While GTG forecasts turbulence based on NWP model data, limitations of the model accuracy, resolution and timeliness limit GTG's ability to adequately diagnose convectively–induced turbulence (CIT). The NEXRAD Turbulence Detection Algorithm (NTDA; see Radar section) partially addresses this deficiency by measuring in–cloud turbulence. However, there may often be regions of CIT above or adjacent to storms that are in clear air or otherwise not detectable by Doppler weather radar. To address this gap, the Diagnose CIT (DCIT) algorithm is being developed. Since DCIT is primarily concerned with resolving areas of turbulence close to but not in convective clouds, traditional PIREPs cannot be used as "truth" in evaluation and tuning because of their inherent location and timing uncertainty. Therefore in situ EDR data are required to tune and verify the DCIT algorithm.
DCIT is being developed using statistical learning and analysis techniques that provide estimates of the importance of candidate predictor variables and produces an empirical predictive model. An initial version of DCIT based on RUC model, NEXRAD, and geostationary satellite data has been developed and is running operationally at NCAR. A prototype version based on WRF RAP NWP data will be deployed as part of the GTGN product software in the 2013 time frame.
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