Optimization of TZVOLCANO to Stream Real-Time GNSS Data into Cloud Hosted Real-Time Data for the Geosciences (CHORDS)

Short term processes that may be occuring during immature continental rifting have yet to be captured due to a lack of low latency observations in an opportune setting. We have devised an experiment, the Tanzania Volcano Observatory (TZVOLCANO), that has the potential to discover continental rifting processes that occur on 1 second time-scales using real-time GNSS and seismic data. In this work, we present progress towards seamlessly accessing the real-time data streams. The NSF EarthCube webservice Cloud Hosted Real-Time Data Services for the Geosciences (CHORDS) offers a portal to upload and download low latency streaming data through structured urls. For TZVOLCANO, which monitors the volcano Ol Doinyo Lengai, we develop a simple Python program to access GNSS streaming data from the NSF Geodesy Facility UNAVCO (www.unavco.org) and build the CHORDS urls to upload and make accessible our centimeter precision positioning data (latitude, longitude, and height). Challenges have occurred due to differences in GNSS streaming speeds and our program's url creation in that data can flow in faster than it is parsed and uploaded. This causes a rise in backlog pressure in the streaming data and forces a stream restart. This pressure will increase when we begin to use measurements collected in the frequency domain (collections < 1 sec). We investigate two primary methods to overcome this obstacle and to optimize the amount of data streamed. The first is partitioning the actions of our programing into two parts, creating two distinct threads to handle one input stream. This approach would increase the url creation and data upload speed to exceed the backlog pressure. The second method is to create a queue for data streamed in and allow the script to upload data from this queue. This second method moves the streaming data to a temporary location allowing the relieve of backlog pressure without the loss of data. This project investigates how these methods can be implemented to used as a workaround for high-rate streaming backlog pressure.