1 satellite remote sensing of aerosols pawan k bhartia laboratory for atmospheres nasa goddard space...

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Satellite Remote Satellite Remote Sensing of Sensing of Aerosols Aerosols

Pawan K BhartiaPawan K BhartiaLaboratory for AtmospheresLaboratory for Atmospheres

NASA Goddard Space Flight CenterNASA Goddard Space Flight CenterMaryland, USAMaryland, USA

ISSAOS 2008ISSAOS 2008 22

LinkagesLinkages

Laboratory Measurements

In situ field Measurements

Ground-based remote sensing

Satelliteremote sensing

Direct-sun Sky-radiance Hem. Irradiance Lidar

Solar occultation Solar backscattered Lidar

Aethalometer Nephalometer Particle Counters • • • •

Chemical prop Optical prop Particle shape

A Priori information


OutlineOutline

Basic ConceptsBasic Concepts• Solar Occultation/ Limb scatteringSolar Occultation/ Limb scattering• Multi-spectral backscattered radianceMulti-spectral backscattered radiance• Multi-angle backscattered radianceMulti-angle backscattered radiance• PolarizationPolarization

Satellites/InstrumentsSatellites/Instruments• The “A-train” The “A-train” • MODIS, MISR & OMIMODIS, MISR & OMI

Model comparisonsModel comparisons


Solar Occultation & Limb Solar Occultation & Limb ScatteringScattering

Occultation: measures ext, sunrise & sunset only, twice per satellite orbit

Limb Scatt: measures Laer,throughout the orbit, but much less accurate than occultation.

Both methods limited to the stratosphere because of cloud interference

Ref: www-sage2.larc.nasa.gov/ www.iup.uni-bremen.de/sciamachy/


A typical scene from a nadir-A typical scene from a nadir-viewing satellite instrumentviewing satellite instrument


Backscattered Radiance MethodBackscattered Radiance Method

Backscattered radiance (watt/m2/nm/sr): L(0,Top-of-the-atm Reflectance: 0,L/I0cos0

Surface reflectance: s(0,

I0L

0

can be thought ofas the Lambert-eqv reflectivity of the atmosphere. A Lambertian surface of reflectivity will produce radiance L in the direction (0,


Properties of TOA Reflectance (Properties of TOA Reflectance ())

Rayl+aer+TRaylTaers+ …. higher order terms

inaccessible by satellite

€

In single - scattering approx :

Laer = I 0

4 πcosθ τ aer Ρ Θ( )ϖ 0

ρ aer = πLI 0 cosθ 0

= 14 cosθ cosθ 0

τ aer Ρ Θ( )ϖ 0

For satellites, typically, aer=0.1aer

Therefore, to get ±0.05 precision in estimating aer one needs ±0.005 precision in estimating s.

Phase fn is small


Reflectivity of OceanReflectivity of Ocean

s(0,Fresnel+water-leaving+white_caps

Fresnel Reflection:0=and =180˚ independent of cone angle depends upon wind speed. diffuse (-dep) sky radiance is Fresnel reflected at all angles.

0

Water Leaving Radiance:strongly dep, peaks at ~400 nm. Very small >500 nm. reduced by chlorophyll and CDOM absorption, enhanced by sediments. weak angular dependence.

White Caps:important at high wind speeds only

Solar glint


Remote Sensing of Aerosols Remote Sensing of Aerosols over open oceanover open ocean

AVHRR Channel 1 (0.6 m)Ocean reflectivity at 0.5 m is very small at directions away from the solar glint direction, which allows accurate estimation of AOT from satellites

Over most of the open ocean, cloud contamination is the main error source.

UV/blue s are much less suitable over ocean.


Estimation of size distribution Estimation of size distribution from from -dependence of -dependence of or or

wt fns20.34

€

ext = 34

Qext

r∫ ∂V∂ ln r d ln r

= W λ ,r( )∫ ∂V∂ ln r d ln r

€

∂V∂ ln r is sensitive to a limited

range of particle volumes As W moves to right with increase in itsamples larger particles

=

issue: W is very sensitive to REAL(), which varies significantly.


Aerosol Remote Sensing Over Aerosol Remote Sensing Over LandLand

Land reflectivity is larger and highly variable, both spectrally and with viewing geometry, which makes it difficult to do aerosol remote sensing over land.

