Journal cover Journal topic
Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 3.400 IF 3.400
  • IF 5-year value: 3.841 IF 5-year
  • CiteScore value: 3.71 CiteScore
  • SNIP value: 1.472 SNIP 1.472
  • IPP value: 3.57 IPP 3.57
  • SJR value: 1.770 SJR 1.770
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 70 Scimago H
    index 70
  • h5-index value: 49 h5-index 49
Discussion papers
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 01 Jul 2019

Submitted as: research article | 01 Jul 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Towards Objective Identification and Tracking of Convective Outflow Boundaries in Next-Generation Geostationary Satellite Imagery

Jason M. Apke1, Kyle A. Hilburn1, Steven D. Miller1, and David A. Peterson2 Jason M. Apke et al.
  • 1Cooperative Institute for Research in the Atmosphere (CIRA), Colorado State University, Fort Collins, CO, USA
  • 2Naval Research Laboratory, Monterey CA, USA

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 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 (GOES)-16 imagery via optical flow. OFBs are identified here by isolating linear features in satellite imagery, and back-tracking 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. 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 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.

Jason M. Apke et al.
Interactive discussion
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Jason M. Apke et al.
Jason M. Apke et al.
Total article views: 644 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
578 62 4 644 13 5 6
  • HTML: 578
  • PDF: 62
  • XML: 4
  • Total: 644
  • Supplement: 13
  • BibTeX: 5
  • EndNote: 6
Views and downloads (calculated since 01 Jul 2019)
Cumulative views and downloads (calculated since 01 Jul 2019)
Viewed (geographical distribution)  
Total article views: 582 (including HTML, PDF, and XML) Thereof 571 with geography defined and 11 with unknown origin.
Country # Views %
  • 1
No saved metrics found.
No discussed metrics found.
Latest update: 23 Jan 2020
Publications Copernicus
Short summary
Objective identification of deep convection outflow boundaries (OFBs) in next-generation geostationary satellite imagery is explored here using motion derived from a tuned advanced optical flow algorithm. Motion discontinuity preservation within the derivation is found crucial for successful OFB tracking between images, which yields new meteorological data for objective systems to use. These results provide the first step towards a fully automated satellite based OFB identification algorithm.
Objective identification of deep convection outflow boundaries (OFBs) in next-generation...