Aerosol optical properties assimilation from low earth orbiting and geostationary satellites: Impacts on regional forecasts

Transcription

1 Aerosol optical properties assimilation from low earth orbiting and geostationary satellites: Impacts on regional forecasts Pablo Saide, Greg Carmichael, University of Iowa, Center for Global and Regional Environmental Research Contributions from: Jhoon Kim, Myungje Choi (Yonsei University), Chul H. Song (Gwangju Inst. Sci. & Tech. ), Yafang Cheng (MPI), David Peterson (NRC), Arlindo da Silva, Bruce Anderson, Luke D. Ziemba, Glenn Diskin, Glen Sachse, Johnathan Hair, Carolyn Butler, Marta Fenn (NASA), Jose L. Jimenez, Pedro Campuzano Jost, O. Brian Toon (U. of Colorado Boulder), Jack Dibb (UNH), Robert Yokelson (U of Montana), Edward Hyer (NRL) 1

2 Outline Assimilation of next generation geostationary aerosol optical depth retrievals and impacts to air quality predictions Multiplatform inversion of the 2013 Rim Fire smoke emissions during SEAC4RS campaign 2

3 Modeling pollution episodes over Korea April 26 May , 3 types of events: Dust Anthropogenic Biomass burning 12km 36km WRF Chem v3.5 CBMZ, 8bin MOSAIC, anthropogenic (MIX), biomass burning (QFED2), dust (GOCART), MACC boundary conditions DUST ANTHRO SMOKE Model generally capture episodes but underestimate loads 3

4 Assimilation experiments Objectives: Assess performance of assimilating GOCI AOD into a system already assimilating MODIS AOD System: WRF Chem GSI for MOSAIC sectional aerosol model (Saide et al., ACP 2013) allows assimilation of multiple data GOCI AOD Aug 27 29, 2012 GSI AOD assimilation every 3 hours, MODIS only, MODIS+GOCI. GOCI assimilated is only over ocean WRF Chem NO Assim WRF Chem MODIS+GOCI Assim Lee et al., RSE 2010, Park et al., ACP

5 Statistics per station AERONET DRAGON network available Substantial Error and bias reduction when including GOCI Reasons: GOCI fills gaps of MODIS (clouds, ocean glint) BAD GOOD BAD GOOD 5

6 Time series statistics Important Air quality forecasting improvements when adding GOCI data to assimilation 6 DUST Anthro Anthro SMOKE Anthro

7 Outline Assimilation of next generation geostationary aerosol optical depth retrievals and impacts to air quality predictions Multiplatform inversion of the 2013 Rim Fire smoke emissions during SEAC4RS campaign 7

8 WRF Chem Forecasting for SEAC4RS field campaign Objectives: provide guidance for flight planning and evaluate model in near real time WRF Chem v3.5, 12km res, CBMZ, 4bin MOSAIC, AOD assimilation (NRL product) every 3 hours, 1 cycle a day

9 RIM fire during SEAC4RS AOD Aug 28 th 00 UTC August 27th August 26th

10 Emission Inversion method WRF- Chem Full Chemistry Hourly Tracers OBS Sensitivities Tracer Sensitivities Simultaneously improve the model fit to various measurements: SEAC4RS in situ (OA, CO, extinction) and remote sensing (DIAL HSRL Extinction) from 2 flights AERONET AOD (1, 2, 3, 4) Ground based CO (1, 5, 6) Satellite AOD Constrain hourly RIM fire emissions Performed at 4km resolution, for 2 met boundary conditions (FNL, NARR) Variational Inversion Correction factors 12km 4km Observation s Airborn e Groun d Satellit e

11 Inversion Results Total constrained emission are factor ~3 higher FNL and NARR inversions generally consistent Correction factors Emissions

12 Inversion Results Flight+Ground inversion shows higher correction factors than satellite Nocturnal emissions up to 26 times higher Correction factors Emissions Counts Diurnal Cycle FRP

13 Inversion Results Greater day to day variability in constrained emissions This variability correlates to daily area burned by airborne infrared obs Correction factors Emissions Initial Constr. FNL Constr. NARR

14 Impacts of constrained emissions Maximum changes shown comparing simulation with initial and constrained emissions Impacts can be substantial 14

15 Conclusions Assimilating next generation geostationary aerosol optical depth retrievals can improve air quality predictions Multiple datasets can be used to constrain fire emissions to better characterize fire characteristics and improve air quality predictions Thanks! Questions? pablo 15