Several clever techniques have been devised to minimize the problem.


Why can’t one see aerosols over Why can’t one see aerosols over bright surfaces?bright surfaces?

Rayl+aer+TRaylTaers+…

Since aerosols reflect light to space, as aer increases Taer

decreases. This reduces the effect of aerosols when s≠0.

At some surface reflectivity (s), 2nd and 3rd terms can cancel, i.e., aerosols cannot be seen at all.

If aerosols are absorbing, they can decrease over bright surfaces. Dust storm over the Red Sea


Land Aerosols Techniques Land Aerosols Techniques

Operational MODIS techniqueOperational MODIS technique• In near IR In near IR ≈ ≈ s s for small particlesfor small particles

• At other At other s, estimate s, estimate ss((k(k()) ss(IR), where k((IR), where k() are pre-) are pre-tabulatedtabulated

““Deep Blue” TechniqueDeep Blue” Technique• Takes advantage of the fact that deserts appear dark at Takes advantage of the fact that deserts appear dark at

blue wavelengthsblue wavelengths Multi-angle TechniqueMulti-angle Technique

Rayl+aer+TRaylTaers+ ….


Multi-angle TechniqueMulti-angle Technique

Satellite motion

€

aer = 14 cosθ cosθ 0

τ aer Ρ Θ( )ϖ 0

1 2

Because of the cos term, aer becomes at large large hence surface contribution becomes smaller.P(also changes with providing phase fun information to help select the correct aerosol model to do retrieval.

1515

UV Remote Sensing of UV Remote Sensing of AerosolsAerosols

Large Rayleigh scattering makes UV unattractive for measuring aerosol scattering. (At 340 nm Rayl can be 10-20 times larger than aer.)

In UV, aerosol absorption reduces the Rayleigh scattering from below the aerosol layer. This effect can be quite large if the aerosols are elevated.

Chief advantage of UV is that smoke and dust plumes can be detected over both dark and bright surfaces, including clouds, deserts, and snow/ice.

Retrieval algorithms exist to estimate abs=ext(1-0) over dark surfaces.


How do aerosols absorb in the How do aerosols absorb in the UV?UV?

€

abs ∝ λ−k

k = 1 for for BC

≈ 2 for OC

~ 3 for Desert Dust

abs=0.05abs=0.05

BCBC

OCOCDustDust


Effect of aerosol absorption on Effect of aerosol absorption on UV reflectance ratioUV reflectance ratio

Solar ZA: 45˚-55˚Satellite ZA: 0˚-60˚Azimuth= ~90˚

Curve Shifts due to aerosol absorption

Sky brightnessSky brightness

col

or S

atur

atio

nco

lor

Sat

urat

ion

blue

gray

Solar ZA: 45˚-55˚Satellite ZA: 0˚-60˚Azimuth= ~90˚

UV Aerosol Index (UV-AI) is derived from the left-down shift of this curve due to aerosol absorption

The shift is proportional to abs, but depends upon the height of the aerosol plume, higher the plume larger the shift.


Smoke from Colorado fires (June 25, 2002)

Transport of Mongolian dust to N. America in April 2001. This image was made by compositing several days of TOMS data.

SmokeSmoke

Desert DustDesert Dust

TOMS UV Aerosol Index

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Satellites & InstrumentsSatellites & Instruments


Older Instruments with Long Older Instruments with Long Time SeriesTime Series

AVHRR on NOAA Polar Satellites TOMS on Nimbus-7 Sea-WIFs

UV-Aerosol IndexEqv. AOT

Dust plume image

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2008

2008


Aerosol Instruments on the A-Aerosol Instruments on the A-TrainTrain

AquaAqua• Moderate Resolution Imaging Spectroradiometer Moderate Resolution Imaging Spectroradiometer

(MODIS)(MODIS) Terra (not part of the A-train)Terra (not part of the A-train)

• MODISMODIS• Multi-angle Imaging Spectroradiometer (MISR)Multi-angle Imaging Spectroradiometer (MISR)

AuraAura• (UV aerosols) Ozone Monitoring Instrument (OMI)(UV aerosols) Ozone Monitoring Instrument (OMI)

ParasolParasol• Multi-angle polarization measurement.Multi-angle polarization measurement.

