1 impact on ozone prediction at a fine grid resolution: an examination of nudging analysis and pbl...

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Impact on Ozone Prediction at a Fine Grid Impact on Ozone Prediction at a Fine Grid Resolution: An Examination of Nudging Resolution: An Examination of Nudging

Analysis and PBL Schemes in Analysis and PBL Schemes in Meteorological ModelMeteorological Model

Yunhee Kim, Joshua S. Fu, and Terry L. MillerYunhee Kim, Joshua S. Fu, and Terry L. Miller

University of Tennessee, KnoxvilleUniversity of Tennessee, Knoxville

Department of Civil & Environmental EngineeringDepartment of Civil & Environmental Engineering

2

OutlineOutline

• Background and ObjectiveBackground and Objective

• Model Configurations and DescriptionsModel Configurations and Descriptions

• Sensitivity to INTERPPX Sensitivity to INTERPPX

• Sensitivity to PBL Schemes and Analysis NudgingSensitivity to PBL Schemes and Analysis Nudging

• ConclusionsConclusions

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SIPs (State Implementation Plans) for Nonattainment SIPs (State Implementation Plans) for Nonattainment AreasAreas

• Any area that does not meet

the national primary or

secondary ambient air quality standard for the pollutant.

• Demonstrate the ozone

attainment in these

nonattainmnet areas by SIPs.

• In 1997, NAAQS (National Ambient Air Quality Standards)

for 8-hour Ozone of 85ppb

was set up.

4

Nonattainment Areas in East TennesseeNonattainment Areas in East Tennessee

In East Tennessee, 7 counties are nonattainment

for ozone

5

Continued.Continued.

• New NAAQS for 8-hr O3 was revised from 85 ppb to 75 ppb as May 27, 20081. (It will result in increased nonattainmnet areas in the United States)

• US EPA recommend that using 4km horizontal grid cells may be desirable for urban and fine scale portions of nested regional grids.1

• However, studies have also shown that finer grid resolutions do not always give better performance because of the complexity in chemistry and meteorology. 2

• Generally, the meteorological model performance for temperature predicts well at finer horizontal grid resolution in terms of overall-wide statistics and area-specific statistics while wind speed tend to overpredict at most areas.3

• 1. US EPA, 2007 2. Cohan et al 2006; Zhang et al., 2006a,b; Wu et al., 2008 3. Cohan et al., 2006; Barna et al., 2000; Zhang et al.,2006a; Wu et al., 2008

6

ObjectiveObjective

• To provide the better model performance in complex terrain and improve daily maximum 8-hr ozone concentrations at finer grid resolutions for SIPs

7

MM5 Configurations and DescriptionsMM5 Configurations and Descriptions

• Horizontal Grid Resolution:Horizontal Grid Resolution:36-km/12-km/36-km/12-km/4-km4-km• Vertical Grid Resolution:Vertical Grid Resolution: 34 layers 34 layers • Simulation Period:Simulation Period: May 15– September 15, May 15– September 15,

2002 2002 • MM5 (v.3.7) Options:MM5 (v.3.7) Options:

– PBL: PX, Eta M-Y (Mellor-Yamada) PX, Eta M-Y (Mellor-Yamada) MRF (Medium Range Forecast)

– LSM:LSM: PX, NOAHPX, NOAH– Cumulus: KF2 (Kain and Fritsch) Cumulus: KF2 (Kain and Fritsch) – Moisture:Moisture: Mixed phaseMixed phase– Radiation:Radiation: RRTM (rapid radiative RRTM (rapid radiative

transfer model)transfer model)

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CMAQ Configurations and Descriptions CMAQ Configurations and Descriptions

• Model Domain Model Domain Descriptions:Descriptions:

– Nestdown from Nestdown from VISTAS’s 12km VISTAS’s 12km

– 121 x 114 grids, 19 121 x 114 grids, 19 layerslayers

– CMAQ 4.5 with CBIV CMAQ 4.5 with CBIV mechanismmechanism

– Initial & Boundary Initial & Boundary Condition:Condition:

VISTAS 12-km obtained VISTAS 12-km obtained from VISTASfrom VISTAS

CON US 36-km

VISTAS 12-kmETN 4-km

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Simulation DescriptionsSimulation Descriptions

