The National Airspace System (NAS) continues to suffer from air traffic delays related to convective weather activity. The Air Traffic Management (ATM) community recognizes that current deterministic weather forecasting accuracy is insufficient for making strategic Traffic Flow Management (TFM) and meteorologists do not anticipate achieving the needed improvements to deterministic weather forecasting in the foreseeable future.  Therefore TFM must make the best possible use of current weather forecasting technology, which includes understanding and using the uncertainty associated with today's convective weather forecasts.  Probabilistic ATM-Weather solutions are needed to improve the predictability and efficiency of the NAS in weather impacted situations, yet challenges exist in the ATM automation design, definition of needed probabilistic forecast products and the integration of the two.

Leveraging the collaboration of ATM and aviation weather forecasting communities, potential approaches for integrating probabilistic weather forecasts with ATM decision making were identified and discussed.

To create a context for analysis, these concepts were compared within a framework[1] where the use of weather forecasts in ATM decision making is viewed form a high level as a three-step process:

1. Creation of Weather Forecasts
2. Translation of Weather Forecasts to ATM Impacts
3. (ATM) Decision Making Based on ATM Impacts due to Weather

Whether using deterministic or probabilistic forecasts, these steps can describe the process to achieve ATM decisions around weather constraints. Although the steps are coupled, this view allows the ATM-weather integration problem to be broken down into digestible components and enables researchers to focus on the most critical issues.

The state-of-the-art concepts for using probabilistic weather forecasts in ATM decision making, mainly described from the perspective of ATM, use various concepts and techniques to address a range of issues. Several of these concepts address the complexities of making decisions using uncertain airspace demand and uncertain capacity constraints. Others provide techniques for calculating capacity uncertainty from weather forecast uncertainty. A few concepts attempt to define probabilistic weather forecast requirements needed for ATM decision making.

A shortcoming of the 3-step decomposition, however, is that solutions may be non-transferrable. Specific information about the weather event and details of the impacted airspace may be lost in breaking down the problem. For example, the forecast created to mitigate the marine stratus layer (fog) at San Francisco International Airport is not transferable to predicting the lake effect affecting Chicago O'Hare International Airport. The 3-step view was, therefore, considered as one of two approaches to using weather forecasts in ATM decision making. A second “Integrated” approach is also considered as a viable method to identifying solutions for using probabilistic weather forecasts for ATM-decision making.

In order to more clearly articulate the similarities and differences between ongoing research efforts in this area the state-of-the-art concepts were also analyzed by:

• The potential benefits that these concepts promise,
• The probabilistic forecast requirements needed to implement these concepts,
• What probabilistic forecast products are assumed to be available,
• The key research issues relevant to these concepts, and
• The timeline of technology readiness for these efforts in progress.

The results of this study will provide the ATM and weather communities a cross-comparative view of the state-of-the-art that enables them to review and discuss current research gaps in integrating ATM with probabilistic weather forecasts. A suggested approach to filling these gaps is also provided as a topic for community and agency discussion and planning.