Field campaigns in atmospheric science typically require making challenging decisions about how best to deploy limited resources, especially aircraft flight hours. Algorithmic decision tools have shown the potential to outperform traditional heuristic approaches to allocating limited flight hours in field campaigns. The present study examines the utility of algorithmic decision tools in an application to the Atmospheric Radiation Measurement (ARM) Small Particles in Cirrus (SPartICus) campaign, which sampled cirrus clouds over the ARM Southern Great Plains (SGP) site between January and June 2010. Probabilistic forecasts of suitable data collection conditions were generated using relative humidity forecasts from the Global Forecast System (GFS) and self-organizing maps. An optimization procedure based on dynamic programming was then used to generate day-ahead fly/no-fly decisions for research flights over the SGP site. The quality of flight decisions thus generated were compared with those made by the SPartICus science team. Results showed that the algorithmic decision tool would have delivered 11% more optimal data while shortening the length of the campaign season by 29 days and reducing the per-day expenditure of investigator time on activities of forecasting and decision-making.