Aerosol from MAIAC algorithm Ian Grant Australian Bureau of Meteorology

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

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

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

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

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

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

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

- 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 -Aerosol Type Discrimination; -Synergy among WV, CM, aerosol and AC; MAIAC: Building a Complete Physical Model of Atmosphere-Surface (RT) BRF 0.35 BRF n (global aerosol retrievals; low urban bias) DTMAIAC RGBRGB NIR (Dark Target Algorithm is biased over urban surfaces; MAIAC is not)

- 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 -Aerosol Type Discrimination; -Synergy among water vapour, cloud mask, aerosol and atmospheric correction; MAIAC: Building a Complete Physical Model of Atmosphere-Surface (RT)

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)

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

… Biomass Burning (2003)

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

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

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

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.

Idaho/Wyoming – Yosemite Fires ( ) TOA RGB MAIAC AOT(0.47)MAIAC CM: Red – Clouds; Grey – Smoke;

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: (if asked for password, press Enter);

Aerosol Validation Data

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

AeroSpan Aerosol characterisation via Sun Photometry: Australian Network 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:

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: /grl 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?