Transcript PPS
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 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 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) 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% 1s 2s 3s 1s + 2s LAM 74 136 22 210 RUC 74 97 61 171 REG 11 167 54 178 Results – By Month • Model rankings have clear monthly differences • LAM model superior Aug-Oct and Feb-Mar, worse than REG Nov and Jan • RUC model superior Nov-Jan and Apr-May, worse than REG Oct and Mar • No month has REG the highest ranked model Results – By Feature Type (N=32) • Model ranking also has clear differences based on feature type (N=21) (N=23) • LAM superior with early and late season lake breezes, worse than REG for winter land breezes • RUC superior with low (N=25) (N=105) positions, worse than REG for early- and lateseason lake breezes • REG does well with (N=19) winter land breezes Results – By Convection • Is there a difference for summer convective environments? • Very little change for convection vs. no convection • Model rankings consistent as well LAM RUC REG Summer – All Events Summer Convection Summer – No Convection 1.71 1.84 2.23 1.70 1.86 2.20 1.79 1.82 2.32 Case Study – Low on 6 Apr 09 Case Study – Low on 6 Apr 09 Case Study – Low on 6 Apr 09 Late Season Lake Breezes - 15 Oct 09 Late Season Lake Breezes - 15 Oct 09 Late Season Lake Breezes - 15 Oct 09 Winter Land Breezes - 16 Jan 09 Winter Land Breezes - 16 Jan 09 Winter Land Breezes - 16 Jan 09 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 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 lateseason lake breezes, while RUC ranked first for low positions • LAM ranked last for winter land breezes, while RUC ranked last for early- and lateseason lake breezes 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? Thank you!