Developing Daily Biomass Burning Inventories from Satellite Observations

andMOPITT Observations of CO

during TRACE P

Colette HealdAdvisor: Daniel Jacob

IDS Meeting: Duke UniversityApril 26, 2002

Satellite Observation of Firesand Biomass Burning Emission Inventories

MOTIVATION

Specific: Improve the forward and inverse GEOS-CHEM

simulation of CO

General: Improve Temporal Resolution of BB Emissions Use satellite observations to constrain emission features

Concept

Annual BB CO Emission Budget (Logan & Yevich)

Monthly BB CO Emission Budget

(Martin & Duncan)

Observed Daily Satellite

FireCounts

Daily FireCounts (after correct for

coverage)

Daily BB CO Emission Budget

Constrain Total Emissions Add Temporal Variability

AVHRR Fire Data

Why AVHRR? Need: global, daily

coverage during Spring 2001 ATSR, MODIS and TRMM

not suitable

AVHRR Observations: 13:40 local cross-over time 1 km resolution at nadir World Fire Web: 22 ground

stations Gridded product: 0.5°x0.5°

• #pixels on fire, #cloudy pixels, total #pixels observed

WFW:10 day

composite

AVHRR Coverage Limitations

Coverage limited by:1. Polar Orbit2. Ground Station

Data Submission 3. Clouds

WFW:1 day

coverage

Percentage of days observed in Spring 2001

Accounting for Clouds

ii

ii ct

fF

Threshold Box > 90% cloudy pixels = No Information

Defining the Fraction on Fire (FOF)

fi = # pixels on fireci = # cloudy pixelsti = # total pixels

Average Cloud Cover during Spring 2001

Correlation Scales

Local temporal correlation = persistence of fire patterns

Local spatial correlation = cohesion of fires

Application of Correlation Scales to Daily Fire Data

Correlation analysis supplements daily measurements with weighted information from neighbouring gridboxes, in either space or time.

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Application of Fire Data to CO Emissions

Magnitude of FOF used to partition monthly BB CO budget

Implementing in GEOS-CHEM

Boundary Layer:Difference strongestover source regions

Mid-troposphere:Difference strongestIn outflow (W. Pacific)

CO: Standard Simulation Standard Simulation - Daily Emissions Simulation

CO Sources: Integrating MOPITT and Aircraft

MOPITT Data EvaluationComparing to aircraft CO, GEOS-CHEM CO and fire activity

Forward Model Evaluation of Aircraft ObservationsComparing flight data with GEOS-CHEM fields

Inverse Modeling of CO Sources using MOPITT and aircraft observations

Characterize regional (Asian) emission sources viainversion of combined observational set

Background: TRACE-P and GEOS-CHEM

TRACE-P Feb-Apr. 2001 Characterize evolution and

composition of outflow from Asia

100 E 130 E 160 E 190 E 220 E 250 E 280 E

Longitude

0 N

10 N

20 N

30 N

40 N

50 N

60 N

La

titu

de

DC-8 FlightsP-3B Flights

GEOS-CHEM tagged CO “tag” CO by emission type

(biomass burning, fossil fuel, etc.) and source region using linear OH chemistry

GEOS-CHEM during TRACE-P

GEOS-CHEM UNDERESTIMATES THE OBSERVED CO BY 5-10%

Attributing Source Type to Observations

Multivariable fit to aircraft CO: PCE (C2Cl4) = fossil fuel HCN = biomass burning, biofuel Background term = chemical production

BLACK=OBSERVATIONSRED=FIT

cHCNbPCEaCO ][][][

Observation and Model Location of Source Influenced CO

PCE=FFHCN=BB/BFTOTAL CO MODEL

MISSING A BB/BF SOURCE?

Observation and Model Location of Source Influenced CO

PCE=FF

HCN=BB/BF

MISSING A BB/BF SOURCE?

TOTALCO

MODEL

Use of MOPITT

Integrated Analysis of global troposphere:

MOPITT COGEOS-CHEM

Aircraft COObservations

CMDL CO

Understanding Tropospheric

Processes (CO)

Emission Inventories

MOPITT Averaging Kernels

Gε)xA(xxx atruea ˆRetrieved CO:

Comparing MOPITT and GEOS-CHEM20010324

MOPITT adjusted for bias

…the Next Day (20010325)

MOPITT adjusted for bias

…and the Next (20010326)

MOPITT adjusted for bias

TRACE-P Validation Profiles

Courtesy: Louisa Emmons (NCAR)

BLACK=AIRCRAFT RED=AIRCRAFTxAVG KERNELSBLUE=MOPITT

V2 Retrieval:~20% bias

PreliminaryV3 Retrieval:

Better agreement

EMBARGO’ED!PRELIMINARY FIGURESPROVIDED BY LOUISA EMMONS

CO Source Inversion: Aircraft + Satellite

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A posteriori CO emissions:

CO Inversion from aircraft and satellite observations: Goal: Refine regional (Asian) sources Collaborate with those working on global inversions = a priori Initially: exploit TRACE-P aircraft data and MOPITT

Associated error covariance:

Future Observations of CO: SCIAMACHY

Interests: Compare and evaluate MOPITT and SCIAMACHY observations of CO Exploit SCIAMACHY observations in future CO source inversion