Weather forecasts are essential in developing more predictable and efficient Traffic Flow Management (TFM) decisions. Shorter-range decisions can be made with deterministic forecasts. For longer-range decisions beyond about one hour, probabilistic weather forecasts can be useful. However, simply having weather forecasts is not sufficient to make more predictable and efficient TFM decisions. There needs to be a method to translate weather forecasts into predicted airspace capacity. There are simple weather translation models that suggest that all aircraft avoid regions of intense convective activity above a particular threshold, thereby reducing airspace capacity. Those simple models can be overly conservative and result in more lost capacity than necessary. More sophisticated weather translation models evaluate several meteorological and non-meteorological parameters to determine TFM impact and can minimize the reduction of capacity by acknowledging that some aircraft can penetrate particular weather constraints instead of avoiding them. Work to create more sophisticated models has focused on using deterministic information, but probabilistic information will likely be used for longer-range TFM planning.

In this project, Mosaic ATM is developing models to translate both deterministic and probabilistic meteorological and non-meteorological information into a TFM impact model for weather constraints other than convection that affect en route operations. Convection has the largest impact to en route operations. Other constraints that affect en route operations include turbulence, icing, and fog. Constraints that do not occur in en route airspace, such as fog that affect ceiling and visibility, can also affect operations beyond where they occur. The output of this research project is algorithms, procedures, and protocols for creating and using weather translation models for convective and non-convective meteorological phenomena that affect en-route operations to determine TFM impact.

The following figures show initial results of an airspace capacity estimation model developed under this project that represents directional demand vs. capacity in wind-rose charts.

The following figure captures work conducted on development of a model that uses the probabilistic forecast of stratus clearing time at SFO to recommend Ground Delay Program end times.