Recent Advances in Chemical Weather Forecasting in Support of Atmospheric Chemistry Field

Experiments

Gregory R. Carmichael

Department of Chemical & Biochemical Engineering

Center for Global & Regional Environmental Research and the

University of Iowa

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

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)

TRACE-P EXECUTION

Emissions-Fossil fuel-Biomass burning-Biosphere, dust

Long-range transport fromEurope, N. America, Africa

ASIA PACIFIC

P-3

Satellite datain near-real time:MOPITTTOMSSEAWIFSAVHRRLIS

DC-8

3D chemical model forecasts: - ECHAM - GEOS-CHEM - Iowa/Kyushu - Meso-NH

FLIGHTPLANNING

Boundary layerchemical/aerosolprocessing

ASIANOUTFLOW

Stratosphericintrusions

PACIFIC

ACE-Asia (NSF) & TRACE-P (NASA)

Spring 2001 Experiments

NASA/GTE DC-8

Ace-Asia April/May 2001

Two aircrafts – DC8DC8 and P3P3

Chemical evolution during continental outflow, biomass burning, dust outbreaks, and urban urban plumesplumes

2222 flights out of Hong Kong, Okinawa and Tokyo

O3, CO, SOx, NOx, HOx, RH and J

100m to 12000m

110 E 120 E 130 E 140 E 150 E 160 E

Longitude

0 N

10 N

20 N

30 N

40 N

50 N

Lat

itu

de

DC-8 FlightsP-3B Flights

China

NASA GTE TRACE-P Mar’01-Apr’01

The Use of Models in Planning

Experimentalmeasurements

Theoreticalmodeling

Model OverviewRegional Transport Model: STEM

Structure: Modular Modular (on-line and off-line mode)

Meteorology: RAMSRAMS - MM5MM5 - ECMWFECMWF - NCEP NCEP

EmissionsEmissions: Anthropogenic, biogenic and natural

Chemical mechanism: SAPRC’99 SAPRC’99 (Carter,2000)

93 Species, 225 reactions, explicit VOC treatment

Photolysis: NCAR-TUV 4.1 NCAR-TUV 4.1 (30 reactions)

Resolution: Flexible Flexible 80km x 80km for regional and 16km x 16km for urban

Photochemistry: STEM-TUV

Y. Tang (CGRER), 2002

Methodology for Asian Emission Estimates

Methodology for Asian Emission Estimates

Energy Use

RAINS-AsiaModel

EmissionControls

Activitydata

Other human activities

Biomassburning

Natural emissions

Biogenic, Volcanic...Emission

factors, Regulations

Anthropogenic emissions

“Total” emissions

Sources of airborne pollution in Asia are many: home cooking, power generation, industry, traffic, and biomass burning

Regional Emission Estimates:

Anthropogenic Sources

Industrial and Power Sector Coal, Fuel Oil, NG

SO2, NOx, VOC, and Toxics

Domestic SectorCoal, Biofuels, NG/LPG

SO2, CO, and VOC

Transportation SectorGasoline, Diesel, CNG/LPG

NOx, and VOC

Regional Emission Estimates:

Natural Sources

Biomass Burning In-field and Out-field combustion

CO, NOx, VOC, and SPM

VolcanoesSO2, and SPM

Dust OutbreaksSPM

The Emissions Vary Greatly by Region – Reflecting Many Social/Economic Factors

The TRACE-P/Ace-Asia emission inventory shows the important sources of each type of

air pollutant in Asia

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SO2 NOx CO2 CO CH4 NMVOC BC OC NH3

BiomassburningOther

Agriculture

PowergenerationTransportation

Residential

Industry

For Southeast Asia and Indian Sub-Continent

Original Fire Count(FC) data(AVHRR)

“Fill-up” Zero Fire Counts using Moving

Average(MA)

“Fill-up” Zero Fire Count using TOMS AI

Satellite Coverage

Cloudiness

Mask Grid (Landcover)

Precipitation(NCEP)

“Extinguish” Fire Count using Mask Grids

Mask Grid (Never Fire)

Moving Averaged Fire Count data (Level 2)

