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David Sills, Norbert Driedger and Emma Hung Cloud Physics and Severe Weather Research Section,

Comparing GEM Regional, GEM-LAM 2.5 and RUC Model Simulations of Mesoscale Features over Southern Ontario. David Sills, Norbert Driedger and Emma Hung Cloud Physics and Severe Weather Research Section, Environment Canada, Toronto, Canada. 2009 CMOS Congress 31 May – 4 June, Halifax, NS.

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David Sills, Norbert Driedger and Emma Hung Cloud Physics and Severe Weather Research Section,

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  1. Comparing GEM Regional, GEM-LAM 2.5 and RUC Model Simulations of Mesoscale Features over Southern Ontario David Sills, Norbert Driedger and Emma Hung Cloud Physics and Severe Weather Research Section, Environment Canada, Toronto, Canada 2009 CMOS Congress 31 May – 4 June, Halifax, NS

  2. Introduction and Motivation • Variety of NWP models used at the OSPC RSD for mesoscale analysis and nowcasting guidance: • REG - regional version of EC’s Global Environmental Multiscale (GEM) model with 15 km horizontal grid spacing, • LAM - limited-area version of the GEM model with 2.5 km horizontal grid spacing, and • RUC- the US Rapid Update Cycle (RUC) model with 13 km horizontal grid spacing • LAM’s higher resolution should provide more accurate solutions in regions of complex topography • RUC’s 1-hr data assimilation cycle should effectively ‘nudge’ the model solution closer to reality

  3. Methodology • Focused on features with mesoscale detail over southern Ontario and surrounding areas: • Early-season, late-season and summer lake breezes • Winter land breezes with snow squalls • Warm / cold fronts • Low positions • Others: prefrontal convergence, trofs, lake funnelling, etc. • used 18 UTC data from June 2008 to May 2009 • Model performance ranked (1st, 2nd, 3rd) based on subjective comparison of mesoscale features against observations (sfc winds, radar reflectivity, vis sat) • Ties were always ranked as 2 (1-2-2, 2-2-3, 2-2-2)

  4. Results - Overall • 232 mesoscale features were compared from 217 days • Overall averaged rankings: • LAM – 1.78 • RUC – 1.94 • REG – 2.19 • LAM ranked higher than REG: 115 events or 49.5% • RUC ranked higher than REG: 103 events or 44.4%

  5. Results – By Month • Model rankings have clear monthly differences • LAMmodel superior Aug-Oct and Feb-Mar, worse than REGNov and Jan • RUCmodel superior Nov-Jan and Apr-May, worse than REGOct and Mar • No month has REGthe highest ranked model

  6. Results – By Feature Type (N=32) • Model ranking also has clear differences based on feature type • LAMsuperior with early and late season lake breezes, worse than REG for winter land breezes • RUCsuperior with low positions, worse than REG for early- and late- season lake breezes • REG does well with winter land breezes (N=21) (N=23) (N=25) (N=105) (N=19)

  7. Results – By Convection • Is there a difference for summer convective environments? • Very little change for convection vs. no convection • Model rankings consistent as well

  8. Case Study – Low on 6 Apr 09

  9. Case Study – Low on 6 Apr 09

  10. Case Study – Low on 6 Apr 09

  11. Late Season Lake Breezes - 15 Oct 09

  12. Late Season Lake Breezes - 15 Oct 09

  13. Late Season Lake Breezes - 15 Oct 09

  14. Winter Land Breezes - 16 Jan 09

  15. Winter Land Breezes - 16 Jan 09

  16. Winter Land Breezes - 16 Jan 09

  17. Conclusions • Overall, the mesoscale features generated by the LAM and the RUC were closer to observations than the REG, with LAM having the highest averaged ranking • There were clear monthly differences in model rankings, as well in differences due to feature type

  18. Conclusions Cont’d • The LAM and RUC ranked about the same for summer lake breezes and warm/cold fronts • The LAM ranked first for early- and late-season lake breezes, while RUC ranked first for low positions • LAM ranked last for winter land breezes, while RUC ranked last for early- and late-season lake breezes

  19. Conclusions Cont’d • There appeared to be little difference between events with convection and events without convection • This is a preliminary investigation – a more objective approach and larger sample sizes are needed • Would a high-resolution LAM with an hourly data assimilation cycle produce even better results?

  20. Thank you!

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