Comparing GEM Regional, GEM-LAM 2.5 and RUC Model Simulations of Mesoscale Features over Southern Ontario

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 ECs 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
• LAMs higher resolution should provide more
  accurate solutions in regions of complex
  topography
• RUCs 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)

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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
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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

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Results By Feature Type
(N32)

• 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

(N21)
(N23)
(N25)
(N105)
(N19)
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Results By Convection

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

Summer All Events Summer -Convection Summer No Convection
LAM 1.71 1.70 1.79
RUC 1.84 1.86 1.82
REG 2.23 2.20 2.32
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Case Study Low on 6 Apr 09
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Case Study Low on 6 Apr 09
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Case Study Low on 6 Apr 09
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Late Season Lake Breezes - 15 Oct 09
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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
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Winter Land Breezes - 16 Jan 09
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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

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Conclusions Contd

• 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

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Conclusions Contd

• 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?

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Thank you!