VRAME: Vertically Resolved Aerosol Model for Europe from a Synergy of EARLINET and AERONET data...

VRAME:Vertically Resolved Aerosol Model for Europe from a Synergy of EARLINET and AERONET data

Elina Giannakaki, Ina Mattis, Detlef Müller, Olaf Krüger

EARLINET ASOS Symposium, Geneva, 20 September 2010

Outline

The idea behind VRAME

Atmospheric correction

Development of a new aerosol model

Activities of VRAME

Combination of EARLINET and AERONET data


The idea behind

Satellites monitoring the oceans in the visible range of electromagnetic spectrum

The primary goal is to extract concentrations of marine phytoplanktonPhyto (φυτό) +plankton (πλανκτόν)


Satellite Ocean Color A satellite observes both oceans

and the atmosphere

The atmosphere is approximately 90% of the measured signal in the visible and must be accurately modeled and removed

A 1% error atmospheric correction will result in a 10% error in water-leaving radiances


5

Signal

Derived bio-optical products, e.g. chl-a

Top-of-the-atmosphere calibrated radiances

Water-leaving radiances

geolocation and calibration

atmospheric correction

bio-optical algorithms

Ocean Color Retrieval

Level-0 ( L0 )

Level-1B ( L1B )

Level-2 ( L2 )

Level-2 ( L2 )


Shettle and Fenn (1979) introduced a set of basic aerosol models

Continental (rural and urban)

Maritime (oceanic and continental)

d’ Almeida et al. (1991) provide a more comprehensive classification

Maritime (clean, mineral, polluted)

Ocean color implementation, Gordon and Wang (1994) tropospheric, coastal, maritime and oceanic

Antoine and Morel (1999) include operational models for desert dust

Up to now…


MERIS

SeaWiFS

New generation of aerosol models, Ahmad et al. (2010)

bimodal lognormal distribution

Eight relative humidity values (30 – 95 %) Varying fine mode fraction (from 0 to 1)Same spectral dependence of SSA as observed in AERONET

Up to now…


Develop a new aerosol model


Comprehensive, quantitative and statistically significant data base


Synergy of EARLINET and AERONET datasets for VRAME


Lidar data already finilized are only used which are included in the

ESA-CALIPSO database


Synergy of AERONET and EARLINET datasets for VRAME

The first and currently primary application of the new aerosol model will be the atmospheric correction of MERIS data over the ocean

WAVELENGTHs (MERIS):

443, 510, 560, 709, 778, 865 nm

VERTICAL RESOLVED:

Extinction Coefficient

Single Scattering Albedo

Phase Function

Assymetry Parameter

AEROSOL TYPES:

Marine polluted

European anthropogenic pollution

Continental background aerosols

Saharan dust

Volcanic aerosols in the stratosphere

Aged and young forest fire smoke


VRAME

1.Development of the non maritime aerosol database

Identification of aerosol layers

Aerosol – type analysis

Profiles of optical and microphysical properties

Extinction Coefficient


Phase Function

Assymetry Parameter

As input to RT model


VRAME

2.LUT generation

RT calculations for individual and for the generalized

EARLINET/AERONET datasets

The results of these calculations will be the radiances on

TOA at different wavelengths and viewing geometries



VRAME


2.LUT generation

3.Sensitivity Study

Is it possible to retrieve the aerosol type from TOA

radiances?

measurable radiances of a satellite sensor show

significant differences in the presence of different

aerosol types

How profitable is the knowledge of the aerosol

type?

climatological mean aerosol data/specific aerosol

type

How profitable is the knowledge of the profile

information?

column average aerosol properties / vertical

information


VRAME


2.LUT generation

3.Sensitivity Study

4.Aerosol distinction algorithms development

Distinguish between different aerosol types by the ratio of

radiances measured in different MERIS channel


VRAME


2.LUT generation

3.Sensitivity Study


5.Validation

Include in-situ measurements of water leaving reflectance

spectra and demonstrate if an improvement of the

atmospheric correction with the new aerosol model can be

achieved EARLINET ASOS Symposium, Geneva, 20 September 2010

VRAME


2.LUT generation

3.Sensitivity Study


5.Validation


EARLINET and AERONET for VRAME

Super-site stations Cabauw, The Netherlands (2008-2009) Leipzig, Germany (2001-2009) Potenza, Italy (2006-2009) Athens, Greece (2008-2009)



