Real-time Weather Awareness for Enhanced Advanced Aerial Mobility Safety Assurance
Abstract
This presentation addresses emerging needs in real-time weather forecasting to improve the safety of low altitude aircraft operations through the integration of real-time observation innovations from autonomous systems with numerical weather prediction capabilities as well as flight management and safety systems. A NASA University Leadership Initiative program, WIND-MAP, will investigate needs for manned and unmanned aircraft improved situational awareness to enhance safety and efficiency, particularly for unmanned traffic management, urban air mobility, and airport operations.
Through delays, diversions, cancellations, and mitigation efforts, weather costs the aviation industry $100 million annually. As such, the industry needs more accurate and higher resolution aviation weather predictions. Airport operations and smaller aircraft, such as Advanced Aerial Mobility (AAM) solutions that include unmanned aircraft and urban air taxis, are emerging in a number of economic sectors (deliveries, infrastructure inspection, public safety) and will greatly benefit from improved forecasting, viz. prediction of low-level winds and turbulence. Today, one of the more advanced operational models over the CONUS, the High-Resolution Rapid Refresh (HRRR), runs with 3-km grid-spacing and assimilates a wealth of observational data. Despite great advances in data assimilation (DA), data analysis and model physics, HRRR forecasts are beginning to plateau. This is due in partly due to the general lack of observations in the atmospheric boundary layer (ABL) and a mismatch between spatio-temporal resolution of the model and the available observations. 3-km grid-spacing is too coarse to resolve key ABL structures as well as terrain- or building-induced gradients, wind-shear, and turbulence, that can impact fog formation, convection initiation, and safe and efficient flight of UAS and UAM as well as airport operations. To address this problem, several technical challenges have been identified by the team. These include (1) developing autonomous UAS capable of conducting observations accurately and reliably; (2) determining the number and frequency of observations, as well as the required sensitivity of observations in data sparse regions of the lower atmosphere; (3) assimilating dense observational data into models in real-time with sufficient resolution and accuracy; (4) developing novel physics-based reduced order models capable of incorporating diverse data sets; and (5) integrating real-time forecasting into UTM and DAA (detect-and-avoid) architectures for path planning and navigation. These challenges center on providing real-time predictive capabilities for real-world dynamic weather environments capable of generating fine scale information for system-wide assurance focused on UAS/UAM trajectory planning, but also applicable to low altitude commercial and ATMx aircraft operations. The presentation will outline program vision and goals and introduce the WIND-MAP team, which has been formed and organized to solve these problems and address current barriers to implementation. Stakeholder and community needs will also be addressed and sought.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMA021...03J
- Keywords:
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- 0305 Aerosols and particles;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 0320 Cloud physics and chemistry;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 3307 Boundary layer processes;
- ATMOSPHERIC PROCESSES;
- 3315 Data assimilation;
- ATMOSPHERIC PROCESSES