Algorithmic decision-making under weather uncertainty in atmospheric science field campaigns: a summary

Scientists conducting field campaigns in the atmospheric sciences are expected to deploy scarce resources to optimize the amount of data collected. Because resource deployment decisions are typically made under weather uncertainty, decision-makers rely on weather forecasts. Using automated, calibrated, probabilistic weather forecasts, automated decision-making algorithms have shown the ability to outperform the traditional heuristic method of forecasting and decision-making in three atmospheric science field campaigns: the Routine Atmospheric Radiation Measurement (ARM) Aerial Facility (AAF) Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) campaign in 2009, the Small Particles in Cirrus (SPartICus) campaign in 2010, and the Deep Convective Clouds and Chemistry (DC3) campaign in 2012. Each field campaign sought different atmospheric phenomena, requiring a different forecasting system for each decision-making algorithm. In each case, retrospective analyses showed that using the automated decision-making algorithm would have increased data yield while reducing the amount of human effort expended on daily forecasting and decision-making. We propose that the use of algorithmic decision-making under weather uncertainty offers added value to human forecasts, statistical forecasts, and numerical weather prediction (NWP) forecasts and a means of integrating human forecasting knowledge with advances in computing.