Comparing GEM 15 km, GEM-LAM 2.5 km and RUC 13 km Model Simulations of Mesoscale Features over Southern Ontario 2010 Great Lakes Op Met Workshop Toronto, ON David Sills, Norbert Driedger and Emma Hung Cloud Physics and Severe Weather Research Section, Environment Canada, Toronto, Canada

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 variable horizontal grid spacing (15 km in central domain), 12-hr data assimilation cycle LAM - limited-area version of the GEM model with 2.5 km horizontal grid spacing, initial and boundary conditions from GEM-REG RUC - the US NOAA/NCEP Rapid Update Cycle (RUC) model with 13 km horizontal grid spacing, 1-hr data assimilation cycle

More Model Details… REG 58 sigma vertical levels hydrostatic ISBA surface physics – 4 surfaces, 3 layers, 24 veg types LAM 58 hybrid sigma-pressure vertical levels, non-hydrostatic (better for mesoscale circulations) ISBA surface physics – 4 surfaces, 3 layers, 24 veg types RUC 50 hybrid isentropic-sigma vertical levels hydrostatic Land Surface Model – 6-layer soil model, 24 veg types no embedded water surface models assimilates radar and profiler winds, aircraft data

Hypotheses LAM’s higher resolution and non-hydrostatic formulation should provide more accurate solutions than REG in regions of complex topography RUC’s 1-hr data assimilation cycle should effectively ‘nudge’ the model solution closer to reality than REG

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 (1 st, 2 nd, 3 rd ) 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)

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% 1s2s3s1s + 2s LAM RUC REG Results - Overall

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 Month

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

Results – By Convection Is there a difference for summer convective environments? Very little change for convection vs. no convection Model rankings consistent as well May be due to 18Z being mainly pre-storm Summer – All Events Summer - Convection Summer – No Convection LAM RUC REG

Case Study – Low on 6 Apr 09

Late Season Lake Breezes - 15 Oct 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 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

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? Conclusions Cont’d

A Complementary Study…

Questions?