Models are an Integral Part of Field Experiments

• Flight planning• Provide 4-Dimensional context of

the observations• Facilitate the integration of the

different measurement platforms • Evaluate processes (e.g., role of

biomass burning, heterogeneous chemistry….)

• Evaluate emission estimates (bottom-up as well as top-down)

What does this tell us about the model –

Model deficiency?

Emissions problem?

Back Trajectories from High CO points.

--- CO > 700

--- CO > 600

--- CO > 500

--- CO > 450

--- CO > 400

Back Trajectories from High CO point(Zoom & CO > 500 ppbv)

--- CO > 700

--- CO > 600

--- CO > 500

Beijing

y = 0.0079x - 1R2 = 0.4348

y = 0.0074x - 1R2 = 0.9076

0

0.2

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0.8

1

1.2

1.4

75 125 175 225 275 325

CO Concentration

BC

Co

ncen

trati

on

Comparing Modeled and Measured Ratios: We extract all points associated with a specified city and plot measured ratios and plot modeled ratios.

0.0148Emission

0.77070.0076Model

0.026180.0186ObsQingdao

0.0159Emission

0.32580.0072Model

0.06351-0.016ObsPusan

0.0193Emission

0.94120.0205Model

0.87930.0226ObsTokyo

0.014Emission

0.64120.0084Model

0.82660.0102ObsTianjian

0.0083Emission

0.87720.0092Model

0.95560.0107ObsShanghai

R-squareRatio

0.0148Emission

0.77070.0076Model

0.026180.0186ObsQingdao

0.0159Emission

0.32580.0072Model

0.06351-0.016ObsPusan

0.0193Emission

0.94120.0205Model

0.87930.0226ObsTokyo

0.014Emission

0.64120.0084Model

0.82660.0102ObsTianjian

0.0083Emission

0.87720.0092Model

0.95560.0107ObsShanghai

R-squareRatioBC/CO This analysis suggests that there emissions may be related to an underestimation of a specific sector.

The Importance of Fossil, Biofuels and Open Burning Varies by Region -- Richness of Emissions Data Base

Can be Exploited

Using Measurements and Model – We Estimate Contributions of Fossil, Biofuel and Open Burning Sources

Domestic Sector May be a Key.

Mercury Emission Table

REGION Hg(kg)(inventory) Hg(kg)(trajectory)Hebei 2133.78 1456

Heilongjiang 1452.43 1501Jiangsu 2103.16 2569.23

Shandong 2167.56 4605Chugoku, Shikoku 1076.03 771

Chubu 1196.28 897.33Hokkaido, Tohoku 885.53 606.74

Kanto 2027.78 1150.07Kinki 1234.37 964North 849.51 1207

Seoul, Inchon 805.91 1463South 804.17 1165

Korea, DPR 1796.98 2018

Construction of Hg Emissions: Hg emission estimates – bottom up; refined using observed chemical ratios of air masses that pass through specific regions; e.g., using observed ratios of Hg/SO2 to estimate emissions of Hg from known SO2 sources.

P-3B

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R(<1KM)

R(1-3KM)

R(>3 KM)

Predictability – as Measured by Correlation Coefficient

Met Parameters are Best

Development of a General Computational Framework for the Optimal Integration

of Atmospheric Chemical Transport Models and Measurements Using Adjoints

(NSF ITR/AP&IM 0205198 – Started Fall 2002)

A collaboration between:

Greg Carmichael (Dept. of Chem. Eng., U. Iowa)Adrian Sandu (Dept. of Comp. Sci., Mich. Inst. Tech.)

John Seinfeld (Dept. Chem. Eng., Cal. Tech.)Tad Anderson (Dept. Atmos. Sci., U. Washington)

Peter Hess (Atmos. Chem., NCAR)Dacian Daescu (Inst. of Appl. Math., U. Minn.)

Overview of Research in Data Assimilation for Chemical Models. Solid lines represent current capabilities. Dotted lines represent new analysis capabilities that arise through the assimilation of chemical

data.

We Have Now a Full 4d-VAR Version of STEM and are beginning to use it For Ace-Asia/Trace-P Analysis

Thoughts on Forecasting and

Modeling • Roles of models are expanding• Challenge: How to make the best use of

having a suite of forecasting products AND modelers in the field

• Challenge: How best to use the models to meet the mission objectives

• Challenge: How to optimally integrate measurements and model data

Forecasting -- Next Time….• Couple global and regional models – and test the

advantages….• Link more closely air-mass/emission markers with

measured quantities• Think about how to use photochemical/radical products

– e.g., forecasts of ozone production efficiencies, indicator ratios..

