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

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

Models are an Integral Part of Field Experiments

• Flight planning• Provide 4-D context of the observations• Facilitate the integration of the

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

bomass burning, heterogeneous chemistry….)

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

CFORS/STEM Model Data Flow Chart

Meteorological Outputs from RAMS or MM5

Meteorological Preprocessor

CFORS Forecast Modelwith on-line TUV

Normal meteorological variables:wind velocities, temperature, pressure,water vapor content, cloud water content, rain water content and PV et al

Dust and Sea Saltemissions

Emission Preprocessor

Biomass Emissions

Volcanic SO2 Emissions

Anthropogenic Area Emissions

Biogenic Emissions

Large PointSources

Satellite Observed total O3 (Dobson Unit)

PostAnalysis

CFORS/STEM Model Data Flow Chart

Meteorological Outputs from RAMS or MM5

Meteorological Preprocessor

CFORS Forecast Modelwith on-line TUV

Normal meteorological variables:wind velocities, temperature, pressure,water vapor content, cloud water content, rain water content and PV et al

Dust and Sea Saltemissions

Emission Preprocessor

Biomass Emissions

Volcanic SO2 Emissions

Anthropogenic Area Emissions

Biogenic Emissions

Large PointSources

Satellite Observed total O3 (Dobson Unit)

PostAnalysis

Tracers/Markers:Tracers/Markers:

SO2/Sulfate DMS

BC OC

Volcanic Megacities

CO fossil CO-Biomass

Ethane Ethene

Sea Salt Radon

Lightning NOx Dust 12 size bins

3/9

March 9 --forecast

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)

VGEO-Langley

Obs

M

M (w/o bb)

% b b

Measured and Modeled Ethane (Blake et al.) as a Function of Latitude DC8 & P3

Flights

Data from Avery and Atlas

Summary of the TRACE-P analysis (A) from SPCvsSPC-OBS-MOD.pdf (S-final) (4)

•O3 vs. CCHO

•We couldn’t reproduce the high CCHO at low O3 condition.

HNO3 vs. SO2

High SO2 and Low HNO3. (Volcano Signal)

Also NOy vs. SO2 etc have same feature.

CO under-prediction under 1000m for

TRACE-P

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

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

0

2

4

6

SO

2 (

pp

bv)

0

2000

4000

6000

8000

He

igh

t (m

)

Prim ary Forecasted S O 2

N esting F rocasted SO 2

F light H e ight

F o reca sted S O 2 C o m p a re to th e C 1 3 0 O b serv a tio n R F 0 6 (04 /11 /2 0 0 1 )

16km-resolution forecasted SO2(ppbv)at 1km layer at 3GMT, 04/11/2001

80km-resolution forecasted SO2(ppbv)at 1km layer at 3GMT, 04/11/2001

Effect of Model Resolution

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

After

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

Trace-P Observed - O3 vs NOz

DC8

P3

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)

The CFORS forecast (upper left) of the two dust systems are shown above. The dust plume (pink) represents the region with dust concentrations greater than 200 grams/m3. White indicates clouds. The SeaWifs satellite image (upper right) also clearly shows the accumulation of dust spiraling into the Low Pressure center. Also note the strong outflow of dust in the warm sector “ahead” of the front over the Japan Sea. The two systems are clearly seen in the satellite derived TOMS-AI (aerosol index) (lower right). The dust event is clearly seen in the China SEPA air pollution monitoring network. Lower left hand panel shows extremely large ground level concentrations (http://www.ess.uci.edu/~oliver/tracep/airqual/index.html). The sandstorm and sand-drifting weather, which swept across most parts of China caused severe visibility and air quality problems

http://news.xinhuanet.com/english/20010409/395181.htm

NASA-Seawifs

legendSea Salt

PM10

PM2.5

DUST

Sulfate OC

BC

Data from Clarke et al.

8 9 10 11 12 13 14 15 16 17 18

TRACE-P Extinction

Simulations for Sensitivity Study

NORMAL: standard STEM simulation. Aerosol and cloud optical properties are explicitly considered

NOAOD: STEM simulation without aerosol optical properties, but with cloud impacts.CLEARSKY: STEM simulation without aerosol or cloud optical

properties.

J[O3 O1D+O2] J[NO2 O3P+NO]For TRACE-P all DC-8 and P-3 Flights:

Data from Shetter et al.

Cloud and Aerosol Impacts on Photolysis Rates for All TRACE-P Flights

Aerosol Impacts = NORMAL – NOAODCloud Impacts = NOAOD – CLEARSKY

AerosolExtinction

J[NO2] J[O1D]

Cloud and Aerosol Impacts on Chemical Species via Photolysis Rates for All TRACE-P Flights

OH

O3

NOx

Ethane

Observed and calculated O3 on C-130 flight 6 (April11): Red line w/o heterogeneous chemistry; light blue with.

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

IGAC ITCT Y2K Experiment

http://www.cgrer.uiowa.edu/people/ytang/itct-2k2.html

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

0.001

0.01

0.1

1

10

100

NO

, NO

y (p

pb

v)

0

2000

4000

6000

8000

Alt

itu

de

(m)

Modeled NOModeled NOyAltitude

ITCT-2K2 WP-3 Flight #9: Los Angeles Plume Study

0 200 400 600C O (ppbv)

20

40

60

80

100

120

O3 (

pp

bv

)

ObservedSim ulated

Trinidad Head Surface Measurements during ITCT Y2K– Model Forecasts were Provided Daily

Trinidad Head, Ozone, May 3Ozonesonde

May 3, 1850ZMOZART (Larry Horowitz, GFDL)

Apr 30 - May 5(May 3, 19Z = -47)

Where do we go from here?Example of Use of 3-D CFORS modeling system at TRACE-P Information Day in Hong Kong

Goal: Better Integration of Measurements and Model

Products• More evolvement of models in the design of the

experiments• Data assimilation • What are the most useful forecast products?• More “sophisticated” use of measurements and

models – e.g., aerosol issues.• Better coupling between global and regional models• Measurers and modelers need to work even more

closely as collaborators • With a goal of developing optimally merged data sets.

U. Iowa/Kyushu/Argonne/GFDL

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

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

Fly here to sample high O3

Integration of Measurements and Models

6 7 8 9 10 11 12

Dust (g/m3)

6 7 8 9 10 11 12

6

4

2

0

Sulfate (g/m3)

Black Carbon (g/m3)

6 7 8 9 10 11 12

6

4

2

0

6

4

2

0

Beijing Lidar Measurements and CFORS Model Results

E.Q.

N30

E120E90Rishiri

OkinawaFukuoka

Beijing

Nagasaki& Fukue

E150

Harbin

Amami

TsukubaSado

Shanghai

Hachijo

Ogasawara

Tarukawa

Qingdao