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Colette Heald Advisor: Daniel Jacob

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. Satellite Observation of Fires and Biomass Burning Emission Inventories. MOTIVATION

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Colette Heald Advisor: Daniel Jacob

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

  2. 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

  3. Concept Constrain Total Emissions Add Temporal Variability Annual BB CO Emission Budget (Logan & Yevich) Observed Daily Satellite FireCounts Monthly BB CO Emission Budget (Martin & Duncan) Daily FireCounts (after correct for coverage) Daily BB CO Emission Budget

  4. AVHRR Fire Data 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 Why AVHRR? • Need: global, daily coverage during Spring 2001 • ATSR, MODIS and TRMM not suitable WFW: 10 day composite

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

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

  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 CO: Standard Simulation Standard Simulation - Daily Emissions Simulation Boundary Layer: Difference strongest over source regions Mid-troposphere: Difference strongest In outflow (W. Pacific)

  11. CO Sources: Integrating MOPITT and Aircraft Forward Model Evaluation of Aircraft Observations Comparing flight data with GEOS-CHEM fields MOPITT Data Evaluation Comparing to aircraft CO, GEOS-CHEM CO and fire activity 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 GEOS-CHEM tagged CO • “tag” CO by emission type (biomass burning, fossil fuel, etc.) and source region using linear OH chemistry TRACE-P • Feb-Apr. 2001 • Characterize evolution and composition of outflow from Asia

  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 (C2Cl4) = fossil fuel • HCN = biomass burning, biofuel • Background term = chemical production BLACK=OBSERVATIONS RED=FIT

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

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

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

  18. MOPITT Averaging Kernels Retrieved CO:

  19. Comparing MOPITT and GEOS-CHEM20010324 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 V2 Retrieval: ~20% bias EMBARGO’ED! PRELIMINARY FIGURES PROVIDED BY LOUISA EMMONS Preliminary V3 Retrieval: Better agreement BLACK=AIRCRAFT RED=AIRCRAFTxAVG KERNELS BLUE=MOPITT Courtesy: Louisa Emmons (NCAR)

  23. CO Source Inversion: Aircraft + Satellite 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 A posteriori CO emissions: 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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