On June 25, the National Oceanic and Atmospheric Administration (NOAA) launched GOES-U, the fourth and final satellite of the Geostationary Operational Environmental Satellites (GOES)-R program. This launch continues and extends the program’s mission to serve as the Western Hemisphere’s most advanced system for observing weather and monitoring the environment, as endorsed by the World Meteorological Organization.

Since the first GOES-R satellite launch in November 2016, these satellites have equipped NOAA with sophisticated imagery and atmospheric measurements, real-time lightning activity mapping, space weather observations, and other critical data collected by an array of sensors and imagers.

GOES-R satellites provide essential data for detecting and monitoring environmental conditions that pose potential threats to the Western Hemisphere. This capability is crucial given the increase in severe weather events in recent years. GOES data helps meteorologists predict and track hurricanes, tornadoes, and other significant weather events. Additionally, it aids in identifying lightning strikes likely to ignite wildfires, determining when and where air quality alerts are necessary, planning air routes to reduce flight delays, and performing other vital functions that impact daily life.

A notable aspect of the GOES-R program is its innovative use of artificial intelligence (AI) and machine learning to extend satellite mission life. The program employs the Advanced Intelligent Monitoring System (AIMS), which enhances operational efficiency and mission resilience by leveraging AI capabilities.

The vast amounts of data collected daily by GOES satellites are beyond human capacity to analyze in a timely manner. AIMS addresses this challenge by rapidly analyzing data and providing actionable information to the teams responsible for satellite operations, health, and safety. It detects data pattern changes and predicts failures based on these patterns, ensuring proactive maintenance.

Deployed on GOES-R, AIMS improves satellite uptime by quickly detecting and predicting anomalies in satellite systems, allowing maintenance teams to address issues before they impact system availability. AIMS can identify anomalies in satellite activity and perform root cause analyses and repairs in minutes or hours, whereas it previously took engineers days. By identifying potential issues early, AIMS minimizes the downtime required for satellite repairs.

Currently, AIMS tracks about 1,800 satellite telemetry points for each satellite, detecting issues such as unexpected heat sensor shutdowns or irregular engine firing rates. It has proven effective in monitoring the Advanced Baseline Imager, the primary Earth imaging instrument on the GOES-R satellites, by identifying data anomalies due to radiation or space weather and taking corrective actions.

NOAA is also testing AIMS for pattern recognition with other data types, such as engineering telemetry. Given its machine learning algorithms’ plug-and-play nature, AIMS could eventually be applied to a wide range of applications beyond its original deployment.

At a time when AI technology is rapidly advancing, its integration into the GOES-R program highlights how new technology can significantly benefit humanity by protecting lives and improving our understanding of environmental conditions in the Western Hemisphere.

By Impact Lab