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Enhancing production of synthetic radar images from geostationary satellite observations through generative diffusion models This research leverages diffusion models to generate radar reflectivity images from geostationary satellite observations. This study introduces an innovative machine learning approach to transform widely accessible satellite infrared images and lightning observations into synthetic radar reflectivity images.
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This study introduces an innovative machine learning approach to transform widely accessible satellite infrared images and lightning observations into synthetic radar reflectivity images to overcome this. Our research aims to integrate multiband observations from the himawari geostationary satellite and extensive lightning observations to develop a novel machine learning model for generating synthetic.
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