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Developing Daily Biomass Burning Inventories from Satellite Observations and MOPITT Observations of CO during TRACE P Colette Heald Advisor: Daniel Jacob.

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Presentation on theme: "Developing Daily Biomass Burning Inventories from Satellite Observations and MOPITT Observations of CO during TRACE P Colette Heald Advisor: Daniel Jacob."— Presentation transcript:

1 Developing Daily Biomass Burning Inventories from Satellite Observations and MOPITT Observations of CO during TRACE P Colette Heald Advisor: Daniel Jacob IDS Meeting: Duke University April 26, 2002

2 Satellite Observation of Fires and 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

3 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 EmissionsAdd Temporal Variability

4 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

5 AVHRR Coverage Limitations Coverage limited by: 1. Polar Orbit 2. Ground Station Data Submission 3. Clouds WFW: 1 day coverage Percentage of days observed in Spring 2001

6 Accounting for Clouds Threshold Box > 90% cloudy pixels = No Information Defining the Fraction on Fire (FOF) fi = # pixels on fire ci = # cloudy pixels ti = # total pixels Average Cloud Cover during Spring 2001

7 Correlation Scales Local temporal correlation = persistence of fire patterns Local spatial correlation = cohesion of fires

8 Application of Correlation Scales to Daily Fire Data Correlation analysis supplements daily measurements with weighted information from neighbouring gridboxes, in either space or time.

9 Application of Fire Data to CO Emissions Magnitude of FOF used to partition monthly BB CO budget

10 Implementing in GEOS-CHEM Boundary Layer: Difference strongest over source regions Mid-troposphere: Difference strongest In outflow (W. Pacific) CO: Standard SimulationStandard Simulation - Daily Emissions Simulation

11 CO Sources: Integrating MOPITT and Aircraft MOPITT Data Evaluation Comparing to aircraft CO, GEOS-CHEM CO and fire activity Forward Model Evaluation of Aircraft Observations Comparing flight data with GEOS-CHEM fields Inverse Modeling of CO Sources using MOPITT and aircraft observations Characterize regional (Asian) emission sources via inversion of combined observational set

12 Background: TRACE-P and GEOS-CHEM TRACE-P Feb-Apr. 2001 Characterize evolution and composition of outflow from Asia GEOS-CHEM tagged CO “tag” CO by emission type (biomass burning, fossil fuel, etc.) and source region using linear OH chemistry

13 GEOS-CHEM during TRACE-P GEOS-CHEM UNDERESTIMATES THE OBSERVED CO BY 5-10%

14 Attributing Source Type to Observations Multivariable fit to aircraft CO: PCE (C 2 Cl 4 ) = fossil fuel HCN = biomass burning, biofuel Background term = chemical production BLACK=OBSERVATIONS RED=FIT

15 Observation and Model Location of Source Influenced CO PCE=FFHCN=BB/BFTOTAL COMODEL MISSING A BB/BF SOURCE?

16 Observation and Model Location of Source Influenced CO PCE= FF HCN= BB/BF MISSING A BB/BF SOURCE? TOTAL CO MODEL

17 Use of MOPITT Integrated Analysis of global troposphere: MOPITT CO GEOS-CHEM Aircraft CO Observations CMDL CO Understanding Tropospheric Processes (CO) Emission Inventories

18 MOPITT Averaging Kernels Retrieved CO:

19 Comparing MOPITT and GEOS-CHEM 20010324 MOPITT adjusted for bias

20 …the Next Day (20010325) MOPITT adjusted for bias

21 …and the Next (20010326) MOPITT adjusted for bias

22 TRACE-P Validation Profiles Courtesy: Louisa Emmons (NCAR) BLACK=AIRCRAFT RED=AIRCRAFTxAVG KERNELS BLUE=MOPITT V2 Retrieval: ~20% bias Preliminary V3 Retrieval: Better agreement EMBARGO’ED! PRELIMINARY FIGURES PROVIDED BY LOUISA EMMONS

23 CO Source Inversion: Aircraft + Satellite 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:

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

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