CAMS GA Space Lidars by Amiridis

Application of advanced EARLINET

methodologies on space-borne lidars for the

retrieval of higher level aerosol products

V. Amiridis

with input from the EARLINET

NOA - National Observatory of Athens

Vassilis Amiridis, CAMS GA, 15 June 2016

EARLINET can advance space-lidar retrievals

EARLINET

How?

1. By providing optical and microphysical properties per aerosol type in the form of

advanced aerosol models, of paramount importance for aerosol type classification

from space (e.g. developments within the ESA-CALIPSO, LIVAS, NATALI, HETEAC)

2. By using these multi-wavelength aerosol models for homogenizing NASA and ESA lidar

missions in order to derive a homogeneous multi-decadal climatic dataset.

3. By applying the EARLINET models for satellite product optimization.

4. By providing advanced methodologies and algorithms for the derivation of higher level

products. Some examples include the retrieval of:

- pure dust product

- fine and coarse particle mode

- particle concentrations

- CCN/IN particle concentrations


CALIPSO:

L1 product and L2 aerosol type


Aerosol classification scores

Aerosol Type Lidar

Ratio (sr)

(Omar et

al., 2009)

Agreement with

EARLINET and

airborne HSRL

Dust 40 80%

Marine 20 55%

Polluted Continental 70 54%

Polluted Dust 65 35%

Smoke 70 13%

Clean Continental 35 -

Aerosol classification is based on:

- Particle depolarization ratio

- Layer-integrated backscatter (~aerosol load)

- Surface information (land/ocean)


Dust case: Depolarization corrections

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

0

2000

4000

6000

8000

10000

12000

14000

16000

Polluted Dust

Nu

mb

er

of

laye

rs

Particle depolarization ratio

CALIPSO

Eq. (4)

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

0

2000

4000

6000

8000

10000

12000

14000

16000

Nu

mb

er

of

laye

rs

Particle depolarization ratio

CALIPSO

Eq. (4)

Dust

Amiridis et al., 2013Tesche et al., 2013


Dust case

CALIPSO LR = 40 sr

EARLINET LR = 58 sr

Multiple Scattering

effect for CALIPSO

Tesche et al., 2013

Wandinger et al., 2012


CALIPSO vs AERONET

Schuster et al., 2012


Dust case

Amiridis et al., 2013

Consolidating all EARLINET

findings and applying

corrections for dust on

CALIPSO observations


CALIPSO vs AERONET for dust

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Pearson's r = 0.905, Slope = 0.660

CA

LIO

P A

OD

- L

R=

40

sr

(53

2 n

m)

AERONET AOD (532 nm)

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Pearson's r = 0.901, Slope = 0.629

CA

LIO

P A

OD

- L

R=

40

sr

(35

5 n

m)


0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0Pearson's r = 0.905, Slope = 0.956

CA

LIO

P A

OD

- L

R=

58

sr

(53

2 n

m)


0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0Pearson's r = 0.901, Slope = 0.912

CA

LIO

P A

OD

- L

R=

58

sr

(35

5 n

m)


CALIPSO-AERONET

Collocation

In pure Dust cases

from CALIPSO typing



CALIPSO vs MODIS for dust over ocean

CALIPSO-MODIS

Collocation

Red overpasses rejected



Pure dust discrimination from dust mixtures

0

1

2

3

4

5

0 1 2 3 4 5 6

0

1

2

3

4

5

-0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

0

1

2

3

4

5

0.000 0.001 0.002 0.003

Polluted

Dust

Dust

Clear Air

Aerosol Subtype

He

igh

t (k

m)

Particulate Depolarization Ratio

CALIPSO L2

mean Layer CALIPSO

mean Layer corrected

BSC. COEF. 532 nm (km-1sr

-1)

Total

Pure Dust

Other Type

with depolarization 0.03

=( − )(1 + )

( − )(1 + )Tesche et al., 2012


BSC-DREAM8b vs LIVAS dust product

0

1000

2000

3000

4000

5000

6000

7000

8000

0.2 0.3 0.4 0.5 0.6 0.7 0.8

Version I

Version II

Version III

Pearson's r for Extinction

Heig

ht (m

)

Sahel0.27

Atlantic0.09

NW Africa-0.14

W.IP W.Med C.Med

E.Med

Abs. bias, AERONET - BSC-DREAM8b

Mo

de

l o

ve

restim

atio

nM

od

el

un

de

restim

atio

n

|bias|<0.1



10-year pure-dust LIVAS climatology

Marinou et al., 2015Vassilis Amiridis, CAMS GA, 15 June 2016

⇒ appearance of localized

regions of increased

extinction coefficient values

over mountains (the Alps,

Pyrenees, Carpathian

mountains)

Dust LIVAS product using EARLINET methods

Marinou et al., 2015


Domain Jan-Feb-Mar Apr-May-Jun Jul-Aug-Sept Oct-Nov-Dec

Dust Layer Height

Marinou et al., 2015


MACC: Using the LIVAS dust product for evaluation

Georgoulias et al., 2016



Georgoulias et al., 2016Vassilis Amiridis, CAMS GA, 15 June 2016

In total: MACC overestimates by 22 %

EU CE EE SWE CM EM ATL CWSah ESah ME

MACC 0.132 0.033 0.043 0.049 0.068 0.071 0.071 0.178 0.173 0.248

LIVAS 0.108 0.012 0.016 0.026 0.051 0.050 0.047 0.186 0.123 0.215

MB 0.024 0.021 0.027 0.023 0.017 0.021 0.024 -0.008 0.050 0.033

NMB 22 % 175 % 169 % 89 % 33 % 42 % 51 % -4 % 41 % 15 %

Evaluation of MACC DOD550


Georgoulias et al., 2016Vassilis Amiridis, CAMS GA, 15 June 2016

MACC underestimates DOD over regions with very high dust loadings

Evaluation of MACC DOD550

0

1

2

3

4

5

6

7

8

9

0.000 0.025 0.050 0.075 0.100 0.125 0.150

Extinction coefficient (km-1)

He

igh

t (k

m)

MACC

LIVAS

EUROPE

0

1

2

3

4

5

6

7

8

9

0.0000 0.0025 0.0050 0.0075

Mean Bias (km-1)

He

igh

t (k

m)


Georgoulias et al., 2016



Georgoulias et

al., 2016Vassilis Amiridis, CAMS GA, 15 June 2016

Evaluation of MACC profiles (EU)

• MACC overestimates ext. coeff. during the cold period

• MACC underestimates ext. coeff. during the warm period (high DODs)

• Over ~ 5 km MACC steadily overestimates ext. coeff.

Climate modeling (RegCM):

Fine-tuning using LIVAS dust climatology

Tsikerdelis et al., 2016


CCN/IN-relevant aerosol parameters

Mamouri and Ansmann, ACP 2016

Pure Dust Separation

+

Ext. Coef. →Ae

roso

l Pa

rtic

le C

on

cen

tra

tio

n APC=const*AECd



Ongoing validation of CCN/IN retrievals from remote sensing:

CATS under-flight of FAAM during ICE-D

First IN retrievals using CALIPSO


Conclusions

EARLINET provides the means and the methods for:

• Advancing space-borne lidar retrievals including CALIPSO, CATS and future

EarthCARE

• Optimizing satellite products using EARLINET-measured intensive properties (e.g.

lidar ratios)

• Providing higher level products using the advanced methodologies developed

within EARLINET and in the framework of the ACTRIS RI (e.g. pure-dust product,

dust concentrations in the fine and coarse mode, CCN/IN particle

concentrations)


CAMS GA Space Lidars by Amiridis

Environment

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