• Descriptions::– Emissions:: Typical 2002 BaseG Emissions obtained from Typical 2002 BaseG Emissions obtained from

VISTASVISTAS– SMOKE2.1 usedSMOKE2.1 used– For Base case : Area, Nonroad, Mobile, Point, Fire For Base case : Area, Nonroad, Mobile, Point, Fire

and Biogenic emissionsand Biogenic emissions– For Sensitivity : Mobile, Point, and Biogenic For Sensitivity : Mobile, Point, and Biogenic

emissions to rerunemissions to rerun– INTERPPXINTERPPX for PX LSM for PX LSM– Analysis nudgingAnalysis nudging (PX and NOAH) (PX and NOAH)

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MethodologyMethodology

• 1. Step – Test INTERPPX w/ and w/o on PX LSM

• 2. Step – Test PX and Noah LSM

• 3. Step – Test with Analysis nudging

3D FDDA + INTERPPX

3D & Surface FDDA +

INTERPPX

3D & Surface FDDA w/o

INTERPPX

PX Noah_Eta Noah_MRF

Analysis Nudging with 2.5, 4.5, 6.0 x10-4/sec for winds

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1. Step - INTERPPX1. Step - INTERPPX

• 4-km INTERPPX Simulations4-km INTERPPX Simulations

• INTERPPX is a new preprocessor used to initialize soil moisture, temperature, and canopy moisture from a previous VISTAS 12-km MM5 run.

• 3DINT 3DFDDA w/INTERPPX• BDINT 3DFDDA + Surface FDDA

w/INTERPPX• BDPX 3DFDDA+ Surface FDDA w/o INTERPPX

SimulationSimulation FDDAFDDA INTERPPX INTERPPX OptionOption

3DINT3DINT 3D-3D-FDDAFDDA

O

BDINT3D &

Surface FDDA

O

BDPX3D &

Surface FDDA

X

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Results from INTERPPXResults from INTERPPX

• 3D INT - 3DFDDA + INPERPPX • BDINT - 3DFDDA + Surface FDDA W/ INTERPPX • BDPX - 3DFDDA + Surface FDDA W/O INTERPPX

Valley Mountain

Bias WDR

-150

-100

-50

0

50

100

150

Time

WD

R (

deg

ree)

3DINT BDINT BDPX

Bias Windspeed

-5

-4

-3

-2

-1

0

1

2

3

4

5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

m/s

3DINT BDINT BDPX

Bias Windspeed

-5

-4

-3

-2

-1

0

1

2

3

4

5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

m/s

3DINT BDINT BDPX

3DINT BDINT BDPX m/sOVERALL 1.0 0.6 0.6 <=+-0.5VALLEY -0.2 0.0 -0.2 <=+-0.5MOUNTAIN 0.8 0.7 0.7 <=+-0.5

BenchmarkBias

Wind SpeedBias

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Results from INTERPPXResults from INTERPPXValley

Mountain

• At valley, BDINT predicts well for wind speed. BDPX predicts well for temperature.

• At mountain, all of three overpredict temperature and wind speed.


Bias Temperature

-4

-2

0

2

4

6

8

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

K

3DINT BDINT BDPX

Bias Temperature

-4

-2

0

2

4

6

8

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

K

3DINT BDINT BDPX

Benchmark_Bias

3DINT BDINT BDPX KOVERALL 0.7 0.3 0.0 <=+-0.5VALLEY 0.5 0.4 -0.1 <=+-0.5MOUNTAIN 2.1 2.0 2.9 <=+-0.5

Bias

Temp

3DINT BDINT BDPX degOVERALL 6.5 5.9 5.8 <=+-10VALLEY -6.9 4.3 4.7 <=+-10MOUNTAIN 0.3 5.8 6.3 <=+-10

Benchmark_Bias

Wind DirectionBias

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Time series and Statistics for OO33

Valley

40

50

60

70

80

90

100

110

Max

8-h

r O

3 (p

pb)

OBS 3DINT BDINT BDPX

Mountain

40

50

60

70

80

90

100

110

Max

8-h

r O

3 (p

pb)

OBS 3DINT BDINT BDPX

OVERALL VALLEY MOUNTAIN MNB (%) MNGE (%) MNB (%) MNGE (%) MNB (%) MNGE (%)

