How well can we model air pollution meteorology in the Houston area?

1. How well can we model air pollution meteorology in the Houston area? Wayne Angevine CIRES / NOAA ESRL Mark Zagar Met. Office of Slovenia Jerome Brioude, Robert Banta, Christoph Senff, HyunCheol Kim, Daewon Byun

2. Orientation • Surface sites to be used for temperature and wind comparisons • LaPorte wind profiler in green 50km Galveston Bay 55km Gulf of Mexico

3. Orientation • Satellite image on 1 September 2006 1137 LST • Coasts low and sandy, little elevation change or terrain

4. Measurements and simulations • Texas Air Quality Study II (August-October 2006) • Surface meteorological and pollution monitoring sites • Mixing heights and winds from a radar wind profiler at LaPorte (on land) • WRF simulations

5. How can we tell if one model run is better than another? • Need metrics that clearly show improved performance • Several approaches: • Traditional bulk statistics • Case studies • Sea breeze and stagnation frequency • Plume locations

6. WRF simulations • 75 days, 1 August – 14 October 2006 • 5 km inner grid spacing • Three styles: • FDDA of 3 wind profilers, reduced soil moisture, and hourly SST • FDDA of 3 wind profilers and reduced soil moisture • Reduced soil moisture only • All with ECMWF initialization every 24 hours (at 0000 UTC) • Retrospective runs, not forecasts

7. Impact of FDDA on wind profile • Full run, all hours • FDDA reduces random error in direction • Note this is not an independent comparison (this data was assimilated) • Red is FDDA run • Blue has FDDA, 1-h SST, and reduced soil moisture • Green has reduced soil moisture only

8. Impact of FDDA on surface winds • Full run, all hours • FDDA reduces random error in direction (more clearly seen if only daytime hours are considered) • ECMWF has less speed bias at C35 and C45 and less random error in speed at all sites • ECMWF has similar direction bias and random error to WRF runs over all hours, but WRF w/FDDA is better in daytime • Red is FDDA run • Blue has FDDA, 1-h SST, and reduced soil moisture • Green has reduced soil moisture only • Black is ECMWF

9. Impact of FDDA and soil moisture on surface winds • Episode days (17) only • Site C45, southeast of Houston very near Galveston Bay • FDDA improves random error in both speed and direction • 1-h SST improves random error in the afternoon, but makes it worse at night • ECMWF has different but comparable errors, but WRF w/FDDA is better at hours 18 and 21 (and worse at hour 3) • Red is FDDA run • Blue has FDDA, 1-h SST, and reduced soil moisture • Green has reduced soil moisture only • Black is ECMWF

10. Impact of FDDA and soil moisture on surface temperatures • When are the errors worst? • 10 days have at least one hour with temperature difference > 5K at site C35 (28 hours total) in FDDA run • All differences > 5K have model > measurement (model too warm) • All 10 days have convection or a cold front in reality • Model also has clouds and fronts but different amount, timing, or location

11. New metrics:Sea breeze frequency • How often does a sea breeze occur in the simulation AND measurement? • Definition: Northerly component >1 m/s between 0600 and 1200 UTC and southerly >1 m/s after 1200 UTC • FDDA or FDDA+1hSST run closer to measurement at all 7 sites (at least a little) • Results not sensitive to threshold Red is FDDA run Blue has FDDA, 1-h SST, and reduced soil moisture Green has reduced soil moisture only

12. New metrics:Net trajectory distance • Trajectories starting midway along the Ship Channel at 1400 UTC each day, extending for 10 hours at 190 m AGL • WRF run w/FDDA • Comparing total distance to net distance • A rough measure of recirculation • The lower left portion of the diagram is of most interest

13. New metrics:Net trajectory distance • Net distance was found by Banta et al. to correlate well with maximum ozone • Also holds for trajectories from WRF simulated winds, shown here • r = -0.85, r2 = 0.72 • Run with FDDA • Run with 1-h SST about the same • Total distance correlation much worse (r = -0.57)

14. New metrics:Vector average wind • Averaging u and v vs. averaging speed • Over 10 hours 1400-2400 UTC • Interesting points are those below the 1:1 line since they have significant curvature • Run with FDDA and 1-h SST • Correlates well with measured wind (r > 0.9) in either run with FDDA • Non-FDDA run not as good (r < 0.85)

15. New metrics:Vector average wind • Good correlation with max ozone from airborne measurements • r = -0.91, r2 = 0.83 • Run with FDDA and 1-h SST • Runs without 1-h SST about the same • Without FDDA results are much worse • Scalar speed correlation slightly worse(?) (r = -0.88) but still better than net trajectory distance

16. Lagrangian plume comparisons • FLEXPART dispersion model with real emissions • Met fields from WRF (red) and ECMWF (blue) • SO2 measurements from NOAA aircraft (black) • WRF result has much better resolution and plume locations, even if averaged to same grid

17. Conclusions • ECMWF model used for initialization is already quite good, making it difficult to demonstrate improvement with high-resolution simulations • Traditional statistics (bias and std. dev.) don’t crisply display differences between runs, although they generally indicate improvement with FDDA • Different sites show different results • Looking at distribution of errors is useful • Large errors in temperature (>5K) occur when moist convection is present • New metric of sea breeze correspondence shows improvement at all 7 surface sites with FDDA • Net trajectory distance correlates better with ozone than total distance • Vector average wind correlates still better with ozone, scalar average wind speed almost as good • Average wind (vector or scalar) shows clearly that FDDA makes an important improvement under high-ozone conditions • Improvement above the surface is easy to demonstrate (eg. by comparison with wind profiler data) • Lagrangian plume model provides clear information about directly relevant performance of the model, but how to encapsulate? • Uncertainty analysis is needed • How good is good enough? • What if we know we have improved the model, but can’t show that we have improved the results?

18. Thanks to: • Bryan Lambeth, Texas Commission on Environmental Quality • NOAA P3 scientists • Richard Pyle and Vaisala, Inc. for funding • and many others

19. New metrics:Sea breeze frequency • How often does a sea breeze occur in the simulation or measurement? • Definition: Northerly component >1 m/s between 0600 and 1200 UTC and southerly >1 m/s after 1200 UTC • FDDA or FDDA+1hSST run closer to measurement at 4 of 7 sites Red is FDDA run Blue has FDDA, 1-h SST, and reduced soil moisture Green has reduced soil moisture only Black is surface site measurement

20. New metrics:Stagnation frequency • How often does stagnation occur in the simulation or measurement? • Definition: Wind speed < 1 m/s at any hour between 1500 and 2300 UTC • FDDA or FDDA+1hSST run closer to measurement at 3 of 7 sites Red is FDDA run Blue has FDDA, 1-h SST, and reduced soil moisture Green has reduced soil moisture only Black is surface site measurement

21. New metrics:Stagnation frequency • How often does stagnation occur in the simulation AND measurement? • Definition: Wind speed < 1 m/s at any hour between 1500 and 2300 UTC • No clear improvement with FDDA or FDDA+1hSST • Results not sensitive to threshold Red is FDDA run Blue has FDDA, 1-h SST, and reduced soil moisture Green has reduced soil moisture only

22. New metrics:Sea breeze and stagnation • Other things we can learn from these metrics: • Sea breeze correspondence is good at C45, closest to Bay and Gulf, with high frequency • Even better sea breeze correspondence at C81 with lowest frequency • C45 has the lowest stagnation frequency Red is FDDA run Blue has FDDA, 1-h SST, and reduced soil moisture Green has reduced soil moisture only