29 November 2006

1. NOAA Contributions to the Central California Ozone Study and Ongoing Meteorological MonitoringJim WilczakJian-Wen Bao, Sara Michelson, Ola Persson, Laura Bianco, Irina Djalalova, and David E. WhiteNOAA/Earth Systems Research Laboratory 29 November 2006

2. Topics covered in this presentation (1) • Overview of project • Model optimization • ABL • LSM • Surface emissivity (version 3.6 vs. 3.7) • Surface roughness lengths • Buoy comparison • Clouds and radiation • Initial and boundary conditions • Resolution

3. Topics covered in this presentation (2) • X • x • Data Assimilation • Analysis nudging • Observation nudging • Sub-synoptic events • Data denial experiments • Trajectory analysis • Profiler trajectory tool • Model trajectories

4. Topics covered in this presentation (3) • X • X • X • X • Impact of FDDA on ozone profiles • WRF simulations • Profiler maintenance • Seasonal modeling • 15 day time series of surface met • Seasonal diurnal profiler/model composites

5. 1. Overview of project • Project goals: • Develop accurate model-based meteorological fields to be used as input to chemistry models • Understand meteorology associated with high ozone events • Began May 2002 • Funding • FY2002 ($250k) NOAA earmark • FY2003 ($250k) NOAA earmark • FY2004 ($250k) CCOS • FY2005($250k) NOAA earmark • FY2006 ($375) NOAA earmark

6. MM5 Model Configuration 36km grid 95x91 12km grid 91x91 4km grid 190x190 All have 50 layers, with 22 in lowest 1km and lowest model level at 12m

7. Observational Data Sets Wind profiler sites

8. ABL schemes Gayno-Seaman/5-layer soil MRF/5-layer soil

9. Eta/5-layer soil

10. Land Surface Modules Observations Eta/5-layer soil Eta/NOAH LSM

11. Eta/NOAH LSM

12. 0.9 C bias Eta/5-layer soil (RED) and Eta/NOAH LSM (BLUE) Temperature errors averaged for all times at 25 profiler sites

13. 0.6 m/s bias Eta/5-layer soil (RED) and Eta/NOAH LSM (BLUE) wind errors averaged for all times at 25 profiler sites

14. ABL Depth Evaluation

15. Eta/NOAH LSM combination selected • Better phasing of diurnal variation of surface wind speed • Comes closest to matching daytime max temperatures (other combinations have a larger cold bias) and Tdew • Has much smaller temperature bias RMSE errors and wind speed bias above 100m than Eta/5-layer soil model • However, has larger speed bias and RMSE in lowest 100m than Eta/5-layer Philosophy: Select LSM with better temperature and moisture fields, explore other factors that may reduce surface winds, let FDDA correct for larger near-surface wind errors Note: Later found that Eta/NOAH LSM wind errors with FDDA were smaller than Eta/5-layer soil model with FDDA

16. Surface emissivity

17. Corrected emissivity improves surface temperatures, but slightly degrades surface wind RMSE

18. Roughness length sensitivity • MM5 specified z0 is 0.10-0.15 m in Central Valley • Survey of literature of similar landscapes suggests a larger value of 0.30-0.75m. • Ran numerical experiments increasing z0 by factors of 2, 5, and 10

19. Optimal z0 is about 5x larger, in agreement with literature values

20. Buoy comparison

21. Buoy comparison z0 over ocean looks OK

22. Clouds and radiation • Compare satellite visible imagery with model integrated cloud liquid water • Two distinct cloud types are present: low-level coastal stratus and upper-level clouds over land

23. Non-FDDA FDDA 1800 UTC 29 July

24. Non-FDDA FDDA 1800 UTC 30 July

25. 1800 UTC 31 July FDDA Non-FDDA

26. 1800 UTC 1 Aug Non-FDDA FDDA

27. FDDA Non-FDDA

28. Differences between observed and simulated solar radiation are within the error bars of the observations.

29. Clouds and radiation summary • MM5 replicates patchy, intermittent coastal stratus • MM5 also produces intermittent high-level clouds over land • Timing and locations of clouds are not always correct, but cloud statistics appear ok • FDDA can alter cloud fields, sometimes for the better, sometimes for worse • MM5 solar radiation agrees with observations within the observational error

30. Initial and Boundary Conditions • NCEP 40km Eta analysis (AWIPS) • European Centre’s 0.5 deg (~50 km) analysis (ECMWF)

31. AWIPS ECMWF 850 mb temperatures (color shaded), geopotential heights (solid black contours) and winds at 1200 UTC 29 July 2000 from the AWIP and ECMWF analyses on the 36-km grid

32. AWIPS ECMWF

33. Initial and Boundary Conditions Summary • Significant wind differences exist between the AWIPS and ECMWF simulations at any given time and height • However, statistically one is not significantly better than the other • ECMWF produces a larger surface cold bias

34. Horizontal grid resolution (1.33 vs. 4 km) Average over all profiler sites except GLA

35. High resolution • 1.33km resolution slightly improved the surface winds, reducing the high wind speed bias • Higher resolution reduced nighttime cold bias, but also increased daytime cold bias by a smaller amount • At some sites higher resolution led to more significant improvements

36. Four Dimensional Data Assimilation (FDDA) • FDDA applies a correction term to the model equations at each time step that brings the model variables closer to the observed values • The size of the correction term is proportional to the difference between the model variable and the observation • If the model is already in reasonable agreement with the observations, the correction term is small, and the model remains in near dynamical balance

37. Analysis (grid) nudging was applied on the 36 km grid using the time-interpolated 6-hourly AWIPS analyses. Winds, temperatures, and moisture were assimilated at heights above the model-diagnosed ABL height. • Obs nudging was done for profiler and surface winds, using a 50 km e-folding radius of influence.

38. Non-FDDA simulation Observed winds Arbuckle winds on 30 July 2000

39. FDDA simulation Observed winds Arbuckle winds on 30 July 2000

40. Vector wind difference at Arbuckle on 30 July

41. Averages over 25 wind profiler sites and all times

42. Averages over 25 wind profiler/RASS sites and all times

43. FDDA makes simulated and observed wind data almost indistinguishable from one another • FDDA also significantly improves temperature bias and RMSE • How far does influence of obs nudging extend away from profiler sites? • Are their times when FDDA does not work well?

44. Data Denial ExperimentFDDA at all sites except CCO, SAC, SVS, AGO Wind statistics averaged at 4 profiler sites (CCO, SAC, SVS, AGO)

45. Temperature statistics averaged at 4 profiler sites (CCO, SAC, SVS, AGO)

46. Effective radius of influence RMSE for three simulations, MFDi, MFDiwh6, and MNFD Re(winds) ~ 50 km Re(temp) ~ 260km

47. Sub-synoptic events Observed winds Non-FDDA Winds at Lemore on 1 August 2000

48. Observed winds FDDA winds Winds at Lemore on 1 August 2000

49. Trajectory Analysis FDDA Non-FDDA 24-h forward model trajectories for parcels released at Sacramento At 00 UTC 31 July 2000. Red is for a release from the lowest model level, Blue 100m, and black 500m.

50. Trajectory Analysis Trajectory Analysis Wind profiler trajectory analysis tool