A Long-Term Overshooting Convective Cloud-Top Detection Database over Australia Derived from MTSAT Japanese Advanced Meteorological Imager Observations

A Long-Term Overshooting Convective Cloud-Top Detection Database over Australia Derived from... ABSTRACTA 10-yr geostationary (GEO) overshooting cloud-top (OT) detection database using Multifunction Transport Satellite (MTSAT) Japanese Advanced Meteorological Imager (JAMI) observations has been developed over the Australian region. GEO satellite imagers collect spatially and temporally detailed observations of deep convection, providing insight into the development and evolution of hazardous storms, particularly where surface observations of hazardous storms and deep convection are sparse and ground-based radar or lightning sensor networks are limited. Hazardous storms often produce one or more OTs that indicate the location of strong updrafts where weather hazards are typically concentrated, which can cause substantial impacts on the ground such as hail, damaging winds, tornadoes, and lightning and to aviation such as turbulence and in-flight icing. The 10-yr OT database produced using an automated OT detection algorithm is demonstrated for analysis of storm frequency, diurnally, spatially, and seasonally relative to known features such as the Australian monsoon, expected regions of hazardous storms along the southeastern coastal regions of southern Queensland and New South Wales, and the preferential extratropical cyclone track along the Indian Ocean and southern Australian coast. A filter based on atmospheric instability, deep-layer wind shear, and freezing level was used to identify OTs that could have produced hail. The filtered OT database is used to generate a hail frequency estimate that identifies a region extending from north of Brisbane to Sydney and the Goldfields–Esperance region of eastern Western Australia as the most hail-prone regions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Meteorology and Climatology American Meteorological Society

A Long-Term Overshooting Convective Cloud-Top Detection Database over Australia Derived from MTSAT Japanese Advanced Meteorological Imager Observations

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1558-8432
eISSN
1558-8432
D.O.I.
10.1175/JAMC-D-17-0056.1
Publisher site
See Article on Publisher Site

Abstract

ABSTRACTA 10-yr geostationary (GEO) overshooting cloud-top (OT) detection database using Multifunction Transport Satellite (MTSAT) Japanese Advanced Meteorological Imager (JAMI) observations has been developed over the Australian region. GEO satellite imagers collect spatially and temporally detailed observations of deep convection, providing insight into the development and evolution of hazardous storms, particularly where surface observations of hazardous storms and deep convection are sparse and ground-based radar or lightning sensor networks are limited. Hazardous storms often produce one or more OTs that indicate the location of strong updrafts where weather hazards are typically concentrated, which can cause substantial impacts on the ground such as hail, damaging winds, tornadoes, and lightning and to aviation such as turbulence and in-flight icing. The 10-yr OT database produced using an automated OT detection algorithm is demonstrated for analysis of storm frequency, diurnally, spatially, and seasonally relative to known features such as the Australian monsoon, expected regions of hazardous storms along the southeastern coastal regions of southern Queensland and New South Wales, and the preferential extratropical cyclone track along the Indian Ocean and southern Australian coast. A filter based on atmospheric instability, deep-layer wind shear, and freezing level was used to identify OTs that could have produced hail. The filtered OT database is used to generate a hail frequency estimate that identifies a region extending from north of Brisbane to Sydney and the Goldfields–Esperance region of eastern Western Australia as the most hail-prone regions.

Journal

Journal of Applied Meteorology and ClimatologyAmerican Meteorological Society

Published: Apr 28, 2018

References

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