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Preliminary results of a global aerosol assimilation system

Preliminary results of a global aerosol assimilation system. N.A.J. Schutgens 1 , T. Miyoshi 2 , T. Takemura 3 , T. Nakajima 1. 1: CCSR, Tokyo University 2: NPD, JMA 3: RIAM, Kyushu University. Contents. Purpose: improve prediction of global aerosol load for GOSAT mission

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Preliminary results of a global aerosol assimilation system

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  1. Preliminary results of a global aerosol assimilation system N.A.J. Schutgens1, T. Miyoshi2, T. Takemura3, T. Nakajima1 1: CCSR, Tokyo University 2: NPD, JMA 3: RIAM, Kyushu University

  2. Contents • Purpose: improve prediction of global aerosol load for GOSAT mission • SPRINTARS: global aerosol transport model • Local Ensemble Transform Kalman filter • Case studies

  3. What is SPRINTARS ? • Spectral Radiation-Transport model for Aerosol Species (Takemura et al. 2000) on top of the MIROC AGCM (t42, 20 -levels) • Five Aerosol species: • Black carbon • Carbonaceous aerosol • Dust • Sea salt • Sulfate • Sources (emission) & Transport & Sinks (wet & dry deposition, gravitational settling) • It is commonly believed that aerosol emission is the biggest unknown

  4. SPRINTARS vs observations Observations come from the SKYNET observing network In South-East Asia.

  5. Local Ensemble Transform Kalman Filter • Hunt et al. 2005, Miyoshi & Yamane 2007 • Local: KF applied to subsets of global grid • Ensemble: model error (covariance) represented by ensemble of model calculations • Transform: • Highly efficient for parallel computing! • Aerosol specific ensemble: emissions • Aerosol specific observations • AOD, LIDAR backscatter at various wavelengths • Space-borne / ground-based • Observing System Simulation Experiments

  6. Free run Assimilated run Ensemble Kalman filter • In KF, model (error) and observation (error) are combined to an improved guess at the truth • In EnKF, model error is represented by an ensemble of model calculations

  7. Creating the ensemble Each ensemble member has its own emission inventory for carbon and sulfate.

  8. Case 1: AERONET, July 2005 • Experiment with simulated observations from a realistic AERONET to validate the assimilated fields (perfect model experiment) • Experiment with real QA lvl 2 AERONET AOD at 675 nm

  9. Simulated AERONET • 131 AERONET sites • July 2005 • Diurnal cycle (SZA < 60o) • Discard obs for high RH • Shown cases use 1 - 30 obs within 1000 km/day

  10. Real AERONET

  11. Case 2: GOSAT, March, 2005 • Perfect model experiments for polar-orbiting sun-synchronous satellite • Cloudiness • Night time • Sun glitter • Recurrence time: 3 days One day of GOSAT

  12. AOD at individual stations

  13. Average AOD March 19 & 20

  14. Summmary • Succesfully implemented an assimilation system for a global aerosol transport model • Uncertainty due to emission inventories can be much reduced • Even sparse observation networks are useful, provided they are well situated (AERONET) • However, recurrent observations seem required, posing difficulties for observations from polar-orbiting satellites

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