Toward Prototype Medium-range Probabilistic Forecasts for Boundary Layer Turbulence

The Aviation Weather Center (AWC), which provides aviation hazard forecasts for the contiguous United States, currently issues only short-term turbulence-related products. Furthermore, the operational version of the sophisticated Graphical Turbulence Guidance system provides clear-air turbulence (CAT) and mountain-wave turbulence (MWT) forecasts at multiple vertical levels but is limited to the shorter-range turbulence prediction domain [1]. Effective medium-range (days 2+), low-level turbulence forecasts can provide the aviation community – especially the general aviation sector – with decision support at longer lead times. This project makes progress toward a medium-range, probabilistic forecasting tool targeting thermal turbulence below the planetary boundary layer (PBL).

Afternoon pilot reports (PIREPs) of turbulence from small aircraft below the PBL are filtered to remove potential convectively-induced or mountain wave-induced turbulence data. Each PIREP is associated with nearby Global Ensemble Forecast System-derived turbulence predictors and model fields, allowing for the assembly of prototype PBL turbulence indices. These efforts expand upon previous turbulence index work performed by Andy Fischer at AWC. Initial findings, however, suggest that the optimized turbulence indices share similar challenges to Fischer’s index, namely difficulties with distinguishing light-to-moderate and higher turbulence classes. Despite these predictability limitations, this work explores and implements a framework for transitioning boundary-layer turbulence predictors to uncalibrated probabilistic turbulence forecasts using random forest classifiers. These prototype forecasts are run and visualized for multiple case studies.

[1] Wandishin, M. S., Paulik, L., Hart, J., Etherton, B., & Petty, M.A. (2014). Assessment of the Graphical Turbulence Guidance Version 3 (OAR GSD-53; NOAA Technical Memorandum). Earth System Research Laboratory: Global Systems Division. https://doi.org/10.7289/V5/TM-OAR-GSD-53