CALIPSOCALIPSO• Aerosol LidarAerosol Lidar

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• NASA, Terra & Aqua

– launches 1999, 2001

– 705 km polar orbits, descending (10:30 a.m.) & ascending (1:30 p.m.)

• Sensor Characteristics

– 36 spectral bands ranging from 0.41 to 14.385 µm

– cross-track scan mirror with 2330 km swath width

– Spatial resolutions:

• 250 m (bands 1 - 2)

• 500 m (bands 3 - 7)

• 1000 m (bands 8 - 36)

– 2% reflectance calibration accuracy

– onboard solar diffuser & solar diffuser stability monitor

MODerate-resolution Imaging Spectroradiometer [MODIS]

Source: MODIS Team, NASA/GSFC

Improved over AVHRR: • Calibration • Spatial Resolution • Spectral Range & # Bands


MODIS ResultsMODIS Results

AOT

Fine to Coarse Mode Fraction

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2007 minus 8-yr mean

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While Indonesia’s smoke had a strong peak in 2006, S. America was more normal. This has a lot to do with wet/dry years and the opposite effects of El Niño on the two regions

27Koren et al. (2007)

Slopes of 6 year AOD trend (2000 - 2005)

Strong IncreaseOf smokeIn 6 years

DifferenceBetween 2006And 2005

SuddenDecreaseIn 2006

Decrease due to a combinationof a wetter year and smallrural farmers adhering to firecontrol measures

MODIS aerosol products used to identify interannual patterns.

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• Nine CCD push-broom cameras

• Nine view angles at Earth surface: 70.5º forward to 70.5º aft

• Four spectral bands at each angle: 446, 558, 672, 866 nm

• Studies Aerosols, Clouds, & Surface

Multi-angle Imaging SpectroRadiometer

http://www-misr.jpl.nasa.govhttp://www-misr.jpl.nasa.gov

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• Land & Water • Bright Surfaces• Globe ~ weekly• ~ 10:30 AM[+ particle size, shape, SSA constraints]

MISR Monthly Global Aerosol Mid-VIS AOT

July 2005

January 2005

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Sensitivity to aerosols over bright surfaces

Thin haze over land is difficult to detect in the nadir view due to the brightness of the land surface

nadir 70º

Saudi Arabia,Red Sea,Eritrea

Over Bright Desert Sites, mid-vis. AOT to ±0.07 [Martonchik et al., GRL 2004]

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MISR height analysis of World Trade Center plume12 September 2001

MISR70º image

MISRstereo heightsof plumepatches

From: Stenchikov et al., J. Env. Fl. Mech., 2006

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//smsc.cnes.fr/PARASOL/

POLDER instrument

6 km x 7 km nadir pixel9 channels (443-910 nm)3 polarization channels (443, 670, 865 nm)Best for detecting fine mode fraction and particle shape.

Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL)


Ozone Monitoring Instrument

Joint Dutch-Finish Instrument with

Dutch/Finish/U.S. Science Team

• PI: P. Levelt, KNMI

• Hyperspectral wide FOV Radiometer

• 270-500 nm

• 13x24 km nadir footprint

• Swath width 2600 km

13 km

(~2 sec flight))2600 km

12 km/24 km (binned & co-added)

flight direction» 7 km/sec

viewing angle± 57 deg

2-dimensional CCDwavelength

~ 580 pixels~ 780 pixels

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Absorbing Aerosols as seen by OMI

Smoke

Dust

Aerosol Transport across the Oceans in terms of the Absorbing Aerosol Index

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By means of an inversion algorithm AOD and SSA are derived

March 9, 2007

Retrieving Aerosol Absorption in the near-UV

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Model Comparisons

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Alaska/Canada smoke transport

North America Boreal fire• In July 2004, large forest fires occurred in the

North America boreal region. Smoke aerosols were being transported to large areas in Canada and the U.S., affecting regional air qualities.

• Figures show the aerosol distributions of July 2004 over North America as seem by the MODIS and MISR satellite instruments and simulated by the GOCART model. Superimposed in circle are the aerosol optical depth measured by the AERONET sunphotometer network

• NASA data used: MODIS, MISR, AERONET for aerosol optical depth, MODIS fire counts for modeling (Petrenko et al., AMS meeting, 2007).

