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SCIAMACHY Water Vapour Retrieval using AMC-DOAS

SCIAMACHY Water Vapour Retrieval using AMC-DOAS. S. Noël , M. Buchwitz, H. Bovensmann, J. P. Burrows Institute of Environmental Physics/Remote Sensing University of Bremen, Germany. The AMC-DOAS Retrieval Method. “Air Mass Corrected” (AMC-)DOAS based on well-known DOAS method:

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SCIAMACHY Water Vapour Retrieval using AMC-DOAS

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  1. SCIAMACHY Water Vapour Retrieval using AMC-DOAS S. Noël, M. Buchwitz, H. Bovensmann, J. P. Burrows Institute of Environmental Physics/Remote SensingUniversity of Bremen, Germany

  2. 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

  3. SCIAMACHY and GOME H2O Columns • SCIAMACHY has higher spatial resolution than GOME (~ 30 km x 60 km) • Advantage of VIS spectral region:Retrievals overland and ocean possible (unlike MW sensors) • AMC-DOAS method requires no calibration with external sources • Independent data source

  4. 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

  5. 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

  6. 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)

  7. 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)

  8. 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

  9. 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

  10. 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

  11. ECMWF Monthly Mean October 2003

  12. SCIAMACHY Monthly Mean October 2003 Preliminary data!

  13. Difference SCIAMACHY - ECMWF Preliminary data!

  14. 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

  15. First Comparisons with AATSR and MWR AATSR MWR Preliminary data! Preliminary data! • 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

  16. Summary & Conclusions • SCIAMACHY “visible” H2O columns agree well with correlative data • High scatter (~ 0.5 g/cm2 ), mainly due to atmospheric variability • Validation 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 independentglobal water vapour data set

  17. 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.

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