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 1.1-km hourly output was 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 because the data 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 is implemented for execution for each dynamical downscaling simulation upon user request. This way, SOMs can 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.


  • Ensemble intra-seasonal forecasting capability has been tested in the new version running on an HPC. This capability includes downloading Climate Forecast System (CFS) every day for the 6-months-ahead period, which can be used as initial and boundary conditions for WRF high-resolution simulations. The user can select any period between today and 6 months ahead to perform WRF simulations to downscale CFS forecasts in any part of the world.
  • Testing a novel, fast downscaling technique. NCAR has been investigating 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) have be performed only for a period shorter than 30 years (12 years) while lower resolution simulations of the parent grid (~3 km) will cover the whole 30-year period. High-resolution simulations are 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.


  • Testing ECMWF-ERA-5 reanalysis. When running GCAT in the “climo” mode, an off-line comparison will be made using ERA-5 (ECMWF) reanalysis fields in addition to the CFSR (NOAA) fields currently in use. As a preliminary test, ERA-5 will be used to run WRF at 1-km resolution for 4 months over Dugway Proving Ground. A comparison with the available observations will assess the WRF performances with respect to using CFSR fields as initial and boundary conditions. A “climo” job will run using both re-analysis (CFSR and ERA5) fields and the performances compared. 
  • Analog Ensemble for downscaling. NCAR will deploy the analog ensemble (AnEn) downscaling tool into the operational GCAT system. In the next period, we will complete this implementation. So far, all scripts have been written in highly portable languages so they can run on many high-performance computing systems (i.e., Fortran, Python, and bash scripts). The remaining steps in this the GCAT deployment will include the following:
  1. Finalize the predictor weighting based on developmental testing described above;
  2. Couple the AnEn scripts with GCAT and offer it as a standard user-selected option;
  3. Test the updated GCAT climo system in a realistic HPC setting, which will include comparing with the 4-grid climo benchmark for accuracy and speed at various grid configuration settings; and
  4. Test the system out for a couple of other domains to examine its performance in other types of topography and climatological conditions.

As a more experimental part of development in the realm of computationally inexpensive downscaling, NCAR will examine as an alternative to the AnEn a machine learning approach to downscaling. In particular, the convolutional neural network (CNN) is well suited for this task since it can be used to identify important spatial patterns from the training data that relate to the patterns seen in the testing period. In other words, instead of conducting a grid-point based downscaling with the AnEn, the CNN would consider the entire domain in generating a downscaled, fine-scale meteorological field from coarse WRF grids. The training dataset (5 years of data for a month of interest) are sufficiently large for machine learning techniques. Moreover, the CNN may be able to provide an alternative to the SOM itself in defining typical days. The CNN has proven to be a powerful classification and diagnostic tool in various geosciences applications, including estimating hurricane intensity from satellite imagery, automatically classifying convective storm modes (e.g., supercell, bow echo, squall line) from radar imagery, and accurately estimating the probability of hail from radar imagery. It is now being used as a post-processing tool for numerical weather prediction as well.

  • Seasonal Forecasting Refinement. NCAR will keep optimizing and will implement the seasonal ensemble forecasting system on the Centennial supercomputer in the next period. We will extensively evaluate the seasonal ensemble forecasts using both the analyses fields and the observations that have been collected. The specific tasks will include:
  1. Implementing the seasonal ensemble forecasting system on Centennial.
  2. Testing with 1.5-month (sub-seasonal), 3-month (seasonal), and 6-month (2-seasonal) forecasting configurations.
  3. Evaluating the sub-seasonal, seasonal, and 2-seasonal ensemble forecasts using the analyses and observations.
  4. Issuing the sub-seasonal, seasonal, and 2-seasonal ensemble forecasts in near real-time.
  5. Testing the typical days' SOM classification based on seasonal forecasting. WRF simulations will use CSF forecast fields as initial and boundary conditions to generate downscaled predictions for a month in the future at different lead times (sub-seasonal, seasonal, 2-seasonal). The results will be compared to those obtained by the “standard” GCAT approach based on reanalysis of the last 30 years. We will focus this study on Dugway Proving Ground, where several weather stations are available.



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

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

[3] Alessandrini S, Hacker J, Vandenberghe F Definition of typical-day dispersion patterns as a consequence of a hazardous release, Proceedings of 34th International Technical Meeting on Air Pollution Modelling, Montpellier, France, May 2015.\

[4] Ferrero, E., Vandenberghe, F., Alessandrini, S. and Mortarini, L., 2016, December. Comparison of WRF PBL Models in Low-Wind Speed Conditions Against Measured Data. In International Technical Meeting on Air Pollution Modelling and its Application (pp. 129-134). Springer, Cham.