Enhancing Stratospheric Weather Analyses and Forecasts by Deploying Sensors from a Weather Balloon
This addresses data scarcity for stratospheric weather analysis, which is crucial for climate change studies, but is incremental as it builds on existing weather balloon technology.
The paper tackles the problem of limited stratospheric data collection by proposing a framework to deploy sensors from a weather balloon, using Gaussian process modeling to optimize release timing and a novel hardware system, and demonstrates effectiveness through real flights and simulations.
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.