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AN END-TO-END SEVERE THUNDERSTORM FORECASTING SYSTEM IN AUSTRALIA: OVERVIEW AND TRAINING ISSUES. Tony Bannister and Roger Deslandes Bureau of Meteorology Melbourne, Australia
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Background System Overview Training issues Conclusion
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Background July 2003 the Australian Government committed AUS$62.2 million dollars over a five year period to upgrade the Australian Bureau of Meteorology’s weather service provision. The bulk of this funding is committed to acquiring and implementing radar hardware. Also directed at the development and operational implementation of radar data display software tools, algorithm-based warning decision tools and a graphically based thunderstorm warning production system.
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Background
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A National Thunderstorm Forecast Guidance System (NTFGS), used to display Numerical Weather Prediction (NWP) data, was also deployed for operational use around Australia in the latter part of 2003. BMRC developed software used to display radar data (3D-Rapic) developing the facility to show algorithm- based overlays BMRC developed warning production tool (Thunderstorm Interactive Forecasting System; TIFS) had been tested successfully in Sydney. These constitute a tool set that can be used end-to-end in the process of diagnosing potential severe weather environments, assessing storm severity on radar and the issuing of warnings.
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Background Before this injection of resources, the Bureau of Meteorology Research Centre was developing a tool set that can be used end-to-end in the process of diagnosing potential severe weather environments, assessing storm severity on radar and the issuing of warnings The Australian Government funding provided resources and a focus to make the system operational
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System Overview Outlook period to 48 hours National Thunderstorm Forecast Guidance System (NTFGS). Nowcasting Visualization - 3D-Rapic radar-based algorithms TITAN and WDSS Forecast Production Thunderstorm Interactive Forecasting System (TIFS)
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System Overview Outlook period to 48 hours - National Thunderstorm Forecast Guidance System. displays those output fields from the 0.125º Australian operational NWP model (Meso-LAPS) that are relevant in diagnosing (severe) thunderstorm potential. Algorithms used to determine threat areas are ingredient based Forecasters view the data in a way that reinforces the (severe) thunderstorm forecast process
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Supercell (warm-season) Conditions for surface- based convection met and: Favourable: Lifted Index (500 hPa) -4.0 Deep Shear (surface to 2.5-4km) 30 knots Very favourable: Lifted Index (500 hPa) -5.0 Deep Shear (surface to 2.5-4km) 35 knots.
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Nowcasting Visualization - 3D- Rapic
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Nowcasting, radar-based algorithms Thunderstorm Identification, Tracking Analysis and Nowcasting System - TITAN (from NCAR) radar-based application that identifies and tracks storm cells and provides short-term forecasts of their movement and size Warning Decision Support System - WDSS (from NSSL) radar-based severe weather detection and prediction algorithms
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TITANWDSS
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Forecast Production - (TIFS)
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Forecaster graphically selects and edits guidance to create a warning TIFS then saves these forecast decisions and automatically generates a range of graphical and text warning products, guaranteeing consistency Warning preparation time has been significantly reduced while the range of warning products has been increased.
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Training issues Based on severe thunderstorm and radar competencies. Train-the-trainer model used. Regional trainers developed the training case studies web-based delivery of the training material Case studies utilise the actual forecast systems After the training forecasters are cross- checked against core competencies to ensure that they are familiar with systems and procedures.
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Training issues Based on severe thunderstorm and radar competencies. Train-the-trainer model used. Regional trainers stronger understanding of local office culture and local forecast procedures available to office staff after the official training has concluded.
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Training issues Regional trainers themselves developed the training case studies ensured a strong sense of ownership and understanding of the training material. The web-based delivery of the training material forecasters can easily access the resources Case studies utilise the actual forecast systems After the training forecasters are cross-checked against core competencies to ensure that they are familiar with systems and procedures.
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Training issues
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Conclusion The Australian Bureau of Meteorology is well- positioned to improve the scope and effectiveness of its Severe Thunderstorm Warning service over the next few years.
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