL-developed GCAT (Global Climate Analysis Toolkit) system. GCAT is a fully-automated dynamical downscaling system that allows NGIC scientists to remotely generate a high-resolution 30-year climatography for any region on the Earth. GCAT is based upon Climate Four-Dimensional Data Assimilation (CFDDA) technologies and can run with four domains, reaching a grid increment of 1.1-km. This enables NGIC to conduct transport and dispersion analyses at very fine scale.  GCAT has the capability to automatically classify WRF output fields into climatological regimes. The method is based on the Self Organizing Map (SOM) [1] artificial neural network pattern recognition technique. Figure 1 shows the results of a SOM classification, in which 30 months (May 1981-2010) of WRF 1.1km hourly outputs were used to estimate the main six regimes of the wind flow over the Energetic Materials Research and Testing Center in Socorro, NM. The six regimes that have been identified are given with their frequency of occurrence and their most representative days, which are chosen based on their Euclidian distance to each SOM node. Weather data valid for the representative days provides better forcing to NGIC’s transport and dispersion climatological studies, as they didn’t undergo averaging which can destroy important model physical properties (balance etc.) available with dynamical downscaling.

Figure 1. Typical days based on SOM classifications for downscaled historical flow during May over Socorro, NM.
Figure 1. Typical days based on SOM classifications for downscaled historical flow during May over Socorro, NM.

The Second-order Closure Integrated PUFF (SCIPUFF) transport and dispersion model has been implemented for execution for each dynamical downscaling simulation upon user request. This allows SOMs to be built based on plume dosage, in addition to weather variables, when analyzing the past climate. The system makes use of the Climate Forecast System Reanalysis (CFSR) data set available on a 0.5-degree grid for initial and lateral boundary conditions.


Updates to GCAT developed in 2016 has been released in a new version. This new version of the toolkit includes:

  • The capability of using additional classifiers (Monin-Obukhov length and upper wind fields) for the SOM classifications has been added to GCAT. Monin-Obukhov length reflects the turbulence characteristics and hence the dispersion processes especially in the lower boundary layer. Upper layer winds are important for correctly represent dispersion processes involving buoyant releases of material rapidly rising to the upper level of the boundary layer.
  • In the previous GCAT release, the SOM classification was carried out using a fixed map made of 5x4 nodes. Classifications with different map dimensions (e.g. 6x5, 7x6) are now possible.

Reanalysis can only be run with one emission source located at the center of the domain. The ability to run SCIPUFF with multiple release points of the same material in different locations has been added.


  • Work with NGIC to develop alternative worst day criteria for various release scenarios. GCAT currently selects the worst day by looking at which day of the input dataset the maximum dosage on a radius of 4 km from the release point occurs.
  • Complete the migration to HPCMP supercomputers: Transition of GCAT system to the DoD HPCMP system will be accomplished.
  • NCAR will investigate the possibility of performing downscaling climatological analysis at a high resolution (~0.51 km) using a limited amount of computational resources. High-resolution WRF simulations (~0.51 km) will be performed only for a period shorter than 30 years (12 year) while lower resolution simulations of the parent grid (~3 km) will cover the whole 30-year period. High resolution simulations will be extended to the 30-year period searching for matching analogs on the parent lower resolution grid. For this purpose, a combination of SOM and analog techniques will be used.


Kohonen T (1995) Self-organizing maps. Springer-Verlag, Heidelberg.

Jun Yan (2010). som: Self-Organizing Map. R package version 0.3-5.

Alessandrini S, Hacker J, Vandenberghe F Definition of typical-day dispersion patterns as a consequence of a hazardous release, Submitted to International Journal of Environment and Pollution.

Ferrero E., Alessandrini S., Vandenberghe F., WRF PBL model comparison against data measured in an urban environment. Submitted to Quarterly Journal of Meteorological Society.