An automated procedure for forecasting mid- and upper-level turbulence that affects aircraft is described. This procedure, termed the Graphical Turbulence Guidance system, uses output from numerical weather prediction model forecasts to derive many turbulence diagnostics that are combined as a weighted sum with the relative weights computed to give best agreement with the most recent available turbulence observations (i.e., pilot reports of turbulence or PIREPs). This procedure minimizes forecast errors due to uncertainties in individual turbulence diagnostics and their thresholds. Thorough statistical verification studies have been performed that focused on the probabilities of correct detections of yes and no PIREPs by the forecast algorithm. Using these statistics as a guide, the authors have been able to intercompare individual diagnostic performance, and test various diagnostic threshold and weighting strategies. The overall performance of the turbulence forecast and the effect of these strategies on performance are described.

Climatologies of the regional, seasonal, and temporal distributions of upper-level (18 000-60 000-ft MSL) turbulence over the contiguous United States (CONUS) are constructed using pilot reports (PIREPs) of aircraft turbulence encounters. The PIREP database used contains over two million entries, and encompasses 12 complete years of data, from January 1994 through December 2005. In spite of known variability in pilot reporting practices, it was found that PIREPs are very consistent among themselves for the null and moderate-or-greater (MOG) intensity categories. Air traffic pattern biases were accounted for by considering only statistics of MOG/total report ratios. Over the CONUS, regional maxima are evident in MOG/ total ratios over mountainous regions in the west, over the south and southeast, and over the North Atlantic seaboard. Some additional investigations are presented to help to identify possible origins of the turbulence using a smaller time interval of PIREPs in comparison with archived 20-km Rapid Update Cycle (RUC) NWP model analyses, satellite and radar-based cloud-top and cloud-base analyses, and lightning flash data, as well as topography statistics.

While traditional verification methods are commonly used to assess numerical model quantitative precipitation forecasts (QPFs) using a grid-to-grid approach, they generally offer little diagnostic information or reasoning behind the computed statistic. On the other hand, advanced spatial verification techniques, such as neighborhood and object-based methods, can provide more meaningful insight into differences between forecast and observed features in terms of skill with spatial scale, coverage area, displacement, orientation, and intensity. To demonstrate the utility of applying advanced verification techniques to mid-and coarseresolution models, the Developmental Testbed Center (DTC) applied several traditional metrics and spatial verification techniques to QPFs provided by the Global Forecast System (GFS) and operational North American Mesoscale Model (NAM). Along with frequency bias and Gilbert skill score (GSS) adjusted for bias, both the fractions skill score (FSS) and Method for Object-Based Diagnostic Evaluation (MODE) were utilized for this study with careful consideration given to how these methods were applied and how the results were interpreted. By illustrating the types of forecast attributes appropriate to assess with the spatial verification techniques, this paper provides examples of how to obtain advanced diagnostic information to help identify what aspects of the forecast are or are not performing well.