page 1 sciamachy validation workshop – bremen - 6-8 december 2004 contribution to the validation...

Sciamachy Validation Workshop – Bremen - 6-8 December 2004

Contribution to the validation of SCIAMACHY scientific data products for

CO, CH4, CO2 and N2O total column amounts using ground-based FTIR

network data.

M. De Mazière, B. Dils, M. Buchwitz, R. De Beek, C. Frankenberg, A. Gloudemans, H. Schrijver, M. van den

Broek et al.

Belgian Institute for Space Aeronomy, Ringlaan 3, B-1180 Brussels, Belgium,

[email protected], [email protected]


Coworkers

at BIRA-IASB

J. GranvilleP. GérardT. Jacobs

J.C. LambertC. Vigouroux

J. Notholt, T. WarnekeInstitute of Environmental Physics, University of Bremen, Germany

T. Blumenstock, S. MikuteitForschungszentrum Karlsruhe, IMK, Germany

E. Mahieu, P. Demoulin, P. DuchateletInstitut d'Astrophysique et de Géophysique, University of Liège, Belgium

J. Mellqvist, A. Strandberg Chalmers University of Technology, Sweden

R. Sussmann, W. StremmeForschungszentrum Karlsruhe, IFU, Germany

H. Fast, R. L. Mittermeier Meteorological Service of Canada (MSC)

T. Kerzenmacher, K. Strong, J.Taylor, A.Wiacek University of Toronto, Canada

S. Wood, D. Smale National Institute for Water and Air Research (NIWA), New-Zealand

D. Griffith, N. JonesUniversity of Wollongong, Australia

C. Rinsland NASA Langley Research Center, USA

Contributing PI’s


Correlative datasetStation Lat N Lon E Alt(m)

NY.ALESUND 78,91 11,88 20

KIRUNA 67,8 20,4 419

HARESTUA 60,22 10,75 580

ZUGSPITZE 47,4 11,1 2964

JUNGFRAUJOCH 46,55 7,98 3580

EGBERT 44,23 -79,78 251

TORONTO 43,7 -79,4 174

IZANA 28 -16 2367

WOLLONGONG -34,45 150,88 30

LAUDER -45,05 169,68 370

ARRIVAL.HEIGHTS -77,85 166,78 190


SCIAMACHY retrieval methods

WFM-DOAS: Weighting Function Modified DOASChannel 8Two windows: CH4 + N2O → 2265.0 – 2280.0 nmCO → 2359.0 – 2370.0 nmCO2 → 1558.0 – 1594.0 nm (channel 6)

IMLM : Iterative Maximum likelihood MethodChannel 8: 2354.00 – 2370.45 nmCH4 and CO from the same window

IMAP : Interactive Maximum A Posteriori-DOASCH4 from channel 6CO from channel 8: 2324.2 – 2334.9 nm


Comparison remarks(FT)IR is the only GB technique to provide correlative total column data –

but number of GB measurements is limited - cf. need clear sky

Comparisons between total column data at high altitude stations;adopted approach: to use altitude-normalised data which is a better compromise for CH4

and N2O than for CO* still in mountainous regions, pixels do not represent uniform elevation

Comparisons at stations situated near the coast (e.g. Wollongong) - cf colocated pixels may be over sea Verify whether SCIA data are over land


Maximizing Data overlap

Maximizing data overlap:

→ polynomial fit through GB data

But no extrapolation!

*Good representation of seasonal variability

(average std= 1-2%, except CO, 10%)

*Loss of information on certain possible short term events


Selection criteria

Data processed for two grids around the GB stations:

Large grid = Lat ± 2.5°

Lon ± 10°

Small grid = Lat ± 2.5°

Lon ± 5°

Sciamachy data cover the jan → okt/nov time periodExcept CH4-IMAP: aug-nov


Data overlap

Global time series for WFMDOAS and GB CO and CH4

(no additional filtering, fixed offset/station, + = GB data)

Large data loss when restricted to point-to-point comparison


Additional Selection criteria

WFM-DOAS: Cloud-free, Over land (altitude > 0), Solar Zenith Angle < 85 deg, Error (fitting) <10% for CH4 and CO2 , < 60% for CO and N2O

IMLM-SRON: Cloud-free, Albedo >=0.01, Error (instrumental) < 2E18 for CH4 (~7%) and <1.5E18 for CO (~70%)

IMAP: No further selection needed for CH4, variance of the fit residual (without weighting) < 0.017, with weighting between 10 and 0.1, error < 7E17 and 30% for CO

Note: Several selection criteria have already been applied to the starting dataset


Timeseries plots

IMLM-SRON


Timeseries plots

WFM-DOAS


Timeseries plots

IMAP


CO

Retrieval algorithm

Average Bias over all stations and time (small grid)

Average Bias over all stations and time (large grid)

WFM-DOAS (24.56 ± 33.22)%

(21.76 ± 30.05)%

IMLM-SRON* (-3.51 ± 82.51)% (-1.56 ± 83.60)%

IMAP (-21.86 ± 27.58)%

(-19.14 ± 26.36)%

Std GB-FTIR 10.56%

*IMLM-SRON data is preliminary dataset!→ further improvement of std likely

Bias = mean (SCIA-polyfitFTIR)/polyfitFTIR


CH4

Retrieval algorithm



WFM-DOAS v4.1 (-1.70 ± 7.93)% (-1.75 ± 7.34)%

IMLM-SRON* (-2.59 ± 15.83)%

(-3.68 ± 18.39)%

IMAP (-4.23 ± 3.54)% (-5.22 ± 4.04)%

Std GB-FTIR 2.09%

*IMLM-SRON data is preliminary dataset!→ further improvement of std likely


Latitude dependence?

CH4_bias as a function of latitude

-14-12-10-8-6-4-202468

-90 -40 10 60

Lat (deg)

Bia

s (%

) WFMDOAS

SRON

IMAP

CH4_bias as a function of latitude

-30

-20

-10

0

10

20

30

-90 -40 10 60

Lat (deg)

Bia

s (%

) WFMDOAS

SRON

IMAP

CO-bias as a function of latitude

-60

-40

-20

0

20

40

60

80

100

120

-90 -40 10 60

Lat (deg)

Bia

s (%

) WFMDOAS

SRON

IMAP

CO-bias as a function of latitude

-60

-40

-20

0

20

40

60

80

100

120

-90 -40 10 60

Lat (deg)

Bia

s (%

) WFMDOAS

SRON

IMAP


CO2 + N2O

Retrieval algorithm



WFM-DOAS CO2 (-11.06 ± 7.42)%

(-11.39 ± 6.64)%

Std GB-FTIR CO2 1.06%

WFM-DOAS N2O (-1.10 ± 26.83)%

(-0.34 ± 26.60)%

Std GB-FTIR N2O 1.75%


Remarks

No information on the time dependence of the bias! Of major importance in determining the accuracy of the

retrieval model, is assessing whether or not the SCIA data ‘follows’ the GB data over time.

No data averaging was performed

Outliner detection and removal (criteria?)

All algorithms, IMLM (correction for dark current over orbits) and IMAP (implementation of the cloud retrieval utility for CO), undergo continuous improvements.


Future work

In depth analysis of the time/lat dependence of the bias

Comparing daily/ weekly/ monthly averages of SCIA and FTIR data

Further investigation of various selection criteria

Comparisons between Ground Based data and Theoretical Models

page 1 sciamachy validation workshop – bremen - 6-8 december 2004 contribution to the validation...

Documents

university of bremen

gb data

nm slide

scia data

nm channel

maximizing data overlap

correlative total column

altitudenormalised data