Understanding and Mitigating Clear Air Turbulence (CAT) Impact on Aviation Safety
Nature of the Problem: Over the past decade, an alarming increase in aircraft turbulence, largely attributed to climate change, has been observed. The United States alone experienced approximately 770 reported clear air turbulence (CAT) incidents from 2010 to 2020, causing harm to passengers and flight crew. Recent incidents, including one at a major airport, highlight the urgent need for more precise CAT detection and forecasting methods.
Key Phenomena: CAT refers to sudden and severe turbulence occurring in cloudless regions, primarily associated with higher altitudes and wind shear. Factors such as the presence of a Jet Stream, terrain features, and upper atmospheric wind shear responding to climate change contribute to the manifestation of CAT.
Impact of CAT: CAT can lead to structural damage and even aircraft breakup, posing risks to passengers and crew. The turbulence also affects flight crew performance, making routine tasks challenging. Beyond commercial aviation, CAT holds significant implications for national security, potentially disrupting military aircraft operations and strategic mobility, emphasizing the need for comprehensive research.
Climate Change Influence: Climate change exacerbates CAT, impacting upper atmospheric wind shear. However, predicting CAT in advance remains challenging, partly due to the limitations of current Numerical Weather Prediction (NWP) models, which employ grid sizes insufficient for capturing turbulent eddies affecting aircraft.
Proposed Solution: Our research proposes an innovative approach to enhance CAT detection and forecasting, with a focus on large-scale patterns and real-time atmospheric conditions. Leveraging reanalysis data and high-resolution geostationary satellite remote sensing data, we aim to provide more accurate predictions. Machine learning and artificial intelligence models will be integrated to improve the accuracy and timeliness of forecasts.
Challenges in Satellite Remote Sensing for CAT Detection:
- Explore obstacles and solutions in using high-resolution satellite remote sensing for CAT detection.
Enhancing CAT Detection with Machine Learning:
- Evaluate how machine learning and artificial intelligence models can improve CAT detection using satellite remote sensing data.
Grid Telescopic Models for CAT Prediction:
- Assess the effectiveness of next-generation grid telescopic models in predicting CAT-prone areas.