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Aerosol Data Assimilation in GEOS-5

Aerosol Data Assimilation in GEOS-5. Arlindo da Silva (1) Arlindo.daSilva@nasa.gov Peter Colarco (2), Dirceu Herdies (6) , Ricardo Todling (1) , Anton Darmenov (1,5) , Virginie Buchard-Marchant (1,3) , Cynthia Randles (2,3) , Ravi Govindaradju (1,4)

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Aerosol Data Assimilation in GEOS-5

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  1. Aerosol Data Assimilation in GEOS-5 Arlindo da Silva(1) Arlindo.daSilva@nasa.gov Peter Colarco(2), Dirceu Herdies(6), Ricardo Todling(1), Anton Darmenov(1,5), Virginie Buchard-Marchant(1,3), Cynthia Randles(2,3), Ravi Govindaradju(1,4) Global Modeling and Assimilation Office, NASA/GSFC Atmospheric Chemistry and Dynamics Branch, NASA/GSFC GESTAR Science Applications International Corp. Earth Resource Technology CPTEC/INPE, Brazil JCSDA 9th Workshop College Park, MD, March 29, 2011

  2. Outline • Aerosols in GEOS-5 • QFED NRT Biomass Burning Emissions • Aerosol Optical Depth Assimilation • Aerosol impact on GSI radiances • Concluding Remarks

  3. GEOS-5 Earth-System Model ESMF From Weather to Seasonal to Decadal Time scales.

  4. GOCART Component Dust radius Seasalt radius Black Carbon hydrophobic hydrophilic Organic Carbon hydrophobic hydrophilic Sulfate Mass • Goddard Chemistry, Aerosol, Radiation, and Transport Model [Chin et al. 2002] • Sources and sinks for 5 aerosol species • Convective and large scale wet removal • Dry deposition (and sedimentation for dust and sea salt) • Optics based primarily on OPAC

  5. GEOS-5/GOCART Forecasts • Global 5-day chemical forecasts customized for each campaign • O3, aerosols, CO, CO2, SO2 • Resolution: Nomally 25 km • Driven by real-time biomass emissions from MODIS • MODIS aerosol assimilation to become operational by Summer 2011. CO Smoke SO4 http://gmao.gsfc.nasa.gov/projects/glopac/

  6. QFED: Quick Fire Emission Dataset • Near real time estimates based on MODIS Fire products (AQUA/TERRA) • Started as an attempt to use diurnal fire counts to better distribute in time the monthly GFED emissions • Current focus on MODIS to be followed by • Geostationary • VIIRS • Plume Rise model (Freitas et al.) • Driven by GEOS-5 meteorology • Under tuning/validation

  7. QFED v2 – Fire Radiative Power • Assumes simple linear relationship for each biome • E(x,y,t) = cbiome * FRP(x,y,t) • Only 4 global constants to estimate (boreal, tropical fires, savannah, grasslands) • Spatial structure determined by satellite data • Accounts for pixels obscured by clouds • In principle, tuning parameters can be determined using inverse modeling techniques.

  8. QFED 2.1: GFED-based Calibration GFED and QFED v2.1 led to anemic BB AOT in GEOS-5

  9. QFED 2.2: MODIS AOD Calibration MODIS AOD 8 9 G5 QFED 2.2 G5 QFED 2.1 aBB ~ 2.5 aAN ~ 0.4 aBB ~ 1.8 aAN ~ 1.3 10 aBB ~2.2 aAN ~ 2.6 Tuning relative to QFED 2.1

  10. Aerosol Data Assimilation • Focus on NASA EOS instruments • Global, high resolution (1/4 deg) AOD analysis • 3D increments by means of Lagrangian Displacement Ensembles (LDE) • Simultaneous estimates of background bias (Dee and da Silva 1998) • Adaptive Statistical Quality Control (Dee et al. 1999): • State dependent (adapts to the error of the day) • Background and Buddy checks based on log-transformed AOD innovation • Error covariance models (Dee and da Silva 1999): • Innovation based • Maximum likelihood

  11. Analysis Variable Quality control and Data Assimilation methodologies assumes Gaussian statistics AOD (and errors) is not normally distributed Log-transformed AOD has better statistical properties: This 0.01 factor is determined from goodness-of-fit considerations Log ( 0.01 + AOD)

  12. MODIS/TERRA Ocean AOD LAOD AOD O-F LAOD O-F

  13. AERONET-MODIS/MISR Joint PDF MODIS-Terra (Ocean) MISR (Ocean+Land)

  14. Neural Net forAOD Empirical Retrievals • Ocean Predictors • Multi-channel • TOA Reflectances • Retrieved AOD • Angles • Glint • Solar • Sensor • Cloud fraction (<85%) • Wind speed • Target: AERONET • Log(AOD+0.01) • Land Predictors • Multi-channel • TOA Reflectances • Retrieved AOD • Angles • Solar • Sensor • Cloud fraction (<85%) • Climatological albedo • < 0.25 • Target: AERONET • Log(AOD+0.01)

  15. OceanNeural Net Retrievals (Terra) Original NN Retrieved

  16. MODIS Aqua MODIS Aqua Annual Mean 2008 Neural Net Neural Net

  17. Bonanza Creek Aerosol Data Assimilation GEOS-5 AOD With MODIS Assimilation GEOS-5 AOD Without MODIS Assimilation Bonanza Creek Cabo Verde Comparison against independent AERONET ground stations.

  18. Better consistency between MODIS on AQUA/TERRA. MISR has not been assimilated

  19. Aerosols in GSI • CRTM allows for the inclusion of (GOCART) aerosols • The GEOS-5 GOCART aerosol species have been introduced as state variables in GSI • No aerosol increments for now • Aerosol effects included in the observation operators for AIRS, HIRS, IASI, etc • NCEP/STAR implementing AOD analysis in GSI

  20. Aerosol Contamination of GSI Radiances Control Experiment Aerosol Experiment Aerosols fully interactive in GEOS-5 No AOD assimilation GSI observation operators: 15 GOCART species Concentration Effective radius Optical parameters internally determined by CRTM • Aerosols fully interactive in GEOS-5 • No AOD assimilation • Standard GSI global analysis • ARCTAS Period • Summer 2008 • Resolution: • ½ degree

  21. AOD Validation MISR MODIS GEOS-5 GEOS-5 GEOS-5 overestimates dust

  22. Dust Distribution

  23. Temperature AnalysisDT = Taero - Tcontrol

  24. Temperature AnalysisDT = Taero - Tcontrol

  25. AIRS Q/C Control Aero effects About 3% more AIRS observations are accepted

  26. Innovation Statistics Neutral Impact on short-term forecast

  27. Concluding Remarks • GEOS-5 produces twice daily aerosols forecasts • AOD assimilation scheduled for parallel testing this summer • The GOCART/GSI interface is under active development in coordination with NCEP. • GOGART ESMF Grid Component has been implemented in GFS • Facilitates sharing of other aerosol modeling/data assimilation capabilities

  28. http://portal.nccs.nasa.gov/cgi-yotc/e540_fp_2d_chem.cgi

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