SBIR-STTR Award

Severe weather remains the main disruptor to airspace operations and traffic managers’ actions. An autonomous airspace system will need to automatically ingest the latest weather forecast(s), reason about its impact, and provide actionable guidance to human operators (in the transition) and/or other service-based airspace automation systems. Our proposed Innovation lays the foundation for such automated weather reasoning and focuses on a specific aspect of autonomous operation with clearly stated practical needs—TMI impact reduction—to demonstrate its capabilities. Today’s manually executed TMIs are often overly restrictive, and once activated are not routinely reviewed for possible reduction in scope or duration resulting in excess delays & costs. We propose an autonomous system which will monitor the latest weather, traffic, implemented TMIs, and look for opportunities to reduce their impact on the NAS. The application will: Continuously ingest latest NOAA weather forecasts, air traffic, and TMI information from FAA SWIM feed Perform automated Forecast Trend Analysis to compare the latest information to previous forecast(s) and NAS status, and identify when an in-depth search for TMI reduction is warranted, e.g., when forecasts evolve toward less-severe If warranted, run a set of parallel fast-time simulations starting from current NAS status and extending up to 6-8 hours ahead, combining two “what-if” series of experiments: Meteorologically sound range of alternative weather scenario outcomes representing the underlying forecast uncertainty Parameterized TMI reductions in scope and end times Evaluate results (including comparison with the outcomes of previous cycles) to establish, with a required degree of confidence, if a non-trivial and specific TMI reduction opportunity exists Alert relevant traffic managers for review and action Continue autonomously monitoring and looking for additional TMI reduction opportunities throughout the operational day Potential NASA Applications (Limit 1500 characters, approximately 150 words) This autonomous severe weather trend reasoning application supports and could be part of NASA’s goal to enable successful transition to an autonomously operating airspace system. Additionally, this initial application could plug into various NASA simulations needing automated weather and/or TMI monitoring. The underlying technology can provide the framework for other autonomous weather impact reasoning systems that support future airspace uses by new entrants including UAM and UTM. Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words) A direct application of the system to be built is for the FAA ATCSCC who plans and executes NAS-level TMIs. By using this technology, thousands of delay minutes could be saved. A modified version of the technology is applicable to airline operations to help them more readily adapt to changes in weather and TMIs. Other potential applications include UAS, UAM, and international ANSP operators.

Severe weather remains the main disruptor to airspace operations and traffic managers’ actions. An autonomous airspace system will need to automatically ingest the latest weather forecast, reason about its impact, and provide actionable guidance to human operators and/or other service-based airspace automation systems. Our Phase I prototype has laid the foundation for such automated weather reasoning, focusing on a specific aspect of autonomous operation with clearly stated practical needs—TMI impact reduction—to demonstrate its capabilities. Today’s manually executed TMIs are often overly restrictive and are not routinely reviewed for possible reduction in scope or duration, resulting in excess delays & costs. To address this, we are developing an autonomous system which will continuously ingest latest weather forecasts, air traffic & TMI information, perform automated Forecast Trend Analysis to compare this latest information with previous forecast(s), identify when forecast trends toward less-severe, and if warranted, launch a search for TMI reduction opportunities. A “what-if” series of parallel fast-time NAS simulations, projecting current situation up to 8 hours ahead, combines meteorologically sound range of potential weather outcomes (given the forecast uncertainty) and parameterized TMI reductions in scope and end times. The application will evaluate results (including those from prior cycles) to establish, with a required degree of confidence, if a non-trivial TMI reduction opportunity exists. If so, it will alert relevant traffic managers and then continue autonomous monitoring, looking for additional TMI reduction opportunities during the operational day. In Phase II, we will transition from emulated to live real-time operation, with input from the FAA ATC System Command Center, using ensemble forecasts, expanded TMI reduction search, and data mining techniques. We will also leverage this technology into other domains, e.g., UAM and UTM. Potential NASA Applications (Limit 1500 characters, approximately 150 words): This autonomous severe weather trend reasoning application supports and could be part of NASA’s goal to enable successful transition to an autonomously operating airspace system. Additionally, this initial application could plug into various NASA simulations needing automated weather and/or TMI monitoring. The underlying technology can provide the framework for other autonomous weather impact reasoning systems that support future airspace uses by new entrants including UAM and UTM. Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words): A direct application of the system to be built is for the FAA ATCSCC who plans and executes NAS-level TMIs. By using this technology, thousands of delay minutes could be saved. A modified version of the technology is applicable to airline operations to help them more readily adapt to changes in weather and TMIs. Other potential applications include UAS, UAM, and international ANSP operators. Duration: 24