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MODIS, MISR, GOCART, AERONET: 200407

Feature: North America Boreal fire – captured by MODIS,

MISR, GOCART• MODIS: Not available over bright

surfaces (e.g., deserts) and cloudy regions (e.g., N. Pacific)

• MISR: Not available over cloudy regions (N. Pacific, central America); excessive AOT over Greenland

• GOCART: North America boreal fire emission or injection height maybe too low so smoke did not go far enough

AERONET data in circles AERONET data in circles

AERONET data in circles

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Aerosols in 200010 and 200610:North America and Europe: Decrease from 2000 to 2006. East Asia: Increase from 2000 to 2006.

Indonesia: Intense fire in October 200620

0001

020

0061

0

MODIS

MODIS

GOCART

GOCART

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The figures below show global aerosol distribution and transport observed by the MODIS instrument on EOS-Terra (left column) and simulated by the global model GOCART (right column) for April 13 (top row) and August 22 (bottom row), 2001. Red color indicates fine mode aerosols (e.g., pollution and smoke) and green color coarse mode aerosols (e.g., dust and sea-salt). Brightness of the color is proportional to the aerosol optical depth. On April 13, 2001, there are heavy dust and pollutions transported from Asia to the Pacific and dust transported from Africa to Atlantic; while on August 22 large smoke plumes from South America and Southern Africa are evident. Figure credit: Yoram Kaufman.

MODIS (Satellite) GOCART (Model)

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Trans-Pacific Transport of Dust

TOMS AI April 11, 2001Dust AOT April 11, 2001 GOCART

TOMS AI April 14, 2001

TOMS AI April 8, 2001Dust AOT April 8, 2001 GOCART

Dust AOT April 14, 2001 GOCART

Simulated by GOCART (model) Observed by TOMS (satellite)

Trans-Pacific transport of dust in April 2001. Dust originating from Asian desert (April 8) is being transported across the Pacific and reaches North America (April 14). Left column: GOCART model simulation; right column: aerosol index from NASA satellite instrument TOMS (Chin et al., JGR 2003).

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Contribution of Satellites in improving aerosol models

• Improving the dust sources by comparing models with TOMS AI (Ginoux et al.).

• Mass transport of dust and pollution aerosols using MODIS (Kaufman et. al. 2005)

• MISR smoke plume height to improve smoke injection height.

• MISR non-spherical particle fraction for evaluating model-derived dust and non-dust aerosols.

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Further ReadingNature, Vol 419, 12 Sept 2002

Yoram Kaufman 1948-2006

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Passive Remote Sensing of Aerosols by Satellites- Future

• New instruments will have MODIS-like spatial and spectral coverage with MISR and PARASOL-like multi-angle and polarization capability to determine ref index, size, and shape.

• Advanced UV instruments may allow separation of OC and BC aerosols.

• High spectral resolution O2-A band measurements may provide aerosol vert profile information with daily global mapping.

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References

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Some Satellite-Aerosol Product Web Sites

• http://www-misr.jpl.nasa.gov MISR Home page; background, image gallery,..

• http://eosweb.larc.nasa.gov MISR, CERES, SAGE, MOPITT, TES, data & docs

• http://modis-atmos.gsfc.nasa.gov/IMAGES/index.html MODIS global browse imagery

• http://g0dup05u.ecs.nasa.gov/Giovanni/ MODIS on-line visualization & analysis tools

• http://modis-atmos.gsfc.nasa.gov/ MODIS atmosphere products & docs

• http://cybele.bu.edu/modismisr/index.html MISR+MODIS climate data (surface emphasis)

• http://modis-fire.umd.edu/ MODIS-UMD Fire products & docs

• http://maps.geog.umd.edu/default.asp MODIS-UMD global Fire occurrence mapper

• http://idea.ssec.wisc.edu/http://idea.ssec.wisc.edu/ IDEA merged MODIS-EPA Air Quality

• http://alg.umbc.edu/usaq/http://alg.umbc.edu/usaq/ UMBC Air Quality events

• http://jwocky.gsfc.nasa.gov/eptoms/ep.htmlhttp://jwocky.gsfc.nasa.gov/eptoms/ep.html TOMS/OMI aerosol & O3, data & docs

• http://www.osdpd.noaa.gov/PSB/EPS/Aerosol/Aerosol.htmlhttp://www.osdpd.noaa.gov/PSB/EPS/Aerosol/Aerosol.html NOAA AVHRR aerosols

