MPAS for Operational Turbulence Forecasts and Other Commercial Applications

The Weather Company (TWC), an IBM Business, has been running the Weather Research & Forecasting (WRF) model operationally for the past 20 years. Model outputs from each forecast cycle are converted into hundreds of unique products utilized by businesses across fields such as aviation, broadcasting, agriculture, and energy, often times supporting mission-critical and life-saving decisions. In order to take advantage of advances in numerical weather predictions to deliver ever more accurate forecasts to more consumers globally, TWC will soon be upgrading to the Model for Predictions Across Scales (MPAS), the next-generation open source numerical weather prediction model developed by the National Center for Atmospheric Research (NCAR). Our software stack, developed specifically to post-process output on rectangular grids from numerical weather prediction systems such as WRF, will also have to be adapted to work with MPAS.

Among our products, TWC produces automated turbulence forecasts based on the Graphical Turbulence Guidance (GTG) algorithms version 2.5, developed at NCAR and running operationally at the Aviation Weather Center of the National Weather Service. We are also working towards operationalizing version 3. The algorithms produce turbulence diagnostics using the atmospheric state variables from model forecasts. The severity outputs are then further calibrated to generate the final graphical aids utilized by commercial pilots, dispatchers, and aviation forecasters. Migrating turbulence postprocessing to MPAS requires special care due to fundamental differences between MPAS and WRF, such as the native non-orthogonal wind representation on the voronoi mesh. In this presentation, we shall discuss some of the key technical challenges we faced in the process of updating our turbulence products for MPAS, and our solutions to these challenges. Multiple formulations and implementations will be compared to highlight the tradeoffs of each choice.