Super-sites Cabauw, The Netherlands (2008-2009) Leipzig, Germany (2001-2009) Potenza, Italy (2006-2009) Athens, Greece (2008-2009)

High performance Thessaloniki, Greece (2003-2009) Potenza, Italy (2004-2006) Barcelona, Spain (2006-2009) Granada, Spain (2006-2009) Hamburg, Germany (2006-2009) Minsk, Belarus (2006-2009)



Super-sites Cabauw, The Netherlands (2008-2009) Leipzig, Germany (2001-2009) Potenza, Italy (2006-2009) Athens, Greece (2008-2009)

High performance Thessaloniki, Greece (2003-2009) Potenza, Italy (2004-2006) Barcelona, Spain (2006-2009) Granada, Spain (2006-2009) Hamburg, Germany (2006-2009) Minsk, Belarus (2006-2009)

Basic stations Belsk, Poland (2006-2009) Lecce, Italy (2006-2009)




22 July 2004: Smoke / Anthropogenic

Super-sites Cabauw, The Netherlands Leipzig, Germany Potenza, Italy Athens, Greece

10 June 2010: AnthropogenicHigh performance

Thessaloniki, Greece Potenza, Italy Barcelona, Spain Granada, Spain Hamburg, Germany Minsk, Belarus

Leipzig as only bp at 532 nm Basic stations Belsk, Poland Lecce, Italy

Leipzig, 22 July 2004

Time (UTC)


Consistency of two datasets


Aerosol Type, 22 July 2004


0

1

2

3

4

5

6

7

8

9

0 1 2 3 4 5 0 50 100 20 40 60 80 -1 0 1 2 3 0.01 0.02 0.03 0.04

BACKSCATTERCOEFF., sr-1 Mm-1

HE

IGH

T,k

m

355 nm 532 nm 1064 nm

EXTINCTIONCOEFF., Mm-1

LIDAR RATIO, sr

åa,VIS-UV

åb,VIS-UV

åb,IR-VIS

ÅNGSTRÖMEXPONENT

DEPOLARIZATIONRATIO

The optical profiles, 22 July 2004


0

1

2

3

4

5

6

7

8

9

0 20 40 60 80 100 120 140 160 180

EXTINCTION COEFFICIENT [Mm-1]

HE

IGH

T, a

sl [k

m]

355 nm 532 nm 443 nm 510 nm 560 nm 709 nm 778 nm 865 nm 1064 nm


Extinction CoefficientWAVELENGTHs (MERIS channels):

443, 510, 560, 709, 778, 865 nm

443,510 and 560 nmAerosol Extinction Coefficient at 532 nm [LIDAR]Ångström exponent between 355/532 nm [LIDAR]

709,778 and 865 nmAerosol Extinction Coefficient at 532 nm [LIDAR]Ångström exponent between 500/870 nm [CIMEL]

0

1

2

3

4

5

6

7

8

9

0 1 2 3 4 5 0 50 100 20 40 60 80 -1 0 1 2 3 0.01 0.02 0.03 0.04

BACKSCATTERCOEFF., sr-1 Mm-1

HE

IGH

T,k

m

355 nm 532 nm 1064 nm

EXTINCTIONCOEFF., Mm-1

LIDAR RATIO, sr

åa,VIS-UV

åb,VIS-UV

åb,IR-VIS

ÅNGSTRÖMEXPONENT

DEPOLARIZATIONRATIO

The optical profiles, 22 July 2004


Time (UTC)



0

1

2

3

4

5

6

7

8

9

0.7 0.8 0.9 1.0

SINGLE SCATTERING ALBEDO

HE

IGH

T [

km]

