SCIAMACHY Water Vapour Retrieval using AMC-DOAS

Institut für Umweltphysik/Fernerkundung Physik/ElektrotechnikFachbereich 1

[email protected]

SCIAMACHY Water Vapour Retrieval using

AMC-DOASS. Noël, M. Buchwitz, H. Bovensmann, J. P. Burrows

Institute of Environmental Physics/Remote SensingUniversity of Bremen, Germany


[email protected]

The AMC-DOAS Retrieval Method• “Air Mass Corrected” (AMC-)DOAS based on well-known DOAS method:

– Uses only differential structures of sun-normalised radiances– Numerically fast algorithm

• Main differences to standard DOAS:– Parameterisation of saturation effect:

Non-linear dependence of absorber amount from absorption depth

– Air Mass Factor (AMF) correctionfrom O2 absorption in same fitting window:

Inherent data quality check to mask out too cloudy ground pixels, etc.• Has been applied successfully to GOME and SCIAMACHY measurements


[email protected]

SCIAMACHY and GOME H2O Columns

• SCIAMACHY has higher spatial resolution than GOME (~ 30 km x 60 km)

• Advantage of VIS spectral region:Retrievals over land and ocean possible (unlike MW sensors)

• AMC-DOAS method requires no calibration with external sources

Independent data source


[email protected]

AMC-DOAS Results• Analysis of all available SCIAMACHY nadir data for the year 2003

(Level 1 NRT and consolidated data)• Automatic retrieval for all 2004 SCIAMACHY Level 1 NRT data

(see also: http://www.iup.physik.uni-bremen.de)

• Remarks:– Not all data are available; larger gaps especially in November 2003– Inclusion of unconsolidated data may influence weighting of individual

measurements– Insufficient radiometric calibration may have an influence on the data quality

(although expected to be small)– Always the same (specially calibrated) solar reference spectrum used for

SCIAMACHY retrieval (provided by J. Frerick, ESA)– No correction for surface elevation

• All data have been gridded to 0.5° x 0.5° for the comparison with SSM/I and ECMWF results


[email protected]

SSM/I H2O Columns(27 January 2003)

• SSM/I gridded Integrated Water Vapour data provided by GHRC

• Only descending part of DMSP F-14 orbit (equator crossing at ~ 0800 LT)

• Only data over ocean available


[email protected]

ECMWF H2O Columns(27 January 2003)

• Operational daily analysis data provided by ECMWF

• Not independent from SSM/I data

• Daily averages derived from 6-hourly values (integrated over height)


[email protected]

SCIAMACHY AMC-DOAS H2O Columns(27 January 2003)

• Regular gaps from alternating limb- nadir measurement mode

• Additional gaps from AMC-DOAS quality check:

– Max. SZA 88° – AMF correction

factor has to be larger than 0.8

(mainly because of clouds)

(swath data)


[email protected]

Correlation (27 January 2003)

SCIA vs.

SSM/I

SCIAvs.

ECMWF

• Good correlation with both SSM/I and ECMWF columns• On average good agreement (better with ECMWF data)• Smaller SCIA columns seem to be lower, higher larger than correlative data• Deviations difficult to quantify because of large scatter


[email protected]

Scatter of Water Vapour Data• Scatter is mainly due to high spatial and temporal variability of water

vapour

• Difficult to compare individual measurements which are (initially) on different temporal and/or spatial scales

• Scatter can not be significantly reduced by averaging more data (but correlation and mean values may improve)

• General problem for validation/verification of water vapour products

Concentrate on long-term analysis of correlation and mean values


[email protected]

Long-Term Deviations

SCIA vs. SSM/I SCIA vs. ECMWF

• Mean deviation with SSM/I: - 0.2 g/cm2

• Mean deviation with ECMWF: - 0.05 g/cm2


[email protected]

ECMWF Monthly Mean October 2003


[email protected]

SCIAMACHY Monthly Mean October 2003

Preliminary data!


[email protected]

Difference SCIAMACHY - ECMWF

Preliminary data!


[email protected]

Comparisons with other ENVISAT Sensors

• Other ENVISAT instruments providing water vapour column data:- MERIS- AATSR- MWR

• Here: First comparisons with AATSR and MWR water vapour data provided by I. Barton, CSIRO, Hobart, Australia

• Advantage of intercomparison: Minimum temporal offset• Disadvantages: Different spatial resolution, ENVISAT products not fully

validated yet• Current limitations:

- AATSR and MWR data not independent- Only sub-satellite track data over ocean (cloud free), only few days


[email protected]

First Comparisons with AATSR and MWR

• First preliminary results, only 4 days analysed up to now (partly limited by availability of SCIAMACHY Level 1b data)

• Agreement with MWR data slightly better than with AATSR

AATSR MWR

Preliminary data!Preliminary data!


[email protected]

Summary & Conclusions• SCIAMACHY “visible” H2O columns agree well with correlative data

• High scatter (~ 0.5 g/cm2 ), mainly due to atmospheric variabilityValidation of water vapour columns difficult

• Mean SCIAMACHY AMC-DOAS water vapour columns typically lower than ECMWF and SSM/I data

• SCIAMACHY monthly means look reasonable; some features need further investigation

• Quite good agreement with first AATSR and MWR water vapour results

SCIAMACHY can provide a new independent global water vapour data set


[email protected]

Acknowledgements• SCIAMACHY data have been provided by ESA.

• SSM/I data have been provided by the Global Hydrology Resource Center (GHRC) at the Global Hydrology and Climate Center, Huntsville, Alabama.

• We thank the European Center for Medium Range Weather Forecasting (ECMWF) for providing us with analysed meteorological fields and our colleagues J. Meyer-Arnek and S. Dhomse for assistance in handling these data.

• MWR and AATSR water vapour data have been provided by I. Barton, Marine Research, Commonwealth Scientific and Industrial Research Organisation, Hobart, Tasmania, Australia.

• This work has been funded by the BMBF via GSF/PT-UKF and DLR-Bonn and by the University of Bremen.

Documents

SCIAMACHY Water Vapour Retrieval using AMC-DOAS