Biomass Burning Emissions from GOES-R ABI --Risk Reduction Presented by Shobha Kondragunta

1. Biomass Burning Emissions from GOES-R ABI --Risk Reduction Presented byShobhaKondragunta NOAA/NESDIS/STAR Team Members: Xiaoyang Zhang

2. Project Objectives • Develop biomass burning emissions algorithm for GOES-R Advanced Baseline Imager (ABI) • Adapt operational GOES algorithm for GOES-R ABI • Explore the development and implementation of an alternate Fire Radiative Power (FRP) based algorithm that is less dependent on ancillary datasets

3. GOES Emissions Algorithm • Conventional approach based on Seiler-Crutzen algorithm • Based on burned area, available fuel loading, combustion efficiency, and emissions factors Emissions (g) = Burned area (ha)*fuel load (kgC/ha)*emissions factors (g/kgC)* fuel consumed (%) • Inputs • MODIS Vegetation Property-based Fuel System (MVPFS) (NASA MODIS) – NESDIS product • Fire location and size (NOAA GOES) – NESDIS product • Fuel moisture category factor (NOAA AVHRR) – NESDIS product • Emissions factors - Literature • Outputs • PM2.5, CO, NOx, NMHC, etc. emissions in tons/hour in near real time Zhang, X and S. Kondragunta, Estimating forest biomass in the USA using generalized allometric models and MODIS land products, Geophysical Research Letter, 33, L09402, doi:10.1029/2006GL025879, 2006 Zhang et al., Near real time biomass burning PM2.5 emissions across CONUS using multiple satellites, Atmospheric Environment, 2008 Zhang and Kondragunta, Temporal and spatial variability in biomass burning area across the USA using the GOES fire product, Remote Sensing of Environment, 2008

4. Biomass Burning Emission Algorithm -–Fire Radiative Power FRP = AσT4 • Fire Radiative Power (FRP) • FRP can also be empirically derived from 3.9 um radiance • Fire Radiative Energy • Biomass combusted • Emissions • σ -- 5.67x10-8 Js-1K-4 • A – area bruned • T – fire temperature • -- 0.368±0.015 kg/MJ • Lh – radiance at 3.9 um • Lbk – background radiance • EF – emissions factors • a -- constant BC = FRE*β E = BC*EF

5. Biomass Combustion Rate (β)--Determined from GOES FRE and Biomass Combusted in TM Burn Scars Each pair of sample indicates the GOES FRE and burn- severity-based biomass combustions in each burn scar detected from TM imagery. Thus the relationship between FRE and biomass combustion is established to determine the rate of biomass combustion for the FRE released. The  value is 0.368±0.015 kg/MJ (Wooster et al., 2005)

6. Reconstruction of Diurnal Pattern in GOES fire size Simulating diurnal fire size for individual GOES fire pixels

7. Inter-comparison of Biomass Combustions: Seiler-Crutzen model vs. FRP model

8. Algorithm Testing: FRE Derived from MET 09 SEVERI data

9. Algorithm Testing: Hourly Biomass Burning Aerosols for Sept. 16, 2009 (animation)

10. Algorithm Testing: Burning Emissions of Aerosols Calculated from FRP for Sept. 15-30, 2009

11. Validation Using Fire Proxy Data--Fire proxy data are generated by CIRA 26 October 2007, California 23 October 2007, California 24 April; 2004, Central America 05 November 2008, Arkansas

12. Validation Using Fire Proxy Data--Biomass Burning Emissions • The biomass burning emissions calculated from FRP provided in the proxy data are taken as the truth. • The emissions estimated from the fire properties detected by WF_ABBA are the model outputs. • The plot displays hourly PM2.5 emissions in the four proxy fire events. The model underestimates biomass burning emissions for larger fires because of the saturation. • The overall accuracy is 87.6%

13. Comparison of GOES Emissions with EPA Estimates in 2005

14. Comparison of GOES Emissions with EPA Estimates in 2005 Biomass burning emissions for 2005 are from EPA (Bluesky method). This dataset is compared with GOES estimates using FRP and Seiler-Crutzen algorithms, separately. EPA emissions are higher for large fire events.

15. Interactions with AWG • Biomass burning emissions product was originally part of option 1 AWG product but got moved to risk reduction • Code written to comply with AWG standards • Algorithm Design Review completed • Draft ATBD written

16. Summary • Biomass burning emissions derived from Seiler-Crutzen and FRP algorithms are similar • FRP algorithm is simple to implement and does not depend on ancillary datasets • Accuracy of emissions product depends on the accuracy of fire products (primarily fire size). • The quality/accuracy of the biomass burning emissions product is strongly dependent on WF_ABBA fire detections. • Validation of biomass burning emissions is difficult due to lack of truth datasets. • Draft version of algorithm ATBD is complete. • Validation plan need to be developed.

17. Future Work • Additional proxy data with realistic fires needed to test the algorithms further. • Multiple satellites that are currently operational • Simulated fires over different biomass type • Product currently is satellite specific. There is a need to combine GOES-E and GOES-W and provide one emissions product for the whole geographical coverage • Will require additional analysis and coding • Interact with fire team to improve product accuracy and test the impact on emissions product. • Develop a validation plan for the algorithm/product. • Compare GOES/SEVIRI/MTSAT FRP with MODIS FRP on global scale • Acquire additional truth datasets for validation