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Severe Weather Forecasting Tools in the Ninjo Workstation

Severe Weather Forecasting Tools in the Ninjo Workstation Paul Joe 1 , Hans-Joachim Koppert 2 , Dirk Heizenreder 2 Bernd Erbshaeusser 2 , Wolfgang. Raatz 2 , Bernhard Reichert 2 and Michael Rohn 2 1 Meteorological Service of Canada 2 Deutscher Wetterdienst. Outline. Brief NinJo overview

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Severe Weather Forecasting Tools in the Ninjo Workstation

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  1. Severe Weather Forecasting Tools in the Ninjo Workstation Paul Joe1, Hans-Joachim Koppert2, Dirk Heizenreder2Bernd Erbshaeusser2, Wolfgang. Raatz2, Bernhard Reichert2 and Michael Rohn2 1Meteorological Service of Canada 2Deutscher Wetterdienst

  2. Outline • Brief NinJo overview • Various Tools Specfic to Weather Warnings • Radar – Doppler, 3D • Algorithms (Konrad, CARDS) • Storm Classification Identification and Tracking • Interactive Cross-sections • Interactive Cell Views • Automon • EPM – Editing, Production, Monitoring of Warnings • OOG – Objectively Optimized Guidance • MMO – Modified Model Output • Current status

  3. What is NinJo? • Consortium of five partners • Data Visualization Workstation • 1.0 release date 27.03.2005 • 1.0 deployed by DWD, DMI, MCH, BGS • MSC responsible for radar, lightning • DWD responsible for SCIT

  4. NinJo Layers = Client Interface to Server Applications

  5. Severe Weather Warnings for DWD DWD announced that it will to take the responsibility to provide a severe thunderstorm/tornado warning service!

  6. Radar in NinJo Initially based on the Canadian radar system in Canada due to requirements for severe weather. Integrates data/products DWD legacy systems [KONRAD Hohenpeissenberg / RDT Offenbach AP2003 group], from MCH (TRT)

  7. Some Philosophy • Assume expert severe wx user • Must maintain situational awareness • Work from composite and drill down to details • Support Analysis/Mental Models • Detail view match “text book” material • Create Leverage Points/not answers • Use algorithms as guidance and not to promote dumbness • Do not rely on algorithms, use them wisely

  8. Implementation – SCIT and Cells Storm Classification Identification and Tracking - “ranked storms” Composite radar; Imagery hidden Cell detections shown

  9. Cell View

  10. Radar Data/Products and NinJo • Can manipulate, view, interact with radar in the same way as any other data – separation by usage rather than by sensor • Radar/data functionality separated • VAD = aerological data • Cell Objects = point data • Radar Fields = 2D or 3D data • Cross-section capability = path layer

  11. NinJo and Radar A NinJo View: Transparent radar and satellite overaid on surface data Composites on the fly or pre-computed, Data viewer

  12. Interactive “Cell” Views(first prototype step) • User sets the area for viewing. • Not necessarily based on algorithm detection of thunderstorms (and their problems)! • Any data type can be included in the cell view window. • eg boundary identification, fog monitoring

  13. Assessing Models with Data Watch / Assessment Phase of the Severe weather forecast process

  14. Cross-sections

  15. MxRadar XSection

  16. AutoMon (Automatic Monitoring)(i) point threshold for alerting, (ii) model vs data, (iii) forecast vs data Objects: Will handle objects generated by lightning cluster algorithms, satellite, manually using the same

  17. Assessing ModelsWatch Phase Real vs Synthetic (NWP)

  18. Assessing Models by Comparing with Data

  19. Editting, Production and Monitoring(Warning Layer)

  20. Objectively Optimized Guidance(Blending)

  21. Summary • Very brief description of the NinJo and the Severe Weather package • Several tools to support the forecaster to make better decisions • Radar, algorithms, ranking, cell views, cross-sections, Automon, MMO (EPM (warning production), lightning, swath products • System in development, v1 released, v1.2 March 2006 • Need to spend time on training to develop expertise • Science, technology, usage • Practice/Simulation needed to optimize use

  22. Lightning / Lightning Cluster Analysis

  23. Modified Model Output(Point and Area editting)

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