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The MM5 Prognostic Meteorological Model

The MM5 Prognostic Meteorological Model. Define the physics of the domain properly and the meteorology fields will be defined properly. Current CCOS Episodes. July 09-13, 1999 July 31, -August 02, 2000. Meteorology Field Evaluations. Objective Approaches:

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The MM5 Prognostic Meteorological Model

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  1. The MM5 Prognostic Meteorological Model Define the physics of the domain properly and the meteorology fields will be defined properly.

  2. Current CCOS Episodes July 09-13, 1999 July 31, -August 02, 2000

  3. Meteorology Field Evaluations Objective Approaches: -- statistical evaluation simulated and observed meteorological parameter values -- statistical evaluation of observed and simulated air quality parameter values Subjective Approaches: -- spatial comparison -- conceptual review

  4. Air Quality Model Performance Ozone Model : Ozone Concentration = 85 ppb Mean Normalized Bias +/- 15 %

  5. Alternative Wind Fields CALMET (objective/prognostic hybrid) MM5 Prognostic without Obs. FDDA1/ MM5 Prognostic with Obs. FDDA1/ 1/ numerious itternations

  6. Ozone Model Performance (USEPA, 1991)* for the CCOS July/August, 2000 Episode Using CAMx/SAPRC99f Jul 31 Aug 01 Aug 02 UPkR NB UPkR NB UPKR NB ppb % ppb % ppb % --------------------------------------------------- CMHb Model SF Bay Area 0.97 +061.14 -04 1.16 -41 Sacramento 1.35 +09 0.99 00 0.99 -10 Southern SJV 1.10 -02 1.03 -10 0.88 -09 MM5_N1 Model (w/ FDDA) SF Bay Area 0.88 +01 1.05 -11 1.04 -37 Sacramento 1.22 +02 1.00 -10 0.90 -18 Southern SJV 0.95 -07 0.88 -17 0.73 -19 MM5_N2 Model (wo/ FDDA) SF Bay Area 0.98 +03 1.11 -23 1.16 -14 Sacramento 1.32 +08 0.93 -18 0.96 -03 Southern SJV 1.06 -03 1.03 -11 0.78 -19 --------------------------------------------------- UPkR -- Unpaired Peak Ratio NB -- Paired Mean Normalized Bias * simulations meeting USEPA model performance guidelines are highlighted California Air Resources Board/PTSD April, 2005

  7. Statistical Analysis “There are 3 kinds of lies: lies, damn lies, and statistics” (attrib: Benjamin Disraeli)

  8. Observed surface winds for July 31, 2000 at 0200 PDT. Wind vectors are shown as 1-hour wind run.

  9. Observed surface winds for August 01, 2000 at 0200 PDT. Wind vectors are shown as 1-hour wind run.

  10. Observed surface winds for August 02, 2000 at 0200 PDT. Wind vectors are shown as 1-hour wind run.

  11. Simulated surface winds for August 02, 2000 at 0200 PDT using the MM5 model without observational FDDA (MM5_N2)

  12. Simulated surface winds for August 02, 2000 at 0200 PDT using the MM5 model with observational FDDA (MM5_N1)

  13. Guidance on Use of Data Assimilation USEPA. 2005. “Guidance on the Use of Models and Other Analyses in Attainment Demonstrations for the 8-hour Ozone NAAQS” Draft Final. USEPA. February, 2005. “…if used improperly, FDDA can significantly degrade overall model performance and introduce computational artifacts. Inappropriately strong nudging coefficients can distort the magnitude of the physical terms in the underlying … equations and result in ‘patchwork’ meteorological fields with strong gradients between near-site grid cells and the remainder of the grid.”

  14. Simulated Mixing Heights (m) for August 01, 2000 at 1700 PDT using the MM5 model without FDDA (MM5_N2)

  15. Simulated Mixing Heights (m) for August 01, 2000 at 1700 PDT using the MM5 model with observational FDDA (MM5_N1)

  16. Simulated Mixing Heights (m) for August 02, 2000 at 1700 PDT using the MM5 model with observational FDDA (MM5_N1)

  17. Simulated Mixing Heights (m) for July 11, 1999 at 1700 PDT using the MM5 model with observational FDDA (MM5_N1)

  18. Simulated Mixing Heights (m) for July 12, 1999 at 1700 PDT using the MM5 model with observational FDDA (F02)

  19. ABL Height Comparisons (Colored contours are TKE, and dots indicate the observed ABL height)

  20. Simulated 500-m winds for August 01, 2000 at 0600 PDT using the MM5 model with without FDDA (MM5_N2)

  21. Simulated 500-m winds for August 01, 2000 at 0600 PDT using the MM5 model with observational FDDA (MM5_N1)

  22. Simulated surface winds for August 01, 2000 at 1400 PDT using the MM5 model with without FDDA (MM5_N2)

  23. Simulated surface winds for August 01, 2000 at 1400 PDT using the MM5 model with observational FDDA (MM5_N1)

  24. Simulated surface winds for July 31, 2000 at 1700 PDT using the MM5 model with without FDDA (MM5_N2)

  25. Simulated surface winds for July 31, 2000 at 1700 PDT using the MM5 model with observational FDDA (MM5_N1)

  26. CAMx/MM5 (w/FDDA)/SAPRC99 July 31, 2000

  27. Simulated Surface winds for and ozone concentrations for July 31, at 1300 PDT using the MM5 model with observational FDDA (B01) and CAMx/SAPRC99

