Abstract

The ability to remotely sense the atmosphere using observations from Global Navigation Satellite Systems (GNSS) can be included in the long list of unexpected applications developed by the broader GNSS scientific and engineering community. GNSS meteorology has evolved from its initial motivation ... Show moreThe ability to remotely sense the atmosphere using observations from Global Navigation Satellite Systems (GNSS) can be included in the long list of unexpected applications developed by the broader GNSS scientific and engineering community. GNSS meteorology has evolved from its initial motivation to improve station coordinate repeatability, progressing into a novel way to observe the atmosphere, and now becoming an important observational system used to improve weather forecasts and monitor the changing climate. Ground-based GNSS meteorology stations are used primarily to retrieve integrated quantities of water vapor. These estimates fulfill a unique niche in the established suite of atmospheric instrumentation. They are almost entirely insensitive to liquid water (i.e. clouds and rain), making this one of the only techniques to measure water vapor that is useful in all weather conditions. The latent heat released when water vapor condenses in the atmosphere to form liquid or ice fuels the severe storms that produce heavy precipitation events, including flash floods, tornadoes, and tropical cyclones. Water vapor is also the Earth’s most abundant greenhouse gas, and the primary mechanism in which the atmosphere transports energy poleward from the equator. These characteristics make it a key component of the Earth's water cycle. This presentation is meant to serve as a broad review of how networks of ground-based GNSS systems have impacted severe storm forecasting and improved the characterization of water vapor in the atmosphere. The impact of these observations will be displayed using specific examples and long-term statistical evaluations. A forward-looking perspective will also be provided, evaluating the potential of streaming data to improve nowcasts (or forecasts of less than 12 hours) and the potential of ocean-based platforms to improve intensity forecasts of tropical cyclones. Show less