1. Impacts of Assimilation of Air Quality Data from Geostationary Platforms on Air Quality Forecasts
Gregory Carmichael(1), Pablo E. Saide (NCAR), Meng Gao (1), Maryam Abdioskouei (1), Arlindo da Silva (NASA GSFC), R. Brad Pierce (NOAA NESDIS-STAR), David G. Streets (ANL), Jhoon Kim, and Myungje Choi (Yonsei University), Chul H. Song (GIST), Greg Thompson, Trude Eidhammer (NCAR), and SEAC4RS, KORUS-AQ and ORACLES science teams
(1) Center for Global & Regional Environmental Research, University of Iowa, Iowa City, Iowa, USA

2. Models Constrained with Observations Play increasing Important Roles in Research and Applications
Trend toward closer linkages of weather, atmospheric composition, and climate related services
Information needed at higher resolution to address societal needs
Improvements require advances in observing systems and model/assimilation systems

3. Testing the Impact of GOCI AOD Assimilation UIOWA/NCAR WRF-Chem forecasting system
WRF-Chem with MOSAIC aerosols and a Reduced Hydrocarbon chemistry (Pfister et al. JGR 2014), including simplified SOA formation (Hodzic and Jimenez, GMD 2011)
GFS and MACC meteorological and chemical boundary conditions
KORUS-AQ anthro (Jung-Hun Woo) and QFED fire emissions
AOD data assimilation using GSI (Saide et al., ACP 2013). MODIS and GOCI data were assimilated simultaneously every three hours. (Saide et al., GRL, 2014)
Four days of forecasts were available for the outer domain, 2 days for the inner domain
Output available to other users: All species and meteorology along DC-8 flight track on KORUS-AQ repository, full model outputs available by request
Images:

4. GOCI AOD Data Significantly Improves Predictions of AOD (over MODIS-only and no-assimilation)

5. With GOCI AOD assimilation prediction skill is high for AOD (evaluation against AERONET) but surface PM is biased high (all Korean sites))
AOD
PM10
PM2.5

6. Example of AOD assimilation impact during KORUS-AQ
(May 24th flight, Chinese haze over Yellow sea)
Day-3
Day-2
Day-1
Observed AOD (GOCI)
WRF-Chem Day-1

7. DIAL-HSRL vs WRF-Chem May 24th (Extinction [1/km]) (Day-1)

8. AOD comparison May 24th (GOCI V2, DIAL-HSRL, 4STAR, WRF-Chem)

9. AOD comparison May 24th (GOCI V2, DIAL-HSRL, 4STAR, WRF-Chem)

10. In-situ aerosol comparison (May 24th)
Ambient Extinction
AMS mass
Fine aerosol produces less extinction per unit mass than observed, probably related to hygroscopic growth?
Aerosol not observed by AMS (“other PM2.5”) too high in the model?
Emissions too high?
Assigned to fine aerosol size bins, thus removed more slowly than in reality?

11. Aerosol hygroscopic growth [ f(RH) ]
Base model (OA and “other PM2.5” do not take up water)
550nm
550nm
No fine “other PM2.5” and
OA slightly hydrophilic
(κ = 0.14)

12. TOTAL OMI NO2 COLUMNS with Pandora OVERLAID
High Resolution Model Profiles Improve Retrievals
TOTAL OMI NO2 COLUMNS with Pandora OVERLAID
OMI_WRF-Chem: Using 4 km WRF-Chem to calculate the Air Mass Factor instead of GMI (resolution: ~110 km)
OMI_GMI NO2 Standard Product
OMI_WRF-Chem NO2 Product
OMI NO2 vs. Pandora
Pandora 2-hour means co-located to valid daily OMI overpasses
New OMI_WRF-Chem product shows much better agreement with total column measurements from Pandora NO2 (slope near unity)
Streets and Goldberg

13. Emission Inversion method for Rapid Update of Emissions
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 (NASA NNR)
Constrain hourly RIM fire emissions from NASA QFED BB emissions
Performed at 4km resolution, for 2 met boundary conditions (FNL, NARR)
26 Aug flight
Ext [1/Mm]
U of Reno AERONET AOD
Saide et al., GRL 2015a

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

15. Emissions update tested in ORACLES
WRF with aerosol-aware microphysics (Thompson and Eidhammer, JAS 2014, Saide et al. JGR 2016) at 12km resolution
Smoke emissions constrained in near-real time using Saide et al. (GRL 2015) over 6 regions for a 4 day window
12 km
Simulations turning on and off fires to assess aerosol-cloud-radiation interactions
One cycle a day, 3 day forecasts
Surface Smoke Extinction
Number droplet change by smoke
Temp. change due to smoke (~1.5 km)

16. Daily evolution of smoke emissions1) 3) 2) 4)
-Region 6 (Mozambique) it’s the only showing substantial increases in emisions due to the inversion
- Different regions show different trends in the emissions, with decrease for 1 and 3 (Congo), increase in 6 Mozambique, and roughly constant for the rest
Aug
Sept 5) 1 2
Prior
Constrained 3 4 5
Tendency
Aug
Sept

17. AERONET over fire regions
1) Gorongosa
(Mozambique)
Aug
Sept
2) Lubango (Angola) 2 1
Aug
Sept
Acknowledgements to the AERONET Team, specially Brent Holben, Joel Schafer and David Giles

