| Methodology for Computing GTG
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|Methodology for Computing GTG Performance
The GTG is the operational version of the Integrated Turbulence
Forecasting Algorithm (ITFA), which has been verified over several
years. Long-term statistics on ITFAs performance are available on
Verification System (RTVS). The statistics that are presented on
the GTG performance page were obtained from the RTVS.
ITFA diagnoses are verified using Yes and No pilot
reports (PIREPs) of turbulence conditions. Only forecasts and PIREPs
located at altitudes of 20,000 ft and above are considered in the
performance statistics, since the current version of GTG is only
intended to forecast Clear-Air Turbulence (CAT) at these
altitudes. These reports and the GTG forecasts are used to compute
three basic statistics: PODy (probability of
detection of Yes PIREPs), PODn (probability of
detection of No PIREPs), and the % Volume
covered by a Yes forecast. Only PIREPs indicating moderate or greater
turbulence severity are used to compute PODy and only PIREPs that
explicitly state "turbulence negative" or smooth are used to
PODy can be interpreted as the proportion of
Yes reports that are correctly classified as having
turbulence conditions. PODn is the proportion of No reports that are
correctly classified as not having turbulence conditions. Thus,
1-PODn can be interpreted as the proportion of negative
reports that are incorrectly classified. The
% Volume is the percentage of the airspace at
20,000 ft and above covered with a Yes turbulence
To create the discrimination and airspace
coverage plots, GTG is converted from a turbulence severity
indicator to a Yes/No forecast, by using a variety of threshold
values (the threshold value for each point is shown on the plots). Grid points
with GTG values greater than the threshold are classified as
"Yes" forecasts; smaller GTG values are classified as
"No" forecasts. Then all of the pairs of statistics are
computed for each threshold and are plotted to create the diagrams.
The discrimination plot shows PODy versus 1-PODn, while the
airspace coverage plot shows PODy versus % Volume.
The discrimination diagram essentially is a
"relative operating characteristic" (ROC) plot, based on an
area of research called signal detection theory (SDT; Mason 1982).
This plot measures the ability of a forecasting system to discriminate
between Yes and No observations. It measures the trade-off between
correctly classifying Yes observations and incorrectly classifying No
observations. For forecasts that are skillful, the ROC curve should
lie above the 45-degree line (i.e., curves for better
forecasts lie further toward the upper left corner in the diagram). In
fact, the area under the ROC curve is a measure of skill, and is
called the skill index (SI) on the GTG performance plots. This index
ranges from 0 to 100. SI values greater than 50 indicate the forecasts
have some skill. Larger values indicate greater skill.
The airspace coverage plot measures the
trade-off between correctly classifying Yes observations, and
covering a large amount of airspace with a Yes forecast.
Unfortunately, due to the nature of PIREPs, it is inappropriate to
compute standard measures of over-warning such as the False Alarm
Ratio (FAR; Brown and Young 2000). Thus, the airspace coverage
plot provides an alternative measure of over-warning. Better
forecasts are indicated by curves that are closer to the upper left
corner. Together, the discrimination plot and the airspace coverage
plot provide a relatively complete picture of ITFA performance.
For more information about GTG performance, see
Brown et al. (2002) which is the quality assessment report for
GTG. Additional information about the verification approach is
included in Brown et al. (1997). Additional information about GTG
performance is also presented in Brown et al. (2000).
- Brown, B.G., G. Thompson, R.T. Bruintjes, R. Bullock, and T.
Kane, 1997: Intercomparison of in-flight icing algorithms. Part
II: Statistical verification results. Wea. Forecasting, 12,
- Brown, B.G., and G.S. Young, 2000: Verification of icing and
turbulence forecasts: Why some verification statistics can't be
computed using PIREPs. Preprints, 9th Conference on Aviation,
Range, and Aerospace Meteorology, Orlando, FL, 11-15
September, American Meteorological Society (Boston), 393-398.
- Brown, B.G., J.L. Mahoney, J. Henderson, T.L. Kane, R. Bullock,
and J.E. Hart, 2000: The turbulence algorithm intercomparison
exercise: Statistical verification results. Preprints, 9th
Conference on Aviation, Range, and Aerospace Meteorology,
Orlando, FL, 11-15 Sept., American Meteorological Society
- Brown, B.G., J.L. Mahoney, R. Bullock, M.B. Chapman, C. Fischer,
T.L. Fowler, J.E. Hart, and J.K. Henderson, 2002: Integrated
Turbulence Forecasting Algorithm (ITFA): Quality Assessment Report.
Report to the FAA Aviation Weather Research Program.
- Mason, I., 1982: A model for assessment of weather forecasts. Australian
Meteorological Magazine, 30, 291-303.
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