C.J. Hanlon, A. Small, S. Bose, G.S. Young, J. Verlinde, 2014
Automated decision algorithm applied to a field campaign with multiple research objectives: the DC3 campaign
Journal of Geophysical Research, 119, 11,527-11,542
Abstract
Automated decision systems have shown the potential
to increase data yields from field experiments in atmospheric science. The
present paper describes the construction and performance of a flight decision
system designed for a case in which investigators pursued multiple, potentially
competing objectives. The Deep Convective Clouds and Chemistry (DC3) campaign
in 2012 sought in situ airborne measurements of isolated deep convection in
three study regions: northeast Colorado, north Alabama, and a larger region
extending from central Oklahoma through northwest Texas. As they confronted
daily flight launch decisions, campaign investigators sought to achieve two
mission objectives that stood in potential tension to each other: to maximize
the total amount of data collected while also collecting approximately equal
amounts of data from each of the three study regions. Creating an automated
decision system involved understanding how investigators would themselves
negotiate the trade-offs between these potentially competing goals, and
representing those preferences formally using a utility function that served to
rank-order the perceived value of alternative data portfolios. The decision
system incorporated a custom-built method for generating probabilistic forecasts
of isolated deep convection and estimated climatologies calibrated to
historical observations. Monte Carlo simulations of alternative future
conditions were used to generate flight decision recommendations dynamically
consistent with the expected future progress of the campaign. Results show that
a strict adherence to the recommendations generated by the automated system
would have boosted the data yield of the campaign by between 10 and 57%,
depending on the metrics used to score success, while improving portfolio
balance.