SBIR/STTR Award attributes
Severe weather remains the main disruptor to airspace operations and traffic managersrsquo; 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 needsmdash;TMI impact reductionmdash;to demonstrate its capabilities.Todayrsquo;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 amp; 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 feedPerform 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-severeIf warranted, run a set of parallel fast-time simulations starting from current NAS status and extending up to 6-8 hours ahead, combining two ldquo;what-ifrdquo; series of experiments:Meteorologically sound range of alternative weather scenario outcomes representing the underlying forecast uncertaintyParameterized TMI reductions in scope and end timesEvaluate 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 existsAlert relevant traffic managers for review and actionContinue autonomously monitoring and looking for additional TMI reduction opportunities throughout the operational day
