The GTG algorithm uses forecast fields from the RAP forecast model distributed by the National Weather Service's NCEP. With each release of a new RAP forecast time, several turbulence diagnostics are automatically computed from the RAP forecast output and combined to provide the final GTG3 product. An explanation of the algorithms and the GTG formulation can be found in Sharman et al. (2006). Basically, the algorithm combines the results of several turbulence diagnostics (listed below) and weights the diagnostics based on comparisons to observations (i.e. Pilot Reports (PIREPs) or in situ EDR data). The final turbulence forecast is in EDR units and are interpolated to flight levels (FLs) from the native RAP grid for display.
The actual GTG algorithm uses different sets of diagnostics and weights for low-levels (below 10,000 ft MSL), mid-levels (10,000 feet MSL and FL200) and upper levels (above FL200). The three separate regions are blended at the boundaries between the altitude bands. GTG outputs grids on 36 flight levels separated by 1000 feet intervals, regardless of the altitude. A flight level is a constant pressure surface referenced to a world-wide sea level pressure datum (1013.25 hPa). Thus the flight level is referenced to a standard atmosphere; and the actual altitude and flight level will not generally be the same, although typically the difference is small. Flight levels are a convenient way to ensure adequate vertical spacing at altitudes above all terrain. At lower altitudes, terrain avoidance is of primary concern and flight altitudes are typically defined as MSL altitude. In the US and Canada the transition altitude (to the flight level regime) was set at 18,000 feet, while in Europe and other parts of the world it is implemented based on terrain height surrounding the airport or on the runway elevation. The GTG3 reports all forecasts on flight levels both above and below the transition altitude of 18,000 MSL with the understanding that the difference between flight level and actual MSL altitude is small.
At upper-levels, the following 7 turbulence diagnostics are used within the GTG-3.0 combination (see Sharman (2006) for an explanation of each index):
At mid-levels, the following 5 indices are used:
And at low-levels, the following 5 indices are used:
The Graphical Turbulence Guidance (GTG) system product is a fully automated system providing turbulence analyses and forecasts for the aviation community over the contiguous U.S., parts of Mexico and Canada, and the western Atlantic Ocean and eastern Pacific Ocean. Funding for the product has been through the FAA’s Aviation Weather Research Program (AWRP) since about 2001. Its basic algorithmic structure is described in detail in Sharman et al. (2006). Briefly, the system automatically pulls full resolution native numerical weather prediction (NWP) model forecast grids as they become available, then computes an ensemble of turbulence diagnostics on a grid point by grid point basis, combines them, and finally interpolates the combination onto flight levels. GTG output was first made available on NOAA’s Aviation Digital Data Service (ADDS) website in March 2003.
Since then the GTG product has gone through a continuous cycle of upgrades, and each major upgrade is independently evaluated before being made available on the ADDS web site. The capabilities of the various releases are as follows:
As with previous versions, GTG2.5 underwent rigorous independent statistical performance evaluations, which were completed in Aug 2011. GTG2.5 then became viewable on the ADDS website in May 2012.
References:
Benjamin, S.G., D. Devenyi, T. Smirnova, S. Weygandt, J.M. Brown, S. Peckham, K. Brundage, T.L. Smith, G. Grell, and T. Schlatter, 2006: From the 13-km RUC to the Rapid Refresh. 12th Conf. on Aviation, Range, and Aerospace Meteorology (ARAM), Atlanta, GA, Amer. Meteor. Soc. CD-ROM, 9.1
Cornman, L. B., C. S. Morse, and G. Cunning, 1995: Real-time estimation of atmospheric turbulence severity from in-situ aircraft measurements. J. Aircraft, 32, 171-177.
Sharman, R., C. Tebaldi, G. Wiener, and J. Wolff, 2006: An integrated approach to mid- and upper-level turbulence forecasting. Wea. Forecasting, 21, 268-287.
Sharman, R. D., L. B. Cornman, G. Meymaris, J. Pearson, and T. Farrar, 2014: Description and derived climatologies of automated in situ eddy dissipation rate reports of atmospheric turbulence. J. Appl. Meteor. Climatol., 53, 1416-1432, doi:10.1175/JAMC-D-13-0329.1
Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X.-Y. Huang, W. Wang, and J. G. Powers, 2008: A description of the Advanced Research WRF version 3. NCAR/TN-475+STR, 113 pp. [Available at http://www.mmm.ucar.edu/wrf/users/docs/ arw_v3.pdf.]