Aerosol from MAIAC algorithm

Ian GrantAustralian Bureau of Meteorology

Non-MeteorologicalAtmosphere Products

• Aerosol• Total Column Ozone• SO2

• 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

SO2

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 proxySmoke

• 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;

- Retrieval of Spectral Regression Coefficient: Relation of ρblue to ρ2.1 independently for each 1 km2

- 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)

Queue of up to previous 16 days of (MODIS) observations

Outputs:Surface reflectanceWater vapourAerosol

Ancillary data corresponding to queue: Previouscloud 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 BRFCM

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.

… Biomass Burning (2003)

242 244 246

247 248 249

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

40•N

3s•N - - - - - - - - - -35°N

I

I3o N

- .2 s•N --------I---------

!-- - - - - - - - - ---- -2s•N

-·-·-·-·-·-!-·-·-I

;::0 0 8 1

68 f;l 0,.._

0.00 0.25 0.50AOT

0.75 1.00 0.00 0.25 0.50AOT

0.75 1.00

Number VIlAS good retrievals- Aug Number MAIAC retrivals- Aug

so N

45•N

40•N

35•N

3o•N

2s•N

;::8

;::80co 0,.._ 0co 0,.._

0 5 10#VIlAS samples (x1000)

15 20 0 5 10# MAIAC samples (x1000)

15 20

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.

Aerosol Validation Data

AOD validation data fromBureau surface radiation network

• 31 stations, 17 currently open• 240 station-years of data• Aerosol data is being analysed

•

AeroSpanAerosol characterisation via Sun Photometry: Australian Network1997 - 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