1. A Proposal - Assimilation of Himawari Aerosol Information for Air Quality Forecasting Michael Hewson CSC PhD Research Fellow m.hewson @ uq.edu.au Remote Sensing Research Centre

2. Hazelwood Open Pit Coal Mine Fire - February 2014 2 Image: www. heraldsun. com. au

3. Hazelwood Open Pit Coal Mine Fire - February 2014 3

4. Recommendation 7: The State equip itself to undertake rapid air quality monitoring in any location in Victoria, to: collect all relevant data, including data on PM 2.5, carbon monoxide and ozone; and ensure this data is used to inform decision-making within 24 hours of the incident occurring.

5. MISR Daily Level-3 Data 0.5 x 0.5 Degree Aerosol Product (MIL3DAE) Image: http://gdata1.sci.gsfc.nasa.gov

7. http://www.icare.univ-lille1.fr/parasol POLDER Fine Mode AOT 865nm - 9 Oct 13

8. Image: http://gdata1.sci.gsfc.nasa.gov/daac-bin/G3/gui.cgi?instance_id=omi. OMI/Aura L2G AOT @ 342.5nm 18 Jun 15

9. Analyses and visualizations used on this page were produced with the Giovanni online data system, developed and maintained by the NASA GES DISC.

10. Paleoclimatology - OMI Seasonal average AOD 1979-2000

11. MODIS Aqua – RGB and AOD – 0340z 23Jul10 The data used in this slide were acquired as part of the NASA's Earth-Sun System Division and archived and distributed by the MODIS Adaptive Processing System (MODAPS).

12. 22-24 September 2009 MODIS Aqua AOD 550nm and “Deep Blue” AOD 550nm Analyses and visualizations used on this page were produced with the Giovanni online data system, developed and maintained by the NASA GES DISC.

14. VIIRS data provided by the NOAA CLASS system.

15. Image: http://rammb.cira.colostate.edu

16. 16 Sydney Opera House Australia 23 September 2009 Case Study - Australian dust storm 21 – 23 Sep 2009 - WRF-Chem µg/kg-air

17. WRF-Chem USA PM 2.5 /O 3 Concentration 17 th June 2005 (using National Emissions Inventory) © Chris Tessum - U of Minnesota

18. Assimilating MODIS AOD into WRF-Chem Source: Dr Zhiquan Liu NCAR, WRF Users Forum 11 July 2011 18

19. WRF-Chem Air Quality Forecasting Workflow Prepare aerosol emissions from global data sets: - Background aerosol - GOCART (dust, sea salt, DMS) - Anthropogenic aerosol- RETRO 0.5°, EDGAR 0.1°, GOCART 1° - Biogenic aerosol- MEGAN 1km - Biomass burning- MODIS (NOAA or NCAR store) Assimilate “deep blue” AOD via NCAR Data Assimilation Research Test-bed (DART) Himawari radiances PM 25/10 SO 2 /O 3 6 2 Run WRF-WPS 4 km resolution domain(s) – geography and apportion meteorology to grids. 1 3 5 WRF-Chem first and restart runs at each DART assimilation. Initial WRF run (no chemistry) meteorology and chemistry initial and boundary conditions. WPS Chem 2 4 19 NASA “deep blue” AOD enhancement 7

20. Proposed Project Goals: 20 Himawari-8/9 WRF-Chem/Himawari proof-of-concept: – “bright surface” aerosol enhancement; – hazard forecasting (volcanic ash, dust, smoke); and – AQ standards compliance – (O 3, SO 2, CO 2, CH 4 ?). Develop air quality products for AU: – Convert AOD/Ae to PM 10/25 ; and – public health ultra-fine particulate parametization; Further possibilities – AU aerosol monitoring: – emissions inventory; and – paleoclimate/geoengineering aerosol transport.

21. 21 2Dec09 – Cu event – Brisbane Australia 1) Doppler radar rain intensity 2) WRF-Chem anthropogenic SO 2 3) Cross-variogram 24Nov11 – St/Sc event Aerosol indirect effects

22. A Proposal - Assimilation of Himawari Aerosol Information for Air Quality Forecasting Michael Hewson CSC PhD Research Fellow m.hewson @ uq.edu.au Remote Sensing Research Centre