360 Automated Detection of the Above-Anvil Cirrus Plume Severe Storm Signature with Deep Learning

Monday, 13 January 2020
Hall B (Boston Convention and Exhibition Center)
Charles Liles, NASA, Hampton, VA; and K. M. Bedka, T. D. Smith, Y. X. Huang, R. Biswas, E. Xia, C. Dolan, and A. Hosseini Jafari
Manuscript (1.6 MB)

Handout (1.9 MB)

The above anvil cirrus plume (AACP) is a weather phenomenon that signifies an intense tropopause-penetrating updraft which can inject cirrus clouds several kilometers into the stratosphere. Storms that have such intense updrafts are often supercells which generate severe weather such as tornadoes, high winds, and hail. In addition, AACPs moisten the stratosphere and influence the Earth’s radiative balance. Though an AACP can be identified by the human eye, no automated AACP detection methods currently exist. Lack of detection inhibits understanding of where and how often AACPs occur, and how these storms influence stratospheric air composition. Previous work involved synthesis of multiple remote sensing and severe weather report/warning data sources to identify AACPs in Geostationary Operational Environmental Satellite system (GOES) satellite imagery and better understand their weather impacts (Bedka et al. (Wea. Forecasting, 2018)). This current study demonstrates an automated AACP identification method based on the application of a deep learning segmentation model known as a U-net. This study documents the development of a U-net model capable of identifying emergent AACPs using only satellite infrared (IR) and visible reflected sunlight imagery. The performance of a U-net is quantitatively benchmarked with human AACP identifications and qualitatively assessed through animations of detections generated from GOES-16 1-minute temporal resolution imagery.
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