1. Aerosol from MAIAC algorithm Ian Grant Australian Bureau of Meteorology

2. Non-Meteorological Atmosphere Products Aerosol Total Column Ozone SO 2 Total Column Water Vapour

3. Total Column Ozone Applications Stratospheric dynamics Air quality GOES-R Algorithm Lead by Chris Schmidt (SSEC, Univ of Wisconsin) Adaption to AHI is underway – complete in ~1 year Chris Schmidt is willing to collaborate

4. SO 2 Applications Air quality Volcanic emissions for aviation safety Is there a need beyond LEO products? Algorithms ???

5. Aerosol applications General Assimilation into Earth System models, and validation Near real time For Air Quality, NWP, Chemical Transport Models (MACC etc) Provides aerosol amount and properties: anywhere, anytime Assimilation uses all available inputs with appropriate errors Atmospheric correction (surface reflectance) Dust storms Air Quality, Erosion proxy Smoke Air Quality Initialisation & validation of BoM bushfire smoke dispersion model (Planning prescribed burns) Carbon accounting Effect on fire weather

6. Aerosol algorithms Dense Dark Vegetation (MODIS) Visible-band surface reflectance from shortwave infrared (SWIR) reflectance using predetermined spectral relationships. Fails over bright surfaces – much of inland Australia GOES-R uses this approach Deep Blue (MODIS) – Michael Hewson presentation GEO + LEO (CSIRO for AATSR) – Yi Qin presentation MAIAC – This presentation

7. MAIAC Algorithm MultiAngle Implementation of Atmospheric Correction Simultaneously retrieves AOT, surface reflectance, BRDF model Builds on earlier methods for MODIS, MISR, etc. Lead by Alexei Lyapustin (NASA/GSFC) Operational for MODIS and VIIRS within next year Applied to DSCOVR/EPIC Works for GOES-R Lyapustin is keen to collaborate to apply to Himawari-8

8. Algorithm MAIAC Alexei Lyapustin (GSFC-613) Yujie Wang (UMBC) Sergey Korkin (USRA) August, 2015

9. - Anisotropic surface; - SRC Retrieval - Detection of seasonal and rapid change: -Dynamic LWS classification; -Adaptive and learning system: (store and dynamically update clear-sky TOA reflectance; spectral BRDF; spatial variability metrics; brightness temperature and contrasts @1km) -Aerosol Type Discrimination; -Synergy among WV, CM, aerosol and AC; MAIAC: Building a Complete Physical Model of Atmosphere-Surface (RT) 0.0500.050 0.15 0.1 0.25 0.2 0.3 0500100015002000250030003500100015002000250030003500 0.4 BRF 0.35 BRF n 0.0500.050 0.15 0.1 0.25 0.2 0.3 0.4 0.35 0500 (global aerosol retrievals; low urban bias) DTMAIAC RGBRGB NIR (Dark Target Algorithm is biased over urban surfaces; MAIAC is not)

10. - Anisotropic surface; - Retrieval of Spectral Regression Coefficient: Relation of ρ blue to ρ 2.1 independently for each 1 km 2 -Dynamic Land-Water-Snow classification; -Adaptive and learning system: Store and dynamically update: clear-sky TOA reflectance; spectral BRDF; spatial variability metrics; brightness temperature and contrasts @1km -Aerosol Type Discrimination; -Synergy among water vapour, cloud mask, aerosol and atmospheric correction; MAIAC: Building a Complete Physical Model of Atmosphere-Surface (RT)

11. Queue of up to previous 16 days of (MODIS) observations Outputs: Surface reflectance Water vapour Aerosol Ancillary data corresponding to queue: Previous cloud mask, BRDF, land-water-snow mask, etc. MAIAC: Building a Complete Physical Model of Atmosphere-Surface (RT)

12. 230 Dry Season and Biomass Burning AOT RGB BRF CM CM Legend -Clear Land -Clear Water -Detected Smoke -Clouds -Cloud Shadows 223 - 2003 Clearing of Amazon forests for agricultural development. As timber dries, biomass burning begins.

13. … Biomass Burning (2003) 242244246 247248249

14. VIIRS AOT IP vs MODIS MAIAC (25km) (S. Kondragunta, S. Superczynski (NOAA), study for NASA GeoCAPE project) NOAA VIIRS MAIAC MODIS

15. VIIRS AOT IP vs MODIS MAIAC (25km) (S. Kondragunta, S. Superczynski (NOAA), study for NASA GeoCAPE project) 45°N 40N 3sN - - - - - - - - - - 35°N I 3o N -.-. 2sN -------- I --------- !-- -------- ---- -2sN -·-·-·-·-·-!-·-·- I ;:: 00 8 16 8 f;l 0,.._ 0.000.25 0.50 AOT 0.751.000.000.25 0.50 AOT 0.751.00 Number VIlAS good retrievals- AugNumber MAIAC retrivals- Aug so N 45N 40N 35N 3oN 2sN ;:: 8 ;:: 8 0 co 0,.._ 0 co 0,.._ 05 10 #VIlAS samples (x1000) 152005 10 # MAIAC samples (x1000) 1520

16. AERONET Comparisons VIIRS AOT IP vs MODIS MAIAC (25km) (S. Kondragunta, S. Superczynski (NOAA), study for NASA GeoCAPE project)

17. MAIAC: Building a Complete Physical Model of Atmosphere-Surface (RT) DT MAIAC Dark target algorithm is biased over urban surfaces; MAIAC is not. Global aerosol retrievals; low urban bias.

18. 232 233 Idaho/Wyoming – Yosemite Fires (08-2013) TOA RGB MAIAC AOT(0.47)MAIAC CM: Red – Clouds; Grey – Smoke;

19. Current Status -MAIAC is at MODAPS (land operational processing system); -MAIAC MODIS reprocessing will start Nov-2015; -MAIAC MODIS (based on C6+ L1B) for North America, South America, Africa (  10  ), and Europe for 2000-mid-2014 is available at NASA NCCS ftp: ftp://maiac@dataportal.nccs.nasa.gov/DataRelease/ (if asked for password, press Enter);

20. Aerosol Validation Data

21. AOD validation data from Bureau surface radiation network 31 stations, 17 currently open 240 station-years of data Aerosol data is being analysed

22. AeroSpan Aerosol characterisation via Sun Photometry: Australian Network 1997 - 2015 AeroSpan is operated by CSIRO Australian component of NASA/AERONET Range of surface and aerosol types Dust (arid zone) Smoke (tropics) Future stations in blue (next 12 months) Data routinely processed by NASA 3-min AOD and 1-hr aerosol microphysics from sky radiance inversions Strong collaboration with Bureau in publishing climatologies from both networks Ideal for validation of Himawari aerosol and surface products Contact: Ross.Mitchell@csiro.au

23. Tropical aerosol Time series Climatology Correlation Mitchell, R. M., B. W. Forgan, S. K. Campbell, and Y. Qin (2013), The climatology of Australian tropical aerosol: Evidence for regional correlation, Geophys. Res. Lett., 118, doi:10.1002/grl.50403. Time series Aerosol responds to intense wet seasons (2001, 2004, 2011) Climatology First characterization of Oz tropical aerosol 2 institutions, 3 sites,12-14 years’ data Correlation Tight regional correlation down to 5 days Regional-scale mixing?