18. HSRL2 data from ER-2 aircraft (Sept 22nd)
PRELIMINARY FIELD DATA (Backscattering)
WRF-AAM Extinction day 1
WRF-AAM AOD
Qualitative AOD comparison
Acknowledgements to the HSRL2 and ER-2 teams, specially Sharon Burton and Rich Ferrare

19. Above Cloud AOD (ACAOD) retrievals
ACAOD retrievals by Meyer et al. (JGR 2015) produced in near-real time during September 2016
MODIS Terra QC1
(Observation)
MODIS Terra QC2
(Observation)
WRF-AAM Day 1
(Forecast)
Acknowledgements to Kerry Meyer and Steven Platnick for the ACAOD data and interpretation Data: ftp://windhoek.nascom.nasa.gov/pub/ridgway/MOD06ACAERO/2016/
ftp://windhoek.nascom.nasa.gov/pub/ridgway/MYD06ACAERO/2016/

20. Models Constrained with Observations Play increasing Important Roles in Research and Applications
Trend toward closer linkages of weather, atmospheric composition, and climate related services
Information needed at higher resolution to address societal needs
Improvements require advances in observing systems and model/assimilation systems
Information from TEMPO, GEMS and others, by themselves (and together with other platforms) will advance a spectrum of research  applications

22. Supporting slides

23. OMI NO2 tropospheric columns at 1
OMI NO2 tropospheric columns at 1.33 km resolution in the Baltimore-Washington metropolitan area
Goldberg et al., 2017; ACPD
OMI NO2 NASA product
OMI_CMAQ NO2 product
OMI_CMAQ scaled NO2 product
OMI_CMAQ scaled & spatial weighted NO2 product
(a)(b): Use CMAQ instead of GMI to calculate the Air Mass Factor (AMF)
(b)(c): Scale CMAQ based on
D-AQ aircraft observations, and then calculate AMF
(c)(d): Spatial weight based on the variability within CMAQ [Kim et al., 2016; GMD]
(d) Is much better than (a) when compared to in situ observations!
Key takeaways:
Model resolution plays a significant role in the calculation of air mass factors (high resolution = better)
Accuracy of model simulation is critical in generating robust satellite observations
If emissions or chemistry are way off, satellite will be unrealistic
Spatial weighting helps satellite match urban-scale variability better

24. Constraining biomass burning emissions for improved prediction skill and assessing smoke-weather interactions
WRF with aerosol-aware microphysics (AAM) (Thompson and Eidhammer, 2014) and WRF-Chem emissions.
Inversion based on Saide et al. (GRL 2015b) using WRF tracers (no adjoint, no ensembles)
Plans for using it operationally for the NASA ORACLES and NOAA FIREX field experiments
Run 2 forecasts needed by the inversion method (with and without feedbacks) so that aerosol feedbacks impacts are also forecasted
Notes:
Figure upper right: regions where scaling factors are applied to biomass burning smoke emissions
Figure at center-right: Time series of emissions on each region showing prior and constrained emissions. Note that scaling of emissions varies through time and region considerably, so scaling all emissions for all times by a single factor would not work
Bottom figure: Shows the improve fitting to observations after re-running with the constrained emissions

25. Improved Forward Models & Assm. Sys. Data Management & Discovery
The Growing Interest in Improving Air Quality Predictions/Services and the Role of Atmospheric Composition in Weather and Climate Applications Offer Great Opportunities for Our Community
Evolving
Obs Systems
Improved Forward Models & Assm. Sys.
Improved Predictions
Requires
Continuous Research
Enhanced
Data Management & Discovery
Systems
Improved
Emissions

26. Technique already Extended to include Assimilation of Surface PM2
Technique already Extended to include Assimilation of Surface PM2.5 Observations enable improved Predictions
Haze JAN 2013
R no DA 0.67
R DA
PM2.5 Observations are growing rapidly
Impacts of assimilating surface PM2.5
Mean over all surface observation
Health impacts from winter haze; e.g., Gao et al., Science Tot. Env., (2015)

27. Forecasting system
Move from WRF-Chem to WRF with aerosol-aware microphysics (AAM) (Thompson and Eidhammer, 2014) including primary emissions (anthropogenic, biomass burning, sea-salt, dust) (Saide et al., JGR 2016)
Perform an inversion based on Saide et al. (GRL 2015b) instead of data assimilation for initial conditions.
- Go from over 100 aerosol species to only 2 but still have a treatment of aerosol-cloud-radiation interactions

28. Forecasting system
WRF with aerosol-aware microphysics (AAM) (Thompson and Eidhammer, 2014, Saide et al. 2016) at 12km resolution
Smoke emissions constrained in near-real time using Saide et al. (GRL 2015) over 6 regions for 8 hour intervals
Simulations turning on and off fires to assess aerosol-cloud-radiation interactions
Tracers for 15 days of smoke emissions used for assessing age
12 km
VIIRS Fires Aug-Sept 2016
FIRMS Web Fire Mapper

29. Some of the forecasting products

30. Some of the forecasting products
Smoke tracer
Cross-sections
Above Cloud AOD
Temperature change due to smoke
Mean Age

31. In-situ comparison
DLH RH
Black Carbon CO