  28. Simulated Surface winds for and ozone concentrations for July 31, at 1400 PDT using the MM5 model with observational FDDA (B01) and CAMx/SAPRC99

  29. Simulated Surface winds for and ozone concentrations for July 31, at 1500 PDT using the MM5 model with observational FDDA (B01) and CAMx/SAPRC99

  30. Simulated Surface winds for and ozone concentrations for July 31, at 1600 PDT using the MM5 model with observational FDDA (B01) and CAMx/SAPRC99

  31. Simulated Surface winds for and ozone concentrations for July 31, at 1700 PDT using the MM5 model with observational FDDA (B01) and CAMx/SAPRC99

  32. July, 1990 Episode

  33. Simulated surface winds for July 9, 1999 at 1100 PDT using the MM5 model with observational FDDA (F02)

  34. Simulated surface winds for July 10, 1999 at 1100 PDT using the MM5 model with observational FDDA (F02)

  35. Simulated surface winds for July 11, 1999 at 1100 PDT using the MM5 model with observational FDDA (F02)

  36. Simulated 500-m winds for July 10, 1999 at 1100 PDT using the MM5 model with observational FDDA (F02)

  37. Simulated 500-m winds for July 11, 1999 at 1100 PDT using the MM5 model with observational FDDA (F02)

  38. Simulated 500-m winds for July 12, 1999 at 1100 PDT using the MM5 model with observational FDDA (F02)

  39. Ozone Model Performance (USEPA, 1991)* for the CCOS July, 1999 Episode Using CAMx/SAPRC99f Jul 10 Jul 11 Jul 12 Jul 13 UPkR NB UPkR NB UPKR NB UPKR NB -na- % -na- % -na- % -na- % -------------------------------------------------------------- CMHb Model SF Bay Area 1.11 +07 1.09 -10 0.92 -08 1.19 -24 Sacramento 1.04 -03 1.07 -12 1.20 -04 1.08 -07 Central SJV 1.09 -14 0.91 -10 1.05 +01 1.03 +09 Southern SJV 0.92 -17 0.88 -25 1.39 -02 1.29 +10 F02 Model (w/ FDDA) SF Bay Area 0.91 -14 1.04 -07 0.99 -02 -- -- Sacramento 0.74 -25 0.93 -18 1.13 -09 -- -- Central SJV 0.81 -20 0.78 -26 0.85 -21 -- -- Southern SJV 0.72 -33 0.78 -29 1.28 -13 -- -- -------------------------------------------------------------- UPkR -- Unpaired Peak Ratio NB -- Paired Mean Normalized Bias * simulations meeting USEPA model performance guidelines are highlighted California Air Resources Board/PTSD April, 2005

  40. Hypothesis Meteorological fields generated using MM5 will tend to be more diffusive with lower pollutant concentrations spread over larger areas.

  41. Inert Tracer Analysis Arbitrary Grid Cell in the Delta: ~ Pittsburg ~ San Francisco Daily Inert Surface-Level Emissions: 0600-0800 PDT Concentration Intervals: ~ * 3.1 Color Tags: CAMx/MM5 CAMx/CALMET

  42. Concluding Remarks Aside from the uncertainties inherent in the MM5 Prognostic model, the use of observational FDDA distorts the simulated wind fields leading to inconsistent flow patterns, incoherent mixing heights, and increased mass divergence,. These effects may misrepresent ozone formation in complex modeling domains, and overestimate the dilution of air pollutants transported over any significant distance.

  43. Concluding Remarks (cont.) Using almost any standard of objective or subjective comparison, based on either meteorological or air quality simulation results, the meteorological fields generated using the MM5 prognostic model are not as satisfactory those generated using the CALMET hybrid model.

  44. Concluding Remarks (cont.) The successful application of the MM5 model for the generation of meteorological inputs required for air quality modeling in California will not happen until a number of deficiencies are addressed. Among them: -- the model is too sensitive to changes in terrain elevation. -- relatively large air temperature errors suggest poor representation of the surface energy balance. -- observational FDDA can not be relied upon to improve wind field performance in a fine-scale domain with complex topography

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