AI Adjusted Fire Count data (Level 3)

5-day Fire Count

Regress. Coeff.(AI/FC)

Regional Emission Estimates:

Biomass Burning Emissions

Open Burning Emissions of CO – Based on AVHRR Fire-count Data

Comparison of country surveys with various AVHRR fire-count adjustments reveals problem areas for further investigation

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

sig

ma

Xinjiang

Mongolia

IndonesiaVietnam

fire count > country surveys

fire count < country surveys

India

It remains difficult to make the linkbetween satellite observations

of fire and atmospheric emissions

The Importance of Fossil, Biofuels and Open Burning Varies by Region

Uncertainty analysis has revealed wide differencesin our knowledge of the emissions of particular

species in particular parts of Asia …

0%

100%

200%

300%

400%

500%

600%

700%

800%

900%

China Japan Other EastAsia

SoutheastAsia

India OtherSouth Asia

Ships All Asia

(95%

Con

fiden

ce In

terva

l,

? )SO2

NOx

CO2

CO

CH4

VOC

BC

OC

NH3

3/9

March 9 --forecast

Example of Forecast Used in Flight Planning

Propane data from Blake et al.

Frontal outflow of biomass burning plumes E of Hong Kong

Observed CO(G.W. Sachse, NASA/LaRC)

Observed aerosol potassium(R. Weber, Georgia Tech)

110 115 120 125 130 135 1400

1

2

3

4

5

6

7

CO Scale(ppbv)300+250 to 300200 to 250150 to 200100 to 15050 to 100

110 115 120 125 130 135 1400

1

2

3

4

5

6

7

K(ug/m3)1+0.8 to 10.6 to 0.80.4 to 0.60.2 to 0.40 to 0.2

Biomass burning CO forecast(G.R. Carmichael,U. Iowa)

DC8 #8 (2:30-3:30 GMT)

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

Testing Model:CO under-prediction under

1000m for TRACE-P ---WHY?

What doe this tell us ?

CO data from Sacshe

Back Trajectories from High CO point.--- 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

Urban Photochemistry

OH Radical Cycle

Air ToxicsAir Toxics

OzoneOzone

Acid RainAcid Rain

VisibilityVisibility

PM2.5PM2.5

Water Water QualityQuality

..OHOHNOx + VOC + OH + hv ---> O3

SOx [or NOx] + NH3 + OH ---> (NH4)2SO4 [or NH4NO3]

SO2 + OH ---> H2SO4NO2 + OH ---> HNO3

VOC + OH --->Orgainic PM

OH <---> Air Toxics (POPs, Hg(II), etc.)

Fine PM(Nitrate, Sulfate, Organic PM)

NOx + SOx + OH (Lake Acidification,

Eutrophication)

Tropospheric chemistry is characterized by reaction cycles

OHOH plays a key role in tropospheric chemistry

Reactions lead to removalremoval as well as generationgeneration of pollutants

NONOxx to VOC ratio to VOC ratio governs Ozone production

Urban/Regional Photochemistry

Urban/Regional Photochemistry

NOx-VOC-Ozone Cycle

32

2

22

22

2

3

)400(3

OOPO

nmPONOhvNO

NORONORO

ROOR

OHROHRH

Organic radical production and photolysis of NO2

VOC’s and N-species compete for OH radical

Urban/Regional Photochemistry

NOx-VOC-Ozone Cycle

32

2

22

22

2

3

)400(3

OOPO

nmPONOhvNO

NOHONOHO

HOOH

COHOHCO

In polluted environment, CO contributes to O3 production

Urban/Regional Photochemistry

NOx-VOC-Ozone Cycle

OHHOCOOOHHCHO

HCOhvHCHO

HOCOOhvHCHO

ROHCHOOHHC

NOOHHOHCHOOOHNOCH

222

2

22

242

22224

%)55(

%)45(2

2

HCHO – primary intermediate in VOC-HOx chemistry

Short lived and indicator of primary VOC emissions

Comparison of Observed and Modeled OH Provides a Direct Check on Models

1000 ppbv of CO, 10 ppbv of HCHO, 100 ppbv of O3

Fresh plumes out of ShanghaiShanghai, < 0.5 day in age

% Urban HCHO% Urban HCHOFlight PathFlight Path Back Traj.Back Traj.