355 nm 532 nm 1064 nm

With inversion of optical properties [3 backscatters and 2 extinctions] we retrieve the profile of SSA for 355, 532 and 1064 nm

Linear approximation to estimate the desired wavelengths

WAVELENGTHs (MERIS channels):

443, 510, 560, 709, 778, 865 nm

0 20 40 60 80 100 120 140 160 1801E-3

0.01

0.1

1

10

100

1000

SCATTERING ANGLE (degree)

PH

AS

E F

UN

CT

ION

- 4

43 n

m

Layer 1 Layer 2 Layer 3 Layer 4 Layer 5 Layer 6 Layer 7 Layer 8 Layer 9


Phase Function and Asymmetry Parameter

g

coscoscos2

1 1

1

dPg

Refractive index and size distribution from inversion algorithm are used in a Mie code to find the phase function in several wavelengths for each layer

Then the asymmetry parameter is being calculated:

The results: Input for RT model


400 500 600 700 800 9000.00

0.05

0.10

0.15

0.20

EX

TIN

CT

ION

CE

OE

FF

ICIE

NT

[km

-1]

WAVELENGTH [nm]

layer1 layer2 layer3 layer4 layer5 layer6 layer7 layer8 layer9

400 500 600 700 800 900 1000 11000.75

0.80

0.85

0.90

0.95

SIN

GL

E S

CA

TT

ER

ING

AL

BE

DO

WAVELENGTH, nm

Layer1 Layer2 Layer3 Layer4 Layer5 Layer6 Layer7 Layer8 Layer9

0 20 40 60 80 100 120 140 160 1801E-3

0.01

0.1

1

10

100

1000

SCATTERING ANGLE (degree)

PH

AS

E F

UN

CT

ION

- 4

43

nm

Layer 1 Layer 2 Layer 3 Layer 4 Layer 5 Layer 6 Layer 7 Layer 8 Layer 9

Thessaloniki, 10 June 2010


The optical profiles


0

1

2

3

4

5

6

7

8

0 100 200 300 0 2 4 6 0 20 40 60 80 100 120 0 1 2 3 40

1

2

3

4

5

6

7

8

[a]

EXT. COEF. [Mm-1]

HEIG

HT, a

sl [km

]

355 nm

[b]

BSC. COEF. [Mm-1sr-1]

355 nm 532 nm

[c]

LIDAR RATIO [sr]

355 nm

THESSALONIKI, 10 JUNE 2010

[d]

ÅNGSTRÖM EXP.

b355

/b532


532532355 aLRLR

10201020/532

532/355

1020/532

532/355 aA

A

A

Atotal

total

layer

layer

Following Balis et al. (2009) the missing information in lidar profiles are approximated with the synergy of sunphotometer data:

In this way the inversion algorithim is being applied and the sequence of the previous steps could be applied

Additional assumptions to reach MWL stations

CIMEL

Consistency check


161 162 1630.00

0.25

0.50

0.75

1.00

AERONET Input of the RT

443nm 510nm 560nm 709nm 778nm 865nm

443nm 870nm

Thessaloniki, 10.06.2010, anthropogenic aerosols

1020nm 870nm 675nm 440nm

A

ero

sol O

ptic

al D

ep

th

Julian day

RT results

Leipzig, 22 July 2004 as basic station


Apply the information of backscatter contribution to the total backscatter into the total aerosol optical depth to retrieve extinction coefficients at several layers and several wavelengths

Assume same microphysical properties though column

Summary

A 1% error in atmospheric correction will result in a 10% error in water-leaving radiances

• The main objective of VRAME is to develop a dynamic, vertically resolved aerosol model to be delivered to the sattelite community for accurate atmospheric correction.

With the synergy of AERONET and EARLINET data a dynamic aerosol model will be developed

Different assumptions need to be introduced for each group of dataset


Thank you for your attention

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VRAME: Vertically Resolved Aerosol Model for Europe from a Synergy of EARLINET and AERONET data...

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