amfic: aerosol retrieval gerrit de leeuw, fmi/uhel/tno pekka kolmonen, fmi anu-maija sundström,...

AMFIC:Aerosol retrieval

Gerrit de Leeuw, FMI/UHEL/TNOPekka Kolmonen, FMIAnu-Maija Sundström, UHELLarisa Sogacheva, UHEL Juha-Pekka Luntama, FMISini Merikallio, FMI

AMFIC KO, KNMI, 26 October 2007 2

Aerosol measurments: in situPhysical, optical and chemical

properties difficult to measure(EUSAAR)

In situ measurements, when well made, provide the highest possible accuracy, reproduceability, temporal resolution (EUSAAR)

Sophisticated measurements provide information on processes

How representative are measurements at a certain site for the surrounding area and beyond? Courtesy Mikael Ehn, UHEL


Satellite: retrieval of aerosol properties• Satellites provide a snapshot

for a large area, with the same instrument, the same method and the same algorithm

However:

• The satellite data is less accurate than in situ data

• Different instruments provide different results

• Different methods provide different results

• Different algorithms provide different results

But: the good news is the good agreement between several instruments and algorithms


• Satellites need ground based measurements:

• Validation

• Evaluation

• Satellites may fill the gaps when properly used

• Satellite retrieval methods need further development:

• to reach maturity

• be useful for scientific and operational applications


Satellite observations and ground based networks

• MODIS retrieved aerosol optical depth at 0.55 μm

• + AERONET sites(Sun Photometers)

• + Aerosol LIDAR networksEARLINETADNETMPLNET

IPCC, AR4, 2007


Validation

AERONET sun photometers

Campaigns: in situ ground based / airborne


Remote Sensing from satellites :Top of the Atmosphere Radiance

atmosphereatmosphere

surfacesurface


Radiative transfer

• T = direct transmittance (↑) upwards and (↓) downwards,

• t = diffuse transmittance

• ρs,dir, ρs,dif↓, ρs,dif↑ and ρs,iso = bi-directional surface reflectance terms.

• All terms depend on the wavelength and on the sun-satellite geometry.

tttTTtTT isosdifsdifsdirsatm ,,,,


ATSR-2 (ERS-2) : 1995 – 2002+AATSR (ENVISAT) : 2002 – 2007+

> 0.555, 0.659, 0.865, 1.6, 3.7, 11, 12 μm> 1 x 1 km2

> 500 km swath (global in 3 days)

> 0.555, 0.659, 0.865, 1.6, 3.7, 11, 12 μm> 1 x 1 km2

> 500 km swath (global in 3 days)

Aerosols/types; clouds

High resolution

Along Track Scanning Radiometer


Along-track scanning with two viewing angles

• Two viewing angles allow to account for surface effects on TOA radiation

• Swath 500 km

• High resolution (1x1 km2)

• Over water (SV)

• Over land (DV)

• Bright surfaces

• Near real time

• Two viewing angles allow to account for surface effects on TOA radiation

• Swath 500 km

• High resolution (1x1 km2)

• Over water (SV)

• Over land (DV)

• Bright surfaces

• Near real time


Dual View AlgorithmDual View Algorithm

TOA atm

surf

surfdown up

R

R rT T

1

TOAn

atmn

downn

upn

TOAf

atmf

downf

upfT T k T T

kR

Rsurff

surfn

TOAf

TOAn

m

m

( . )

( . )

16

16


Algorithms


Satellite remote sensing of aerosol

• AOD over land:

• 1990’s

• ATSR-2 (Veefkind et al., 1998): First AOD retrieved over land!

• Polder-1 & 2

• MODIS

• MISR


ATSR-2 / AATSR: Examples

Veefkind et al., 2000

Sulfate Concentration ECN, Petten, NL

0

5

10

15

25-Jul 00:00 26-Jul 00:00

Date

SO

4 µ

g m

-3


ATSR-2 / AATSR: Examples

Aerosol distribution over Europe for August 1997:

See colour scale on the right:

high concentrations are red

low concentrations are blue

Roblez-Gonzalez et al., 2000


TEMIS progress: Validation vs AERONET

Ispra, 12 June 2003 Venice, 2003


ATSR-2: INDOEX• Mixture of aerosols produced over

land (industrial, fossil fuel and biomass burning, dust) and over sea

• Minimizing error function to determine optimum mixture

• Provides:

• AOD

• Angstrom coefficient

• Mixture

• Over the ocean the mixture gradually changes from continental to sea salt

• Validation with campaign data

Robles Gonzalez et al., 2006


0.0

0.1

0.2

0.3

0.4

0.5

0.6

(b)

AO

D

(a)

ATSR-2 Sun photometer

Goa IndiaMar. 10, 1999

0.0

0.1

0.2

0.3

0.4

0.5

0.6

KaashidhooMar. 26, 1999

(d)


0.4 0.6 0.8 1.0 1.2 1.4 1.60.0

0.1

0.2

0.3

0.4

0.5

0.6

Wavelength (m)

(c)

AO

D

DharwarMar. 10, 1999


0.4 0.6 0.8 1.0 1.2 1.4 1.60.0

0.1

0.2

0.3

0.4

0.5

0.6

Wavelength (m)

MaleFeb. 10, 1999

(e)


0.0 0.2 0.40.0

0.1

0.2

0.3

0.4

0.5

0.67

AOD (ATSR-2)

AO

D (Sun

pho

tom

eter

)

Dual viewalgorithm

0.0 0.2 0.40.0

0.1

0.2

0.3

0.4

0.5

AOD (ATSR-2)

Single viewalgorithm (f)

0.55 0.67 0.87


ATSR-2: SAFARIAugust 2000 September 2000

Robles Gonzalez et al., 2007


AATSR aerosol retrieval over bright surfaces:United Aerosol Experiment, United Arabic

Emirates

UAE2, 7 Sep, 2004 (670 nm)

Over water:

R=0.78 (22 points)

Over land:

R=0.57 (53 points)

Robin Schoemaker


TEMIS Progress: NRT• Data availability:

• ftp from Rolling Archive: automated using script

• RAID: data stored on 2 HD

• Regular update with new data

• Example: AOD(670), Po Valley, 2 August 2007


TEMIS Progress: NRT• Automated selection area of interest (Po valley)

• Inclusion data processing in script

• QC: manual (later automated?)

• Comparison with AERONET (Ispra, Venice)

• Spatial variation and statistics

• Data presentation on website

• FMI, link from TEMIS website

• TEMIS website – examples

• Data description and background info

• User feedback:

• Data presentation

• Data use and use-ability

China


TEMIS Progress• Improvements:

• Aerosol models tuned to China conditions (a priori), using climatology, models and in situ data

• Cloud screening

amfic: aerosol retrieval gerrit de leeuw, fmi/uhel/tno pekka kolmonen, fmi anu-maija sundström,...

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