Assimilation of Satellite Derived Aerosol Optical Depth

1. Udaysankar Nair1, Sundar A. Christopher1,2 1 Earth System Science Center, University of Alabama in Huntsville 2 Department of Atmospheric Science, University of Alabama in Huntsville Assimilation of Satellite Derived Aerosol Optical Depth

2. Prior research Numerical modeling of Saharan Dust storm, Use of GOES derived AOD Long range transport of smoke, Central American biomass Long range transport of smoke, Georgia fires Future plans, Inverse modeling of Ammonia and fire emissions Outline

3. Used Regional Atmospheric Modeling System (RAMS) to simulate passage of Saharan Dust Storm over Puerto Rico during the PRIDE field experiment (Wang et al. 2004) Explore radiative impacts of dust aerosols Utilized hourly observation of GOES retrieved AOT to initialize and nudge the lateral boundaries Numerical simulation of Saharan Dust Storm

4. Utilized vertical distribution from aircraft measurements Numerical simulation of Saharan Dust Storm

5. In the case of episodic events, such as forest fires and biomass burning in Central America, simple smoke transport modeling is adequate for predicting air quality category RAMS incorporating satellite derived smoke emissions, used to simulate long range transport of smoke from Central America Used thirty minute, Fire Locating and Modeling of Burning Emissions (FLAMBE) Numerical modeling of smoke transport from Central America

6. Fire emissions for the period 20 April to 21 May was utilized. Numerical modeling of smoke transport from Central America

7. Fire emissions for the period 20 April to 21 May was utilized Specification of injection height required Numerical modeling of smoke transport from Central America

8. Numerical modeling of smoke transport from Central America

9. Numerical modeling of smoke transport from Central America

10. Numerical modeling of smoke transport from Central America

11. Smoke emissions for 23-25th of May 2007 Numerical modeling of Georgia fires

12. Smoke emissions underestimated by 70% MODIS RAMS RATIO a b c d e f g h i Numerical modeling of Georgia fires

13. Surface concentrations underestimated during day time, vertical mixing? Injection height? Numerical modeling of Georgia fires

14. Apply to Georgia fire simulations Assimilate 30 minute GOES AOD/ MODIS AOD, use multipliers to surface emissions, update in a manner similar to state variables Ammonia emissions from animal agriculture Future work, Inverse modeling approach using Ensemble Kalman filtering

15. Use AOD as a constraint Assimilate 30 minute GOES AOD/ MODIS AOD Future work, Inverse modeling approach using Ensemble Kalman filtering