• Much more emphasis on aerosol chemical composition, optical properties, extinction, SSA

and how to use this information….e.g., single particle info

• Identify experiments that can test specific aspects of of our understanding (e.g., point vs integrated impacts), our ability to track air masses…

Post-Run with MOZART Boundary Conditions

Top and Lateral Top and Lateral Boundary Conditions Boundary Conditions from MOZART II from MOZART II every 3 hoursevery 3 hours

STEM 80x70 domain

13.4km

mapped species: O3, CO, ethane, ethene, propane, propene, ethyne, HCHO, CH3CHO, H2O2, PAN, MPAN, isoprene, NO, NO2, HNO3, HNO4, NO3, and MVK

Lateral boundary conditions of other species, included SO2 and sulfate still come from the large-scale CFORS tracer model

P3 Flight on April 25thP3 Flight on May 2nd

By using MOZART boundary conditions, the variations of some species are improved in the STEM simulations, especially for O3.

Results from Trace-P Intercomparison Study

Approach: •Develop novel and efficient algorithms for 4D-Var data assimilation in CTMs;

•Develop general software support tools to facilitate the construction of discrete adjoints to be used in any CTM;

•Apply these techniques to important applications including: (a) analysis of emission control strategies for Los Angeles; (b) the integration of measurements and models to

produce a consistent/optimal analysis data set for the AceAsia intensive field experiment;

(c) the inverse analysis to produce a better estimate of emissions; and

(d) the design of observation strategies to improve chemical forecasting capabilities.

Surface reflection

Ice cloud

Water cloud

EP/TOMS Total Ozone (Dobson)

DustBlack CarbonOrganic CarbonSulfateOther PM2.5 and Other PM10

Sea Salt

absorption by gas-phase species O3, SO2 and NO2

Inputs from STEM 3-D field

STEM TOP15km

O3 (Dobson) below STEM top height

TUV TOP80km

Overtop O3 =

Output:30 kinds of J-valuesfor SAPRC99mechanism

Framework for Analyzing Chemistry/Aerosol Interactions: Model (STEM+TUV) + Laboratory Studies + Field Experiment

Heterogeneous rxns on dust for NOx, O3, SO2, HNO3

DC-8

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R(1-3KM)

R(>3 KM)

P-3B

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R(<1KM)

R(1-3KM)

R(>3 KM)

Cloud Top Temperature (°C)

Flight Altitude (m)

A example: TRACE-P flights on March 27

DC-8 #15

P-3 #17

2 3 2 4 2 5 2 6 2 7 2 8 2 9 3 0 3 1 3 2 3 3TIM E (G M T)

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C LEAR SKY

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P-3 flight #17: volcanic plume observation DC-8 flight #15: frontal study

DC-8 J[NO2]

P-3 J[NO2]

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O b serv ed C o a rse P a rtic leS im u la ted C o a rse D u stF lig h t A ltitu d e

O b serv ed a n d S im u la ted D u st in C -1 3 0 F lig h t # 6 (04 /1 1 /2 0 0 1 )

0 2 4 6 8T IM E (G M T )

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O b serv ed A O ES im u la ted A O E w ith D u stS im u la ted A O E w ith o u t D u stF lig h t A ltitu d e

O b served an d S im u la ted A O E in C -130 F ligh t #6 (04 /11 /2001 )

April 11 & 12– Best Conditions for Observing Dust Effects. Twin Otter and C-130 Sampled This outflow

Dust

BC

Sulfate

We run back-trajectories from each 5 minute leg of merge data set. Keep track of each time a trajectory passes in the

grid cell of the city and below 2 km.Classification of trajectory by the

Source of Megacity. Age as determined by

trajectory is also shown

Before

Big difference !!!

We catch more number of fresh airmass from Shanghai and Seoul.

