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. Sudden wind direction and speed shifts from outflow boundaries (OFBs)associated with deep convection significantly affect weather in the lowertroposphere. Specific OFB impacts include rapid variation in wildfire spreadrate and direction, the formation of convection, aviation hazards, anddegradation of visibility and air quality due to mineral dust aerosollofting. Despite their recognized importance to operational weatherforecasters, OFB characterization (location, timing, intensity, etc.) innumerical models remains challenging. Thus, there remains a need forobjective OFB identification algorithms to assist decision support services.With two operational next-generation geostationary satellites now providingcoverage over North America, high-temporal- and high-spatial-resolution satelliteimagery provides a unique resource for OFB identification. A system isconceptualized here designed around the new capabilities to objectivelyderive dense mesoscale motion flow fields in the Geostationary OperationalEnvironmental Satellite 16 (GOES-16) imagery via optical flow. OFBs areidentified here by isolating linear features in satellite imagery andbacktracking them using optical flow to determine if they originated from adeep convection source. This “objective OFB identification” is tested witha case study of an OFB-triggered dust storm over southern Arizona. Theresults highlight the importance of motion discontinuity preservation,revealing that standard optical flow algorithms used with previous studiesunderestimate wind speeds when background pixels are included in thecomputation with cloud targets. The primary source of false alarms isthe incorrect identification of line-like features in the initial satelliteimagery. Future improvements to this process are described to ultimatelyprovide a fully automated OFB identification algorithm.

Towards objective identification and tracking of convective outflow boundaries in next-generation geostationary satellite imagery