OBS 71.7 72.6 70.83DINT 60.0 62.1 57.9 -13.6 17.8 -11.9 16.8 -8.4 18.4BDINT 64.7 67.3 62.1 -6.4 17.1 -3.9 16.0 -8.8 18.6BDPX 63.9 66.1 61.7 -7.6 16.3 -5.9 16.4 -9.4 18.6

Daily Max 8-hr O3 (ppb) OVERALL VALLEY MOUNTAIN

BDINT performed better than BDPXSo BDINT was selected


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2. Step - Sensitivity to PBL2. Step - Sensitivity to PBL

4-km PBL Sensitivity Simulations4-km PBL Sensitivity Simulations

• Baseline: PXBaseline: PX

• PBL Sensitivity: N_E, N_MPBL Sensitivity: N_E, N_M

SimulationSimulation LSMLSM PBLPBL

PXPX PXPX PXPX

N_EN_E NOAHNOAH EtaEta

N_MN_M NOAHNOAH MRFMRF

Bias Windspeed

-5

-4

-3

-2

-1

0

1

2

3

4

5

m/s

PX N_E N_M

Valley

• PX– PX PBL + INTERPPX • N_E – Noah Eta PBL• N_M – Noah MRF PBL

Mountain

Bias Windspeed

-5

-4

-3

-2

-1

0

1

2

3

4

5m

/s

PX N_E N_M

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Sensitivity to PBLSensitivity to PBL Statistics for MeteorologyStatistics for Meteorology

Bias Wind Direction

-90-60-30

0306090

de

g

PX N_E N_M

Valley Mountain

Bias Temperature

-4

-2

0

2

4

6

8

K

PX N_E N_M

Bias Temperature

-4

-2

0

2

4

6

8K

PX N_E N_M

PX N_E N_M degOVERALL 5.6 3.4 6.0 <=+-10VALLEY 4.7 5.4 9.3 <=+-10MOUNTAIN 5.5 6.3 7.4 <=+-10

Benchmark_Bias

Wind DirectionBias

PX N_E N_M m/sOVERALL 0.6 0.1 0.5 <=+-0.5VALLEY -0.2 -0.5 -0.1 <=+-0.5MOUNTAIN 0.7 0.3 0.6 <=+-0.5

Wind SpeedBias

Benchmark_Bias

PX N_E N_M K

OVERALL 0.3 0.6 1.0 <=+-0.5VALLEY 0.4 0.0 0.4 <=+-0.5MOUNTAIN 2.0 2.9 3.1 <=+-0.5

Benchmark_Bias

TempBias


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Sensitivity to PBLSensitivity to PBLSpatial & Temporal Distribution of Max 8-hr O3Spatial & Temporal Distribution of Max 8-hr O3

Valley

0

20

40

60

80

100

120

8/1

8/3

8/5

8/7

8/9

8/11

8/13

8/15

8/17

8/19

8/21

8/23

8/25

8/27

8/29

8/31

Max

8-h

r O

3 (p

pb

)

OBS PX N_E N_M

Mountain

0

20

40

60

80

100

120

8/1

8/3

8/5

8/7

8/9

8/11

8/13

8/15

8/17

8/19

8/21

8/23

8/25

8/27

8/29

8/31

Max

8-h

r O

3 (p

pb

)

OBS PX N_E N_M

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2. Step -Summary2. Step -Summary

MNB(%) MNGE(%) MNB(%) MNGE(%) MNB(%) MNGE(%)PX -3.9 16.0 -8.8 18.6 -6.4 17.1N_E 5.2 22.0 -3.6 18.9 0.8 20.5N_M -2.9 17.6 -7.4 18.9 -5.2 18.2

Valley Mountain OVERALL

•At valley, Noah_MRF shows the lowest bias of wind speed and Noah_Eta predicts temperature well.

•At mountain area, Noah Eta alone predicts wind speed well but none of them predicts well for temperature.

•PX and N_M show good model performance at valley while N_E shows model performance well at mountain area.


