Distributions Used in the Development of a Low-Level Turbulence Algorithm

Low level turbulence provides a threat to the safety of personnel and property within aviation operations. Providing timely, accurate forecasts of this hazard in an operationally-favorable format is considered a high priority within the 16th Weather Squadron of the Air Force Weather Agency (AFWA).

Current methodology for forecasting low level turbulence within AFWA can become complicated quite quickly, as no comprehensive method for forecasting all turbulence currently exists. The Panofsky Turbulence Index (PTI) does well in some cases, such as areas of strong winds or areas of intense low level instability. However, the PTI struggles in several other areas, such as cases where low level instability does not reach a given threshold, or cases of turbulent waves. Current AFWA mountain wave forecasting methodology involves a box methodology for areas of favored terrain overlaid with wind parameters. This method has proven effective in the western United States, but this process can be cumbersome, and this box methodology has not been designed for every region of terrain in the world. Few products are currently available to account for other turbulent waves, such as gravity waves. The limitations of these forecasting methodologies must be addressed within the confines of operational forecast models. Furthermore, it is hoped that a single forecast product will save time in the forecasting process.

Creating a forecasting technique that encompasses parameterizations of the variety of physical processes responsible for low level turbulence requires a statistical balance of these various processes. A “contribution value” for each physical process is calculated based on numerical model output. These distributions are then balanced to produce a deterministic 4-element turbulence algorithm. Finally, probabilities of light, moderate, and severe turbulence are developed based on this deterministic algorithm result.

This algorithm has been developed using the 15-KM Advanced Research Weather Research and Forecasting (WRF-ARW) model at AFWA. This poster will display the various distributions used for each physical process, the methodologies used to arrive at the final distributions, and verification of the 4-element low level turbulence algorithm.