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

Outline  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

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

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

Numerical modeling of smoke transport from Central America 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

Numerical modeling of Georgia fires Smoke emissions for th of May 2007

Numerical modeling of Georgia fires Smoke emissions underestimated by 70% ba c dfe ih g MODIS RAMS RATIO

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

Future work, Inverse modeling approach using Ensemble Kalman filtering 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 Use AOD as a constraint Assimilate 30 minute GOES AOD/ MODIS AOD East N.C.UrbanEuropeArcticAtlantic Case ICase IIUKUSAverageMarine Continent al SO NO Cl NH