  The ability to analyze and forecast stratospheric weather conditions is
fundamental to addressing climate change. However, our capacity to collect data
in the stratosphere is limited by sparsely deployed weather balloons. We
propose a framework to collect stratospheric data by releasing a contrail of
tiny sensor devices as a weather balloon ascends. The key machine learning
challenges are determining when and how to deploy a finite collection of
sensors to produce a useful data set. We decide when to release sensors by
modeling the deviation of a forecast from actual stratospheric conditions as a
Gaussian process. We then implement a novel hardware system that is capable of
optimally releasing sensors from a rising weather balloon. We show that this
data engineering framework is effective through real weather balloon flights,
as well as simulations.