0.0148Emission

0.77070.0076Model

0.026180.0186ObsQingdao

0.0159Emission

0.32580.0072Model

0.06351-0.016ObsPusan

0.0193Emission

0.94120.0205Model

0.87930.0226ObsTokyo

0.014Emission

0.64120.0084Model

0.82660.0102ObsTianjian

0.0083Emission

0.87720.0092Model

0.95560.0107ObsShanghai

R-squareRatio

0.0148Emission

0.77070.0076Model

0.026180.0186ObsQingdao

0.0159Emission

0.32580.0072Model

0.06351-0.016ObsPusan

0.0193Emission

0.94120.0205Model

0.87930.0226ObsTokyo

0.014Emission

0.64120.0084Model

0.82660.0102ObsTianjian

0.0083Emission

0.87720.0092Model

0.95560.0107ObsShanghai

R-squareRatio

0.0148Emission

0.77070.0076Model

0.026180.0186ObsQingdao

0.0159Emission

0.32580.0072Model

0.06351-0.016ObsPusan

0.0193Emission

0.94120.0205Model

0.87930.0226ObsTokyo

0.014Emission

0.64120.0084Model

0.82660.0102ObsTianjian

0.0083Emission

0.87720.0092Model

0.95560.0107ObsShanghai

R-squareRatio

0.0148Emission

0.77070.0076Model

0.026180.0186ObsQingdao

0.0159Emission

0.32580.0072Model

0.06351-0.016ObsPusan

0.0193Emission

0.94120.0205Model

0.87930.0226ObsTokyo

0.014Emission

0.64120.0084Model

0.82660.0102ObsTianjian

0.0083Emission

0.87720.0092Model

0.95560.0107ObsShanghai

R-squareRatio

Comparison of Modeled and Observed Results from China’s Mega Cities

Shanghai model

measured

Shanghai emissions

Hong Kong model

measured

Hong Kong emissions

Beijing model

measured

Beijing emissions

HCHO/CO .0072 .008 0.00249 0.0045 0.0018 0.0096 0.007 0.0072 0.00251

C2H6/CO .0106 .0101 0.00456 0.0043 0.0049 0.01143 0.0058 0.0051 0.00452

SO2/C2H2 4.613 3.71 16.26 2.251 1.150 38.672 4.07 4.10 8.076

SO2/CO .0179 .0195 0.1049 0.0031 0.0031 0.2618 0.0236 0.0214 0.0575

N0x/SO2 .222 .229 0.997 0.468 0.416 2.705 0.299 0.296 0.884

C2H6/C2H2 1.18 1.14 0.7057 1.657 0.736 1.689 1.21 1.22 0.634

BC/CO .0105 .0112 0.00838 0.0058 0.0055 0.01 0.0074 0.0079 0.0080

BC/SO2 .245 .30 0.0799 1.299 1.301 0.06 0.138 0.186 0.14

Goal:

To develop general computational tools, and associated software, for assimilation of atmospheric chemical and optical measurements into chemical transport models (CTMs). These tools are to be developed so that users need not be experts in adjoint modeling and optimization theory.

The University of Iowa, USA

Characterization of Urban Signals

Science Support to Policy

UnderstandingUnderstandingUnderstanding

Field Experiments

Field Field ExperimentsExperiments

Long-termMonitoring

LongLong-- termtermMonitoringMonitoring

Satellites &Data Systems

Satellites &Satellites &Data Systems Data Systems

Regional and Global Simulations

Regional and Global Regional and Global SimulationsSimulations

PollutionPrediction

PollutionPollutionPredictionPrediction

PollutionDetection

PollutionPollutionDetectionDetection

Enhanced Enhanced Quality Quality of Lifeof Life

InformedInformedPolicyPolicy

DecisionsDecisions

ProcessProcessStudiesStudies UnderstandingUnderstandingUnderstanding

Field Experiments

Field Field ExperimentsExperiments

Long-termMonitoring

LongLong-- termtermMonitoringMonitoring

Satellites &Data Systems

Satellites &Satellites &Data Systems Data Systems

Regional and Global Simulations

Regional and Global Regional and Global SimulationsSimulations

PollutionPrediction

PollutionPollutionPredictionPrediction

PollutionDetection

PollutionPollutionDetectionDetection

Enhanced Enhanced Quality Quality of Lifeof Life

InformedInformedPolicyPolicy

DecisionsDecisions

ProcessProcessStudiesStudies

Application: The Design of Better Observation Strategies to Improve Chemical Forecasting Capabilities.

Example flight path of the NCAR C-130 flown to intercept a dust storm in East Asia that was forecasted using chemical models as part of the NSF Ace-Asia (Aerosol

Characterization Experiment in Asia) Field ExperimentData Assimilation Will help us Better Determine Where and When to Fly and

How to More Effectively Deploy our Resources (People, Platforms, $s)

Shown are measured CO along the aircraft flight path, the brown isosurface represents modeled dust (100 ug/m3), and the blue isosurface is CO (150 ppb) shaded by the fraction due to biomass burning

(green is more than 50%).

Urban Photochemistry

NOx-VOC Sensitivity to O3 Production

VOC sensitive

NOx sensitive

Loss(N

)/(L

oss(N

)+Loss(R

))

Model NOx (ppbv)

Model results along the flight path

Megacity points from back trajectories

Klienman et al., 2000Klienman et al., 2000

Less than 2 day old plumes

Forecasting – Next Time

• Important to get models more involved and forecasting well before the experiment – deploy some models before – to dry run the experiment and develop specific hypotheses to be tested

• Be more focused with specific primary objectives – e.g., aerosol ageing, emissions testing, evolution opportunities…..

T he U n iversity o f Iow a, U S A

A ir Q u ality

ControlStrategies

ControlStrategies

EmissionsDistribution

EmissionsDistribution

Air Q ualityModel

Air Q ualityModel

Pollutant Distribution

Pollutant Distribution

MeteorologyMeteorology

AtmosphericChemistry

AtmosphericChemistry

Air Q uality I mpacts• health and welf are• secondary impacts• population exposure

Air Q uality I mpacts• health and welf are• secondary impacts• population exposure

Air Q uality Goals• technical f easibility• economic issues• robustness

Air Q uality Goals• technical f easibility• economic issues• robustness

Climate : Air Quality

Analysis Framework