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Thesis Defense - Abernethy

ECOT 831

A Domain Analysis Approach to Clear Air Turbulence Forecasting Using High-Density In-Situ Measurements
Jennifer A. Abernethy
Computer Science PhD Candidate

Pilots' ability to avoid clear-air turbulence (CAT) during flight affects the safety of the millions of people who fly commercial airlines and other aircraft, and turbulence costs millions in injuries and aircraft maintenance every year. Forecasting CAT is not straightforward, however; microscale features like the turbulence eddies that affect aircraft (~100m) are below the current resolution of operational numerical weather prediction (NWP) models, and the only evidence of CAT episodes, until recently, has been sparse, subjective reports from pilots known as PIREPs. To forecast CAT, researchers use a simple weighted sum of top-performing turbulence indicators derived from NWP model outputs -- termed diagnostics -- based on their agreement with current PIREPs. However, a new, quantitative source of observation data -- high-density measurements made by sensor equipment and software on aircraft, called in-situ measurements -- is now available.

The main goal of this thesis is to develop new data analysis and processing techniques to apply to the model and new observation data, in order to improve CAT forecasting accuracy. This thesis shows that using in-situ data improves forecasting accuracy and that automated machine learning algorithms such as support vector machines (SVM), logistic regression, and random forests, can match current performance while eliminating almost all hand-tuning. Feature subset selection is paired with the new algorithms to choose diagnostics that predict well as a group rather than individually. Specializing forecasts and choice of diagnostics by geographic region further improves accuracy because of the geographic variation in turbulence sources. This work uses random forests to find climatologically-relevant regions based on these variations and implements a forecasting system testbed which brings these techniques together to rapidly prototype new, regionalized versions of operational CAT forecasting systems.

Committee: Elizabeth Bradley, Professor (Chair)
Bob Sharman, National Center for Atmospheric Research
Gregory Grudic, Assistant Professor
Rod Frehlich, Cooperative Institute for Research in Environmental Sciences (CIRES)
Kenneth Anderson, Associate Professor

See also:
Department of Computer Science
College of Engineering and Applied Science
University of Colorado Boulder
Boulder, CO 80309-0430 USA
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