1 CRGAQS: Meteorological Modeling prepared for Southwest Clean Air Agency 19 June 2006 prepared by Alpine Geophysics, LLC ENVIRON International Corp.

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Presentation transcript:

1 CRGAQS: Meteorological Modeling prepared for Southwest Clean Air Agency 19 June 2006 prepared by Alpine Geophysics, LLC ENVIRON International Corp

2 MM5 Simulations Model OptionMM5 Simulation Run1Run2Run3 (UW Forecast) Run 4 (PortlandSIP) Run 5 (2 +surf) Run 6* (2+surf+BM) Land Surface ModelPleim-Xiu 5-Layer SoilNOAHPleim-Xiu Planetary Boundary Layer ACM MRF ACM RadiationRRTM CCM2RRTM Shallow ConvectionNone Cumulus Parameterization KF 2 (36/12)KF 2 (36/12) KF (36/12) KF (36/12) KF 2 (36/12) BM (36/12) Moist PhysicsReisner I Reisner IIGSFC GraupelReisner I Analysis Nudging Surface None U/V Analysis Nudging Aloft U/V/T/Q Surface Obs Nudging NoneU/V (No Gorge) None U/V (w/Gorge) U/V (w/ Gorge) * Run6 similar to WRAP configuration

3 Analysis Regions WG = Western Gorge CG = Central Gorge EG = Eastern Gorge PS = Puget Sound NC = North Coast WV = William EC = East of Cascades

4 August km Humidity Bias/Error

5 August km Humidity Mean

6 August km Temperature Bias/Error

7 Aug km Temperature Mean

8 Aug km Wind RMSE/Error

9 Aug km Wind Mean Speed

10 Aug km Humidity Bias/Error

11 Aug km Humidity Mean

12 Aug km Temperature Bias/Error

13 Aug km Temperature Mean

14 Aug km Wind RMSE/Error

15 Aug km Wind Speed Mean

16 Nov km Humidity Bias/Error

17 Nov km Humidity Mean

18 Nov km Temperature Bias/Error

19 Nov km Temperature Mean

20 Nov km Wind RMSE/Error

21 Nov km Wind Mean Speed

22 Nov km Humidity Bias/Error

23 Nov km Humidity Mean

24 Nov km Temperature Bias/Error

25 Nov km Temperature Mean

26 Nov km Wind RMSE/Error

27 Nov km Wind Mean Speed

28 MM5 Configuration Selection Based on Overall Synthesis of the Candidate Simulations, Run 6 is chosen as best performing

29 Run 6 Performance Evaluation  Qualitative Precipitation Analysis  Comparison with 0.25 o (~27km) CPC Episode Total  CPC analysis does not extend over water  Gorge Mean Value Analysis  Compare Time Series of Spatial Mean Model/Obs.  Wind Vector Analysis

30 Qualitative Precipitation 4km Aug 2004

31 Qualitative Precipitation 12km Aug 2004

32 Qualitative Precipitation 4km Nov 2004

33 Qualitative Precipitation 12km Nov 2004

34 Qualitative Precipitation Summary  MM5 generally captures spatial extent and magnitude of precip.  MM5 shows smaller scale structure that can not be verified with the coarse CPC analysis  MM5 underestimates precip. in:  Southeastern Oregon in August  Oregon coast and Portland in November

35 Aug Mean Humidity 4km Gorge West Central East

36 Aug Mean Temperature 4km Gorge West Central East

37 Aug Mean Wind Speed 4km Gorge West Central East

38 Nov Mean Humidity 4km Gorge West Central East

39 Nov Mean Temperature 4km Gorge West Central East

40 Nov Mean Wind Speed 4km Gorge West East

41 Mean Value Analysis Summary  MM5 generally overestimates humidity in August and underestimates in November  Significant humidity phase difference in eastern gorge in mid-August.  MM5 tends to underestimate daytime and overestimate nighttime temperatures  Typical pattern but larger than normal  Model overestimating temperatures in central gorge early in Nov. episode.  Wind speed trends generally captured

42 Wind Vector Analysis  Hourly wind vectors have been prepared  Brief subset for presentation  Black vectors MM5 winds  Red vectors Obs. winds

43 Aug km Wind Vector

44 Aug km Wind Vector

45 Nov km Wind Vector

46 Nov km Wind Vector

47 Nov km Wind Vector

48 Nov km Wind Vector

49 Nov km Wind Vector

50 Nov km Wind Vector

51 Nov km Wind Vector

52 Wind Vector Analysis Summary  MM5 generally captures up-gorge flow in Aug. and down-gorge flow in Nov.  Flows follow gorge quite closely  Gorge monitors show more variation between nearby monitors than MM5 fields  Indication that obs. influenced by small scale features

53 Summary  Significant Effort was put into determining the best performing options in MM5  MM5 is still performing less well than performance benchmarks based on historic MM5 applications.  Gorge more complex than other areas. Monitors more likely to be influenced by small scale flows  Gorge analysis regions have fewer sites than were used for benchmarks  Nov. case has weak synoptic forcing. MM5 traditionally performs better under stronger forced conditions  MM5 Captures August Up-gorge flows  MM5 Captures November Down-gorge flows

54 Next Steps  Prepare emissions using WRAP 2002 inventory projected to 2004 replaced by ODEQ/SWCAA data for selecteed WA and OR Counties  36 and 12 km WRAP 2002 projected to 2004  4 km add OR and WA inventories Some issues/inconsistencies with OR/WA data  CMAQ and CAMx base case modeling and model performance evaluation  Evaluate need/usefulness of 1.33 km MM5 modeling