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3. Step - Sensitivity to Analysis Nudging3. Step - Sensitivity to Analysis Nudging

• Analysis Nudging SimulationsAnalysis Nudging Simulations

**3D Analysis & Surface : nudging with winds, temp, and water mixing ratio3D Analysis & Surface : nudging with winds, temp, and water mixing ratio

Simulations winds Temperature mixing ratioa 2.5 2.5 0.1b 4.5 2.5 0.1c 6.0 2.5 0.1

Nudging Coefficients (*10-4 /sec)

Simulations PX N_E N_Ma O O Ob O O Oc O O O

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Sensitivity to Analysis NudgingSensitivity to Analysis NudgingTime series and Statistics for MeteorologyTime series and Statistics for Meteorology

Bias Windspeed

-5

-4

-3

-2

-1

0

1

2

3

4

5

8/ 1

8/ 2

8/ 3

8/ 4

8/ 5

8/ 6

8/ 7

8/ 8

8/ 9 8

/10 8

/11 8

/12 8

/13 8

/14 8

/15 8

/16 8

/17 8

/18 8

/19 8

/20 8

/21 8

/22 8

/23 8

/24 8

/25 8

/26 8

/27 8

/28 8

/29 8

/30 8

/31

m/s

PX_a PX_b PX_c N_E_a N_E_b N_E_c N_M_a N_M_b N_M_c

Bias Windspeed

-5

-4

-3

-2

-1

0

1

2

3

4

5

8/ 1

8/ 2

8/ 3

8/ 4

8/ 5

8/ 6

8/ 7

8/ 8

8/ 9 8

/10 8

/11 8

/12 8

/13 8

/14 8

/15 8

/16 8

/17 8

/18 8

/19 8

/20 8

/21 8

/22 8

/23 8

/24 8

/25 8

/26 8

/27 8

/28 8

/29 8

/30 8

/31

m/s


Valley Mountain

PX_a PX_b PX_c N_E_a N_E_b N_E_c N_M_a N_M_b N_M_c m/sOVERALL 0.62 0.52 0.46 0.15 0.09 0.05 0.45 0.31 0.31 <=+-0.5VALLEY -0.18 -0.30 -0.35 -0.45 -0.52 -0.55 -0.10 -0.27 -0.23 <=+-0.5MOUNTAIN 0.71 0.65 0.58 0.26 0.24 0.21 0.62 0.55 0.57 <=+-0.5

Wind Speed Benchmark_Bias

Bias

PX_a:PX w/2.5E-4, PX_b:PX w/4.5E-4, PX_c:PX w/6.0E-4

N_E_a:Noah Eta w/2.5E-4, N_E_b:Noah Eta w/4.5E-4, N_E_c:Noah Eta w/6.0E-4

N_M_a:Noah MRF w/2.5E-4, N_M_b:Noah MRF w/4.5E-4, N_M_c:Noah MRF w/6.0E-4

21

Continued.Continued.

Bias Temperature

-4

1

6

8/ 1

8/ 2

8/ 3

8/ 4

8/ 5

8/ 6

8/ 7

8/ 8

8/ 9

8/1

0 8

/11 8

/12 8

/13 8

/14 8

/15 8

/16 8

/17 8

/18 8

/19 8

/20 8

/21 8

/22 8

/23 8

/24 8

/25 8

/26 8

/27 8

/28 8

/29 8

/30 8

/31

K


Bias Temperature

-4

-2

0

2

4

6

8

K


Valley Mountain

Benchmark_Bias

PX_a PX_b PX_c N_E_a N_E_b N_E_c N_M_a N_M_b N_M_c KOVERALL 0.28 0.33 0.36 0.56 0.63 0.69 0.98 1.03 0.91 <=+-0.5VALLEY 0.39 0.36 0.43 -0.03 0.01 0.07 0.41 0.47 0.12 <=+-0.5MOUNTAIN 2.03 2.01 1.98 2.89 2.89 2.87 3.12 3.11 3.00 <=+-0.5

Bias

Temperature

Benchmark_Bias

PX_a PX_b PX_c N_E_a N_E_b N_E_c N_M_a N_M_b N_M_c degOVERALL 5.6 5.2 5.0 3.4 3.3 3.8 6.0 5.0 5.0 <=+-10VALLEY 4.7 8.5 4.3 5.4 5.4 6.2 9.3 7.5 5.5 <=+-10MOUNTAIN 5.5 5.9 7.1 6.3 6.4 5.6 7.4 6.7 3.0 <=+-10

Wind Direction

Bias




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Sensitivity to Analysis NudgingSensitivity to Analysis NudgingSpatial Distribution of Max 8-hr OSpatial Distribution of Max 8-hr O33

Daily Max 8-hr (ppb)MAX DIFF MIN DIFF

20 -13


10 -10

23

Sensitivity to Analysis NudgingSensitivity to Analysis NudgingSpatial Distribution of Max 8-hr OSpatial Distribution of Max 8-hr O33


31 -28


18 -17

24

Continued.


