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1 Prepared by Neil J.M. Wheeler and Kenneth J. Craig Sonoma Technology, Inc. Petaluma, California for the Fifth Annual Community Modeling and Analysis.

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Presentation on theme: "1 Prepared by Neil J.M. Wheeler and Kenneth J. Craig Sonoma Technology, Inc. Petaluma, California for the Fifth Annual Community Modeling and Analysis."— Presentation transcript:

1 1 Prepared by Neil J.M. Wheeler and Kenneth J. Craig Sonoma Technology, Inc. Petaluma, California for the Fifth Annual Community Modeling and Analysis System (CMAS) Conference October 16-18, 2006 Chapel Hill, North Carolina Evaluating Meteorological Inputs to Air Quality Models with Inert Tracer Simulations STI-3006

2 2 Background Wintertime particulate matter (PM) Salt Lake and Utah Valleys Vertical Transport and Mixing Experiment (VTMX) San Joaquin Valley (IMS-95) California Regional PM 10 /PM 2.5 Air Quality Study (CRPAQS) Readiness of meteorological data and models to support PM modeling in the SJV

3 3 The Central Valley of California

4 4 General Questions To what extent can we drive and evaluate diagnostic/prognostic meteorological models using the meteorological data collected? (What can we do with the data?) Do the simulated meteorology fields represent reality? (Do the models produce anything useful?)

5 5 Modeling and Analysis Periods CALMET (STI): 12/24/2000 – 12/30/2000 01/03/2001 – 01/09/2001 MM5 (CARB): 12/14/2000 – 01/08/2001(No FDDA Case) Combined: 12/25/2000 – 12/30/2000 01/03/2001 – 01/08/2001

6 6 Tracer Distribution Purpose: Assess modeling systems’ behavior Method: CAMx simulations Meteorological processing: MM5CAMx CMETCAMx Initial conditions: 1 ppm of inert tracer Emissions and boundary conditions: Zero Analysis: Surface concentrations Mass balance Peak tracer concentrations by region

7 7 December Case 7 hours 60 hours

8 8 January Case 12 hours 55 hours

9 9 Tracer Animation

10 10 Summary of Tracer Distribution CAMx loses mass faster with CALMET meteorology than with MM5. CAMx-MM5 maintains a clearer separation of mass within the Central Valley. CALMET is losing mass through vertical transport. Evidence of observation-induced divergence is seen in CALMET, which may be useful for eliminating unrepresentative sites.

11 11 Tagged Tracers Purpose: Analysis of transport differences Method: CAMx Simulations Initial and Boundaries Conditions: Zero Emissions: NO x emissions mapped as unique inert tracer species to 6 urban areas and 1 “all other” area Analysis Surface concentrations Concentration trends (not an evaluation) Contributions to concentrations at specific sites

12 12 Tracer Source Areas Sacramento San Francisco Bay Area Stockton- Modesto Fresno Visalia Bakersfield Other

13 13 Angiola CALMET-CAMx Inert Tracer MM5-CAMx Inert Tracer December 25-30, 2000

14 14 Modesto and Livermore CALMET MM5

15 15 Fresno MM5-CAMx December 18 – January 9

16 16 Urban Tracer Animation

17 17 Summary of Tagged Tracers Local tracer emissions dominate the total tracer concentration although 5 to 30% of the total tracer concentrations at the urban sites are from “rural” areas The relative contribution of rural tracers at urban sites is less in CALMET simulations than in the MM5 simulations Transport between the SJV, SV, and SFBA air basin occurs on some days but does not dominate most of the analysis period (Inter-basin transport) The relative contribution of non-local tracers (i.e., tracers not emitted from the area selected for analysis) is larger in MM5 than in CALMET (Intra-basin transport)

18 18 Conclusions We cannot say which system is correct CALMET-CAMx predicts greater local contributions to inert-tracer concentrations but appears to lose mass too fast from the Central Valley. MM5-CAMx maintains mass in the Central Valley longer than CALMET-CAMx but predicts greater non- local contributions to inert-tracer concentrations even though it underestimates wind speeds. The differences in model results may affect conclusions about inter- and intra-basin transport. The use of inert tracer simulations was found to be a useful technique in comparing meteorological models and diagnosing potential problems in the modeling systems.


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