Abstract
Abstract. Sudden wind direction and speed shifts from outflow boundaries (OFBs) associated with deep convection significantly affect weather in the lower troposphere. Specific OFB impacts include rapid variation in wildfire spread rate and direction, the formation of convection, aviation hazards, and degradation of visibility and air quality due to mineral dust aerosol lofting. Despite their recognized importance to operational weather forecasters, OFB characterization (location, timing, intensity, etc.) in numerical models remains challenging. Thus, there remains a need for objective OFB identification algorithms to assist decision support services. With two operational next-generation geostationary satellites now providing coverage over North America, high-temporal- and high-spatial-resolution satellite imagery provides a unique resource for OFB identification. A system is conceptualized here designed around the new capabilities to objectively derive dense mesoscale motion flow fields in the Geostationary Operational Environmental Satellite 16 (GOES-16) imagery via optical flow. OFBs are identified here by isolating linear features in satellite imagery and backtracking them using optical flow to determine if they originated from a deep convection source. This “objective OFB identification” is tested with a case study of an OFB-triggered dust storm over southern Arizona. The results highlight the importance of motion discontinuity preservation, revealing that standard optical flow algorithms used with previous studies underestimate wind speeds when background pixels are included in the computation with cloud targets. The primary source of false alarms is the incorrect identification of line-like features in the initial satellite imagery. Future improvements to this process are described to ultimately provide a fully automated OFB identification algorithm.
Highlights
Downburst outflows from associated deep convection (Byers and Braham Jr., 1949; Mitchell and Hovermale, 1977) play a significant, dynamic role in modulation of the lower troposphere
PM was a popular method for atmospheric motion vector (AMV) over other optical flow approaches prior to the Geostationary Operational Environmental Satellite R (GOES-R) era due to its simplicity, computational efficiency, and capability to handle displacements common in low-temporal-resolution satellite imagery (Bresky and Daniels, 2006)
Since such low-level linear features are often obscured by cloud layers at higher altitudes, this case study in some respects represents a best-case scenario for evaluating optical flow capabilities towards identifying outflow boundaries (OFBs)
Summary
Downburst outflows from associated deep convection (Byers and Braham Jr., 1949; Mitchell and Hovermale, 1977) play a significant, dynamic role in modulation of the lower troposphere. The vast improvement of temporal resolution alone (which includes mesoscale sectors that refresh as high as 30 s) allows for dramatically improved tracking of convection (Cintineo et al, 2014; Mecikalski et al, 2016; Sieglaff et al, 2013), fires and pyroconvection (Peterson et al, 2015, 2017, 2018), ice flows, and synoptic-scale patterns (Line et al, 2016). This higher temporal resolution makes the identification of features like OFBs easier as well because of greater frame-to-frame consistency. Paper with a discussion on plans for future work in objective feature identification from next-generation geostationary imagers of similar fidelity as the GOES-R ABI, which are presently coming online around the globe
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