Tobin, Jackson C., author2025-11-172025-11-172025-05https://hdl.handle.net/10217/242333Cooperative Institute for Research in the Atmosphere.Severe weather intensity trends can be monitored from satellite imagery over regions with sparse radar coverage using novel products available from optical flow retrievals. For example, cloud-top divergence rendered from the retrieved brightness motions, provides an indirect measure of updraft intensity with time. Recent demonstrations have now shown that dense optical flow can render sub-storm scale (< 5 km) motions, which appear noisy to operational forecasters during warning operations evaluations. The bilateral filter, a spatial signal smoothing filter that retains large-scale features while attenuating signal noise, is an approach for removing any unwanted cloud-top divergence noise signals while preserving the large-scale signals forecasters use in practice. Little is currently understood, however, on how such filters modify observed trends and magnitudes in cloud-top divergence, in particular how such magnitudes change during and in advance of severe weather observations at the ground. Two bilateral filter sizes were evaluated on a mid-latitude supercell in the Great Plains and tropical convection off the Northeast coast of South America, and it was determined that a bilateral filter with a gaussian sigma size of 2.5 adequately removed unwanted signals for both cases while maintaining the 5 min severe weather lead time associated with cloud-top divergence. Additionally, filtered signals were reduced by ~38% to ~60% for sigma sizes of 2.5 and 5.0 respectively.born digitalStudent worksengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.remote sensingoptical flowsevere weatherNowcastcloud-top cooling and divergenceimage processingText