2.4 Assessment of the User Impacts to the GOES-17 ABI Anomaly

Monday, 7 January 2019: 11:15 AM
North 231AB (Phoenix Convention Center - West and North Buildings)
Daniel T. Lindsey, NOAA/NESDIS, Fort Collins, CO; and K. Schrab, J. M. Daniels, and T. J. Schmit

NOAA launched GOES-17 into geostationary orbit on March 1, 2018, the second satellite in its GOES-R series having state-of-the-art instrumentation, including the Advanced Baseline Imager (ABI). During the checkout phase of the ABI, it was determined that the Loop Heat Pipes were not operating properly, resulting in a degraded cooling system. During certain parts of the nighttime hours, the sun shines onto the ABI detectors, introducing a source of heat that must be offset by the instrument’s cooling system. However, given its degraded state, the temperature of the detectors rises above nominal levels, resulting in degraded and even saturated imagery in some of the infrared channels.

To address this problem, NOAA put together several teams, each focusing on different aspects of the anomaly. The Science Options Team was tasked with determining the user impacts to various ABI configurations and satellite constellation options, including the possibility of operating one of the legacy GOES satellites alongside GOES-17. By changing ABI settings, it’s possible to operate at elevated temperatures and avoid saturation, but with the tradeoff of increased noise in the data. Noise degrades the data, and in turn the satellite imagery and downstream products are partially or completely compromised. Through a series of tests, the team quantified the impacts to the products relied upon by the National Weather Service and other users, and utilized this information to recommend an optimal ABI operating configuration. This presentation will provide details of the testing that was performed, show the impacts of the degraded data, and provide yearly and hourly predictions of the imagery and product quality given the chosen constellation and ABI configuration. Efforts to modify downstream algorithms to better perform with noisy data in certain spectral bands will also be detailed.

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