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Proxy Datasets in Support of GOES-R Algorithm Development

This study highlights the importance of proxy datasets for Algorithm Working Group (AWG) efforts in improving next-generation GOES-R imagery analysis. By utilizing model-simulated GOES-R Advanced Baseline Imager (ABI) data, the project aims to support algorithm development for critical phenomena such as hurricanes, lake effect snow, and severe weather events. The initiative also focuses on realistic simulations to address science challenges and will leverage user feedback for product improvements, ensuring effective transitions to enhanced AWG products.

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Proxy Datasets in Support of GOES-R Algorithm Development

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  1. Authors: Don Hillger, Louie Grasso, Renate Brummer, Robert DeMaria Requirement:Proxy Datasets for AWG use Science:Model-simulated GOES-R ABI imagery Benefit:Prepare for next generation GOES-R Proxy Datasets in Support of GOES-R Algorithm Development Hurricanes   Lake Effect Snow Fires   Severe Weather Science Challenges: Realistic simulations Next Steps:Feedback from users Transition Path: Improvements to AWG products

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