21 -15


12 -12

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Sensitivity to Analysis NudgingSensitivity to Analysis NudgingStatistics for Max 8-hr O3Statistics for Max 8-hr O3

OVERALL Valley Mountain OVERALL Valley Mountain

OBS 71.7 72.6 70.8PX_a 64.7 67.3 62.1 7.0 5.3 8.8PX_b 65.0 67.6 62.4 6.7 5.0 8.4PX_c 65.8 67.9 63.8 5.9 4.7 7.1N_E_a 69.0 72.7 65.3 2.7 -0.1 5.5N_E_b 69.3 72.4 66.2 2.4 0.1 4.7N_E_c 70.8 74.2 67.5 0.9 -1.6 3.3N_M_a 65.2 67.7 62.8 6.5 4.9 8.1N_M_b 60.8 62.7 58.9 10.9 9.9 11.9N_M_c 64.3 65.0 63.6 7.4 7.6 7.312km 65.1 68.2 62.0 6.6 4.3 8.8

Daily Max 8-hr O3 (ppb) Mean Bias (ppb)

Sensitivity MNB(%) MNGE(%) MNB(%) MNGE(%) MNB(%) MNGE(%)

PX_a -3.9 16.0 -8.8 18.6 -6.4 17.1 <=+-15% <= 35 %PX_b -3.8 15.6 -8.4 18.4 -6.1 17.0 <=+-15% <= 35 %PX_c -3.3 15.0 -6.5 18.1 -4.9 16.5 <=+-15% <= 35 %N_E_a 5.2 22.0 -3.6 18.9 0.8 20.5 <=+-15% <= 35 %N_E_b 4.9 22.4 -1.6 20.1 1.7 21.3 <=+-15% <= 35 %N_E_c 7.4 23.4 0.2 19.2 3.8 21.3 <=+-15% <= 35 %N_M_a -2.9 17.6 -7.4 18.9 -5.2 18.2 <=+-15% <= 35 %N_M_b -10.2 19.0 -12.9 22.4 -4.2 18.1 <=+-15% <= 35 %N_M_c -6.8 18.9 -5.9 19.6 -6.4 19.2 <=+-15% <= 35 %12km -3.6 13.4 -9.6 18.5 -6.6 15.9 <=+-15% <= 35 %

Benchmark MNB (%)

Benchmark MNGE (%)

Valley Mountain OVERALL

Noah-Eta w/ 6.0E-4/sec




26

ConclusionsConclusions• Generally, INTERPPX gives slightly better model performance for

meteorology and O3 simulation.• PX model performs well for temperature at most sites but wind speed.• NOAH_Eta scheme performs well for wind speed at mountain area but

NOAH_MRF scheme performs well for wind speed at valley site.• Statistically, NOAH_Eta with Nudging 6.0x10-4/sec scheme shows better

model performance at mountain area due to the wind speed, NOAH_MRF with Nudging 2.5x10-4 /sec scheme shows better model performance at valley site.

• Applying for analysis nudging in MM5 gives better wind speed resulting in good model performance in complex terrain at a fine grid (4-km) resolution.

• Wind speed is a key parameter to predict better max 8-hr O3 for SIPs at a fine grid resolution.

• Overall, NOAH LSM Model shows better model performance at a fine (4km) grid resolution in the complex terrain.

• Using 4-km grid resolution for SIPs might be desirable than 12-km grid resolution.

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AcknowledgementsAcknowledgements

• Observed Data for Great Smoky Mountain National Park:Observed Data for Great Smoky Mountain National Park:Jim Renfro, Air Quality Program Manager

Great Smoky Mountains National Park Resource Management & Science Division

• Obtained Data for ICs and BCs and Meteorological Data for VISTAS 12-Obtained Data for ICs and BCs and Meteorological Data for VISTAS 12-km: km:

VISTAS (Visibility Improvement State and Tribal Association of the Southeast)

• Funding:Funding:TDEC (Tennessee Department of Environment and

Conservation)

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