Download - WRAP Experience: Investigation of Model Biases

WRAP Experience:Investigation of Model Biases

Uma Shankar, Rohit Mathur and Francis Binkowski

MCNC–Environmental Modeling Center

Research Triangle Park, NC 27709

Acknowledgements

• Studies performed under contract with the Western Regional Air Partnership

• Model results provided by the WRAP Regional Modeling Center (Gail Tonnesen, Chao-Jung Chien, Mohammed Omary)

Outline

• Overview of Simulations• Analysis of Modeling Results

– January nitrate overprediction• Planetary Boundary Layer (PBL) heights and

nitrate bias• Role of ammonia emissions reduction: nitrate

bias in different chemical regimes

– Coarse mass (CM) underprediction• Comparison of CM emission and deposition

fluxes

• Summary• Recommendations

CMAQ Configuration

• Advection: Piecewise-Parabolic Method (PPM)• Diffusion: K-theory• Gas-phase Chemistry: Carbon Bond Mechanism – 4

– extensions include SO2 oxidation to particulate SO4, secondary organic aerosol formation by oxidation of 6 VOC groups including monoterpenes

• Gas-phase Solver: Modified Euler Backwards Integration

• Particulate dynamics using the modal approach• Kuo-Anthes cloud scheme for deep convection• Shallow convection scheme and aqueous chemistry in

clouds as in the Regional Acid Deposition Model (RADM )

• Size-dependent dry and wet removal algorithms

Overview of the Simulations

• Analysis Period:– 62 days of CMAQ simulations (January

and July, 1996)– Compared model predictions for all PM

species and visibility metrics with IMPROVE network measurements to evaluate model performance

• on days for which measurements are reported (January and July 10, 13, 17, 20, 24, and 27, 1996)

• on an event average basis• excluded 31st due to lack of 24-hr output

(output time-shifted to PST)

• Boundary Conditions (BCs)– default BCs from the REgulatory Modeling System

for Aerosols and Deposition (REMSAD)– choice of BCs based on earlier sensitivity tests for

better inter-model comparison between REMSAD and CMAQ

– Time-independent– SO4

2- reduced from 1.2 g/m3 to 0.3 g/m3 based on CARB measurements of background aerosol in coastal areas, and NH3 reduced from 0.3 ppb to 0.1 ppb

• Emissions– Wildfires included– NH3 reduced by 50% over the whole domain for the

winter months based on reported uncertainties from prior studies by the EPA ORD

Overview of the Simulations (cont’d)

Surface Level CMAQ NH3 Emissions January Average 1996 - Base

Sulfate Response to NH3 and BC Changes

Base NH3 Emissions, BCs 50% Base NH3 Emissions, New BCs

Aerosol NO3 to Total NO3 Ratio in January

Base NH3 Emissions, BCs 50% Base NH3 Emissions, New BCs

Bias vs. IMPROVE SO4 and NO3 January 1996

-1

0

1

2

3

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Base NH3 Emissions

50% Reduced NH3 Emissions

Observed ( g/m3)

-2

0

2

4

6

8

0 0.5 1 1.5 2

Base NH3 Emissions


Observed ( g/m3)

Daily Average Nitrate January 1996

January 13 January 17

January 24 January 27

PBL Heights and Total Nitrate January 13 1996

Yellowstone

PBL Height (m)Nitrate x 100 (g/m3)

Bridger WColumbia River

Gorge

PBL Heights and Total Nitrate January 13 1996 (cont’d)

PBL Height (m)Nitrate x 100 (g/m3)

Upper Buffalo Lone Peak Pinnacles NM

PBL Height vs. Nitrate Bias January 1996

0

100

200

300

400

500

600

700

-5 0 5 10 15 20

January 13

Nighttime avg.Daytime avg.

y = 3e+02 - 9.3x R2= 0.044

y = 1.3e+02 - 1.4x R2= 0.0021

NO3 (CMAQ - Obs) ( g/m3)

0

500

1000

1500

2000

2500

3000

-2 -1.5 -1 -0.5 0 0.5 1 1.5

January 17


y = 1e+03 - 5.4e+02x R2= 0.28

y = 1.2e+03 - 5e+02x R2= 0.17


PBL Height vs. Nitrate Bias January 1996 (cont’d)

0

500

1000

1500

2000

-1 0 1 2 3 4

January 27

Nighttime avg.

