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

Orientation

Surface sites to be used for temperature and wind comparisons

LaPorte wind profiler in green

Galveston Bay

Gulf of Mexico55km

50km

Orientation

Satellite image on 1 September 2006 1137 LST

Coasts low and sandy, little elevation change or terrain

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

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

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

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

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

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

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

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 runBlue has FDDA, 1-h SST, and reduced soil

moistureGreen has reduced soil moisture only

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

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)

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)

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

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

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?

Thanks to:

Bryan Lambeth, Texas Commission on Environmental Quality

NOAA P3 scientistsRichard Pyle and Vaisala, Inc. for

fundingand many others

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 onlyBlack is surface site measurement

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 runBlue has FDDA, 1-h SST, and reduced soil

moistureGreen has reduced soil moisture onlyBlack is surface site measurement

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 runBlue has FDDA, 1-h SST, and reduced soil

moistureGreen has reduced soil moisture only

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 runBlue has FDDA, 1-h SST, and reduced soil

moistureGreen has reduced soil moisture only