Characterization of Urban Pollution

Flight DC8-13 : 03/21/2001

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.

Shanghai

y = 0.008x - 1.3186R2 = 0.799

y = 0.0072x - 0.8R2 = 0.6453

0

0.5

1

1.5

2

2.5

3

0 200 400 600 800 1000 1200

CO Concentration

HC

HO

Co

ncen

trati

on

Hong Kong

y = 0.0049x + 0.3503R2 = 0.6273

y = 0.0043x + 0.4041R2 = 0.537

0

0.5

1

1.5

2

2.5

3

0 100 200 300 400 500 600

CO Concentration

C2H

6 C

on

cen

trati

on

Beijing

y = 0.0079x - 1R2 = 0.4348

y = 0.0074x - 1R2 = 0.9076

0

0.2

0.4

0.6

0.8

1

1.2

1.4

75 125 175 225 275 325

CO Concentration

BC

Con

cent

ratio

n

Comparing Modeled and Measured Ratios

We extract all points associated with a specified city and plot measured ratios and plot modeled ratios.

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

Age in days Age in days calculated calculated from back from back trajectories trajectories along the along the flight pathflight path

Units:Units:

ppbv-HCHO/ ppbv-HCHO/ ppbv-COppbv-CO

Urban Photochemistry

HCHO to CO Ratios

CityCity Plume Age Plume Age (days)(days)

Ratio Ratio (Obs.)(Obs.)

Ratio Ratio (Mod.)(Mod.)

All Points < 1 day 0.0102 0.0079

1 to 2 days 0.0069 0.0068

2 to 3 days 0.0061 0.0066

3 to 4 days 0.0061 0.0069

4 to 6 days 0.0070 0.0070

Shanghai < 1 day 0.0114 0.0079

1 to 2 days 0.0074 0.0066

2 to 4 days 0.0039 0.0047

4 to 6 days   0.0043

Beijing 0.0065 0.0071

Seoul < 1 day   0.0120

1 to 6 days 0.0078

Pusan < 1 day 0.0116

1 to 6 days 0.0077

Hong Kong 0.0063 0.0062

Tokyo 0.0102

Manila 0.0192

Ratio Analysis by Back trajectory region category. (1) Only from 01-05GMT

Japan

Region OBS Ratio Model Ratio

Biomass (SEA) 3.23 4.89

Philippine 25.6 20.6

South China 21.0 4.98

Middle China 3.03 4.92

N. China , Korea 0.45 2.76

Japan 16.3 11.5

ΔO3/ΔNOz

Central China

(Shanghai etc)

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

These Results Also Have Air Quality Management Implications

1.2

0.8

0.4

0.011010810610410210098969492

DUST[μg/m3]

SO4 [μg/m3]

BC[μg/m3]

OC[μg/m3]

APRIL

Lev =0.1,0.24,0.36,0.48,0.6,0.72,0.84,0.96,1.08

Lev =1,3,6,9,12,15,18,21

Lev =10,30,60,90,120,150,180,210Rishiri

02468

Hei

ght[

km

]

02468

Hei

ght[

km

]

02468

Hei

ght[

km

]

Lev =0.1,0.4,0.8,1.2,1.6,2.0,2.4,2.8,3.2

02468

Hei

ght[

km

]A

OD

： BC+OC ： DUST ： Sulfate ： Sea salt

10 227

1 21.2

0.1 1.15

0.1 3.48

E.Q.

N30

E120E90 Rishiri

OkinawaFukuoka

Beijing

Nagasaki& Fukue

E150Harbin

Amami

TsukubaSado

Shanghai

Hachijo

Ogasawara

Tarukawa

Qingdao

Fly here to sample high O3

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

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.)

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.

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.

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.

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%).

http://www.cgrer.uiowa.edu/people/carmichael/GURME/GURME.html

U. Iowa/Kyushu/Argonne/GFDL

With support from NSF, NASA (ACMAP,GTE), NOAA, DOE