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.
翻译:分析和预报平流层天气条件的能力对于应对气候变化至关重要。然而,我们在平流层中收集数据的能力受到部署很少的气象气球的限制。我们提出了一个框架来收集平流层数据,在气象气球上升时释放一个小传感器装置。关键的机器学习挑战是如何确定何时和如何部署有限的传感器集,以产生有用的数据集。我们决定何时释放传感器,方法是将预报偏离实际平流层条件的模型作为高斯过程。然后我们实施一个新的硬件系统,能够从不断上升的气象气球中最佳释放传感器。我们表明,通过真正的气象气球飞行以及模拟,这一数据工程框架是有效的。