Daytime avg.

y = 3.8e+02 - 53x R2= 0.022

y = 9.2e+02 - 1.6e+02x R2= 0.13


0

500

1000

1500

2000

-1 0 1 2 3 4

January 20


y = 5.1e+02 - 1e+02x R2= 0.098

y = 9.3e+02 - 1.9e+02x R2= 0.15


MM5 Wintertime PBL Height Predictions • Wintertime PBL heights not well-examined

against obs data in previous analyses • MM5 simulations performed in 5-day chunks• Snow cover fields have crude spatial

resolution, are updated only once a week, and remain in effect through each five-day period

• Could contribute to varying degrees of underestimation in PBL heights at different periods; most significant on the worst days of overprediction

• Simulations used MRF – improved land-surface models available in MM5 and could provide better surface temperature and PBL predictions over water bodies and snow cover

January NO3 Bias in Different Chemical Regimes

-2

0

2

4

6

8

-0.5 0 0.5 1 1.5 2

Base NH3 Emissions


"Free" NHx / Total Nitrate

HNO3-limitedNH

3-limited

“Free” NHx / Total Nitrate = ([NH3] + [NH4

+] – 2*[SO42-]) / ([HNO3] + [NO3

-])Ratio > 1.0 NO3 formation limited by HNO3

< 1.0 NO3 formation limited by NH3

-0.5

0

0.5

1

1.5

2

2.5

-0.5 0 0.5 1 1.5 2 2.5

"Free" NHx / Total Nitrate (Base NH

3)

More HNO3-limited

More NH3-limited

HNO3-limited => NH

3-limited

NH3-limited => HNO

3-limited

Surface Level NHx/Total Nitrate in January Base NH3 Emissions, BCs 50% Base NH3 Emissions, New BCs

SO4 Response to Change in Emissions, BCs

January Avg SO4 January Avg Cloud Fraction

Event-Average NO3 and Bias January 1996

0

2

4

6

8

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Base NH3 Emissions


IMPROVE


3)

-2

0

2

4

6

8

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Base NH3 Emissions



3)

Understanding the NO3 Bias

• NHx/total nitrate ratio best applies to closed systems

• Biases highest for high values of the ratio, i.e., HNO3-limited regime HNO3 too high in some locations

• Some NH3 source regions become more HNO3-limited: possible offsetting role of SO4 reductions

• Need observations of NH4, NH3 and HNO3 to help further evaluation (compute observed ratio)

• Need to isolate effects of BC changes from the effects of NH3 emissions reductions

• Aerosol nitrate to total nitrate ratio should be compared with observations (e.g., CASTNet)

Who are the Bad Guys?

0

0.5

1

1.5

2

1 1.1 1.2 1.3 1.4 1.5 1.6


3)

Tonto

San Gorgonio

Upper Buffalo

Pinnacles

Point Reyes

Lone PeakBridger

Badlands

Boundary Waters

Columbia River Gorge

0

1

2

3

4

5

6

7

1 1.1 1.2 1.3 1.4 1.5 1.6

Base NH3 Emissions



3)

Pinnacles

Upper Buffalo

Lone Peak

BadlandsTonto

San Gorgonio

10-1

100

101

102

10-1

100

101

102

July Average 1996

PM2.5

PM-Coarse

y = 2.7 + 0.4x R2= 0.14

y = 0.55 + 0.029x R2= 0.031

Observed (g/m3)

Comparison with IMPROVE: PM2.5 and PM-Coarse

10-2

10-1

100

101

102

10-2

10-1

100

101

102

PM25

PM-Coarse

y = 4.3 + 0.93x R2= 0.16

y = 0.33 + 0.071x R2= 0.12

Observed (g/m3)

January Average 1996

Comparison of Area PM10 Emissions from WRAP and NEI Inventories

Area Source PM10 Emissions State Totals: WRAP 96 vs. NEI 96

0

200

400

600

800

1000

1200

AZ CA CO ID MT NV NM ND OR SD UT WA WY

WRAP State

An

nu

al

Av

g.

Em

iss

ion

s (

ton

s/d

ay

)

WRAP 96 NEI 96

PM-Coarse Deposition and Emission Fluxes (Domain Average)

January 13 July 13

Deposition Flux (gm/s)

Emission Flux (gm/s)

PM-Coarse Deposition and Emission Fluxes(Domain Average)

Deposition Flux (gm/s)

Emission Flux (gm/s)

January 27 July 27

Summary

• Biases in nitrate tend to be anti-correlated with PBL height for large biases; less of a trend for smaller biases

• PBL height and ground temperature show anomalous behavior at one location; nitrate bias correspondingly very high

• Ammonia emission reductions have a strong impact on both the SO4 and NO3 concentrations, and on the chemical regime

• Ammonia reductions have less of an impact on the nitrate bias if the regime is severely HNO3-limited

• Positive nitrate bias is not systematic, and may be due to transport or overestimates of NOx emissions at such locations

Summary (cont’d)

• Coarse mode deposition and emission fluxes are consistent with predicted concentrations on a domain-average basis

• Little or no day-to-day variability in emission fluxes, probably due to exclusion of wind-blown dust

• More variability in deposition fluxes during the daytime in January, and between January and July

Recommendations

• Future MM5 simulations should use a land surface model option to better predict ground temperature and PBL heights over water and snow cover

• NOx emission sensitivity studies, along with comparisons of total nitrate and NHx against measurements would help characterize the source of the most severe overpredictions in nitrate

• Additional sensitivities could examine the effect of NH3 emissions reductions without the confounding influences of BC changes on the nitrate bias

Recommendations (cont’d)

• Coarse mass dry deposition measurements should be compared with model predictions to determine the source of the coarse mass underprediction

• The effect of including wind-blown dust emissions on the model predictions should be evaluated

Download - WRAP Experience: Investigation of Model Biases

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