Modeling of biomass burning with the nasa geos 5 modeling and data assimilation system
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source: NASA, AFP. Modeling of Biomass burning with the NASA GEOS-5 modeling and data assimilation system. Anton Darmenov , Arlindo da Silva GMAO Research Meeting, 31 March 2011. Outline. Biomass burning emissions Background The QFED v2.1 fire emissions inventory Fire weather index

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Modeling of Biomass burning with the NASA GEOS-5 modeling and data assimilation system

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Modeling of biomass burning with the nasa geos 5 modeling and data assimilation system

source: NASA, AFP

Modeling of Biomass burningwith the NASA GEOS-5 modeling and data assimilation system

Anton Darmenov, ArlindodaSilva

GMAO Research Meeting, 31 March 2011


Outline

Outline

  • Biomass burning emissions

    • Background

    • The QFED v2.1 fire emissions inventory

    • Fire weather index

  • The Russian fires of 2010 – a case study

    • Performance of the coupled GEOS5/GOCART system

    • Performance of the GAAS aerosol assimilation system

    • Improving the biomass burning emissions in GEOS5


Quantifying biomass burning emissions

Quantifying biomass burning emissions

  • Bottom-up approach

    Ex = EFx M

    Ex – emission load of species x

    EFx – emission factor for species x for the specific vegetation type/biome

    M – biomass burned

    M = A * B * β

    A– area burned

    B– biomass load/density

    β– combustion completeness / fraction of available fuel burned

  • Top-down approach

    • Energy released per unit dry mass is 16-22 MJ/kg

      Etot = Erad + Econv + Econd + Evapor

      Erad ~ Etot

    • Remote sensing Fire Radiative Power (FRP)

      pk– fractional area of the k-th fire component

      Tk– temperature of the k-th fire component

      FRPMIR ~ FRPtrue


The quick fire emission dataset

The Quick Fire Emission Dataset

  • QFED v2.1

    • Utilizes FRPs from MODIS Terra and Aqua

    • Calibrated against GFED v2

    • Provides daily emissions of CO, CO2, SO2, OC, BC and PM2.5 at a ¼ degree resolution

QFED uses the IGBP vegetation map and four types of biomes – tropical and extra-tropical forests, savanna and grassland.


Qfed calibration

QFED calibration

The independent calibration of MODIS Terra and Aqua FRPs provides redundancy in case of data loss from one of the satellites.


Qfed calibration cont

QFED calibration (cont.)

Time series of GFED and QFED global monthly mean CO emissions for the period of 2003-2007.


The extreme summer of 2010

The extreme Summer of 2010

+10 °C

Global Land Surface Temperature Anomalies, July 2010

source: http://earthobservatory.nasa.gov

  • Model the Russian Fires of 2010 with GEOS-5

  • Evaluate the fire emissions

  • Investigate the performance of the newly developed aerosol data assimilation system

  • Air quality applications

Drought and Air Quality, August 2010

source: http://earthobservatory.nasa.gov


Studying the russian fires with geos 5

Studying the Russian fires with GEOS-5

  • Model setup

    • 0.5°x0.625° resolution

    • Intermittent replay using the NASA MERRA reanalysis

    • GOCART aerosol module

  • Fire emissions inventory

    • QFED-v2

    • FRP based fire emissions – MODIS Terra and Aqua

    • GAAS - aerosol assimilation system

  • Validation

    • AOT - MODIS, MISR

    • Visibility – WMO stations


Fire emissions adequacy

Fire emissions adequacy

Far-Eastern Region

Central Region

The fire emissions are likely underestimated in the Central Region.


Validation of modeled aot

Validation of modeled AOT

Observed AOT

GEOS-5 AOT

time averaged AOT: 20 Jul – 20 Aug, 2010


Assimilation of aot

Assimilation of AOT

Observed AOT

time averaged AOT:

20 Jul – 20 Aug, 2010

Modeled AOT


Air quality applications pm2 5

Air quality applications – PM2.5

time averaged PM2.5: 20 Jul – 20 Aug, 2010

Assimilating AOT also improves the surface PM2.5 concentrations.

unhealthy

μg/m3


Pm2 5 and visibility in moscow

PM2.5 and Visibility in Moscow

Moscow – Visibility [km]

GEOS5

WMO

unhealthy

reported VIS < 1000m

The GEOS-5/GAAS visibility is overestimated (higher than the measured values), which suggests that the PM2.5 are underestimated.


Current developments

Current Developments

  • MERRA Fire Weather Index

    • Follows the Canadian Forest Fire Weather Index algorithm

    • Using meteo fields from MERRA

  • Improving the biomass burning emissions in GEOS5/GOCART

    • A diagnostic study of fire emissions strengths on a regional level

    • Use the results in the next version of QFED


Merra fwi

MERRA FWI

  • The FWI components account for the effects of fuel moisture and wind on fire behavior.

  • The six standard components provide numerical ratings of relative potential for wildland fire.

source: CFFWI


Improving the fire emissions in geos 5

Improving the fire emissions in GEOS-5

GEOS-5/GOCART tends to underpredict the AOT in the areas affected by biomass burning.


Addressing the weak fire emissions

Addressing the weak fire emissions

  • Methodology

    • To correct the fire emissions strength we performed multiple linear regression assuming that in the regions affected by fires only the biomass burning and anthropogenic AOT components need adjustment

      AOT = (AOTDU + AOTSS + AOTXX+αBBAOTBB + αANAOTAN


Results from the diagnostic study

Results from the diagnostic study

αBB = 3.4

αAN = 0.01


Results from the diagnostic study1

Results from the diagnostic study

αBB = 2.5

αAN = 0.3


Results from the diagnostic study2

Results from the diagnostic study

αBB = 1.8

αAN = 1.1


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