Enhancing Stratospheric Weather Analyses and Forecasts by Deploying Sensors from a Weather Balloon

TL;DR

This paper introduces a new framework for deploying tiny sensors from weather balloons to improve stratospheric weather data collection, leveraging machine learning to optimize sensor release timing and demonstrating effectiveness through real flights and simulations.

Contribution

It presents a novel hardware system and a data collection framework that optimally releases sensors from weather balloons based on Gaussian process modeling.

Findings

Effective sensor deployment demonstrated in real flights.

Framework improves data collection for stratospheric weather analysis.

Machine learning enhances deployment timing accuracy.

Abstract

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.