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Olivia Martius
,
A. Hering
,
M. Kunz
,
A. Manzato
,
S. Mohr
,
L. Nisi
, and
S. Trefalt
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Kristopher M. Bedka
,
John T. Allen
,
Heinz Jurgen Punge
,
Michael Kunz
, and
Denis Simanovic

ABSTRACT

A 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.

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Keith A. Browning
,
Alan M. Blyth
,
Peter A. Clark
,
Ulrich Corsmeier
,
Cyril J. Morcrette
,
Judith L. Agnew
,
Sue P. Ballard
,
Dave Bamber
,
Christian Barthlott
,
Lindsay J. Bennett
,
Karl M. Beswick
,
Mark Bitter
,
Karen E. Bozier
,
Barbara J. Brooks
,
Chris G. Collier
,
Fay Davies
,
Bernhard Deny
,
Mark A. Dixon
,
Thomas Feuerle
,
Richard M. Forbes
,
Catherine Gaffard
,
Malcolm D. Gray
,
Rolf Hankers
,
Tim J. Hewison
,
Norbert Kalthoff
,
Samiro Khodayar
,
Martin Kohler
,
Christoph Kottmeier
,
Stephan Kraut
,
Michael Kunz
,
Darcy N. Ladd
,
Humphrey W. Lean
,
Jürgen Lenfant
,
Zhihong Li
,
John Marsham
,
James McGregor
,
Stephan D. Mobbs
,
John Nicol
,
Emily Norton
,
Douglas J. Parker
,
Felicity Perry
,
Markus Ramatschi
,
Hugo M. A. Ricketts
,
Nigel M. Roberts
,
Andrew Russell
,
Helmut Schulz
,
Elizabeth C. Slack
,
Geraint Vaughan
,
Joe Waight
,
David P. Wareing
,
Robert J. Watson
,
Ann R. Webb
, and
Andreas Wieser

The Convective Storm Initiation Project (CSIP) is an international project to understand precisely where, when, and how convective clouds form and develop into showers in the mainly maritime environment of southern England. A major aim of CSIP is to compare the results of the very high resolution Met Office weather forecasting model with detailed observations of the early stages of convective clouds and to use the newly gained understanding to improve the predictions of the model.

A large array of ground-based instruments plus two instrumented aircraft, from the U.K. National Centre for Atmospheric Science (NCAS) and the German Institute for Meteorology and Climate Research (IMK), Karlsruhe, were deployed in southern England, over an area centered on the meteorological radars at Chilbolton, during the summers of 2004 and 2005. In addition to a variety of ground-based remote-sensing instruments, numerous rawinsondes were released at one- to two-hourly intervals from six closely spaced sites. The Met Office weather radar network and Meteosat satellite imagery were used to provide context for the observations made by the instruments deployed during CSIP.

This article presents an overview of the CSIP field campaign and examples from CSIP of the types of convective initiation phenomena that are typical in the United Kingdom. It shows the way in which certain kinds of observational data are able to reveal these phenomena and gives an explanation of how the analyses of data from the field campaign will be used in the development of an improved very high resolution NWP model for operational use.

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