• http://oceancolor.gsfc.nasa.gov/SeaWiFS/BACKGROUND/http://oceancolor.gsfc.nasa.gov/SeaWiFS/BACKGROUND/ SeaWiFS data & docs

• http://aeronet.gsfc.nasa.gov/ http://aeronet.gsfc.nasa.gov/ AERONET AOT & properties, data & docs

http://modis-fire.umd.edu/








http://maps.geog.umd.edu/default.asp








http://idea.ssec.wisc.edu/











http://alg.umbc.edu/usaq/







http://jwocky.gsfc.nasa.gov/eptoms/ep.html











http://www.osdpd.noaa.gov/PSB/EPS/Aerosol/Aerosol.html









http://oceancolor.gsfc.nasa.gov/SeaWiFS/BACKGROUND/







http://aeronet.gsfc.nasa.gov/





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A SAMPLE OF RELEVANT PUBLICATIONS – SEE REFERENCES THEREIN

Abdou, W. A., et al, 2005, Comparison of coincident MISR and MODIS aerosol optical depths over land and ocean scenes containing AERONET sites, J. Gelphys. Res., doi:10.1029/2004JD004693.

DiGirolamo, L., et al, 2004, Analysis of Multi-angle Imaging SpectroRadiometer (MISR) aerosol optical depths over greater India during winter 2001-2004, Geophys. Res. Let., 31, L23115, doi:10.1029/2004GL021273.

Diner, D.J, et al, 2005, The value of multi-angle measurements for retrieving structurally and radiatively consistent properties of clouds, aerosols, and surfaces, Remt. Sens. Env. 97, 495-518.

Kahn, R., et al, 2005, MISR global aerosol optical depth validation base d on two years of coincident AERONET observations, J. Geophys. Res., doi:10:1029/2004JD004706.

Kalashnikova O. V. , R. Kahn 2006, Ability of multiangle remote sensing observations to identify and distinguish mineral dust types: 2. Sensitivity over dark water, J. Geophys. Res., 111, D11207, doi:10.1029/2005JD006756.

Kaufman, Y.J ., et al. 1997. Operational remote sensing of tropospheric aerosol over land from EOS Moderatre Resolution Imaging Spectroradiometer. , J. Geophys. Res. 102, 17 051–17 067.

Levy, R.C., et al., 2003. Evaluation of the Moderate-Resolution Imging Spectroradiomenter (MODIS) retrievals of dust aerosol over the ocean during PRIDE, J. Geophys, Res. 108, doi:10.1029/2002JD002460.

Liu, Y, R.J. Park, D.J. Jacob, .Q. Li, V. Kilaru, and J.A. Sarnat, 2004, Mapping surface concentrations of fine particulate matter using MISR satellite observations of aerosol optical thickness, J. Geophys. Res., doi:10.1029/2004JD005025.

Martonchik, J.V., D.J. Diner, K.A. Crean, and M.A. Bull, 2002. Regional aerosol retrieval results from MISR, IEEE Transact. Geosci. Remt . Sens. 40, 1520-1531.

Mishchenko, M., et al, 1999. Aerosol retrievals over the ocean by use of channels 1 and 2 AVHRR data: Sensitivity analysis and preliminary results. Appl. Opt. 38, 7325-7341.

Remer, L.A., et al., 2005. The MODIS aerosol algorithm, products, and validation, J. Atmos. Sci., 62, pp. 947-973. Tanre, D., et al, 1997. Remote sensing of aeroosll properties over oceans using the MODIS/EOS spectral radiances, J. Geophys, Res. 102, 16 971-

16 988. Torres, O., et al, 2005. Total Ozone Mapping Spectrometer measurements of aerosol absorptio n from space: Comparison to SAFARI2000

ground-based observations, J. Geophys, Res. 110, doi:10.1029/2004JD004611.

Levy et al., 2nd generation MODIS Land algorithm, JGR, vol 112, (doi:10.1029/2006JD007815 & 10.1029/2006JD007811), 2007.

1 satellite remote sensing of aerosols pawan k bhartia laboratory for atmospheres nasa goddard space...

Documents

effect of aerosols

aerosol remote sensing

dep sky radiance

skyradiance hem

landland reflectivity

mmocean reflectivity

surface reflectivity

rrayl raer trayltaerrs