Latest Developments on JPL's GUARDIAN System: Stabilisation of the Near Real-Time GNSS-Based TEC Measurement Stream
Abstract
Natural hazards (earthquakes, tsunamis, volcanic eruptions, etc.) have devastating human and economic consequences. Early detection and characterization of such threats lead to timely evacuations, which are critical for significantly reducing casualties and economic cost. However, traditional warning systems (e.g., seismometers or ocean buoys) are challenging to deploy and maintain in remote areas and in the open ocean, leading to limited coverage. By monitoring the effects of natural hazards in the ionosphere, Global Navigation Satellite Systems (GNSS) can be a valuable and inexpensive augmentation to existing early warning systems.
We at NASA's Jet Propulsion Laboratory are developing an operational example of such a method: the GNSS-based Upper Atmospheric Real-time Disaster Information and Alert Network (GUARDIAN). Using dual-frequency GNSS data from JPL's Global Differential GPS (GDGPS) network, its architecture computes TEC time series in near-real-time. Currently, about 78 stations around the Pacific Ring of Fire constantly monitor the four main GNSS constellations: GPS, Galileo, GLONASS, and BeiDou. The resulting data stream is output with minimum latency to a user-friendly public website, benefitting the general public and scientific community. We present the latest developments on the GUARDIAN system. We describe the data flow from station to GDGPS server, the current algorithm and implementation, as well as advantages and limitations. We provide updated validation cases, demonstrating the quality of our near-real-time stream is comparable to post-processing methods. Focusing on the Pacific Ring of Fire, we address issues of spatial coverage and the actions that will be taken to increase it. We also display example science cases through historic examples (e.g., Haida Gwaii, Tonga). Furthermore, we will present updates to the prototype inverse modelling framework, whose goal is to extract tsunami wave characteristics from the observed travelling ionospheric disturbances.- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.G35A0312M