Long‐term satellite‐based datasets of atmospheric water vapour derived within CM SAF

1. Long‐term satellite‐based datasets of atmospheric water vapour derived within CM SAF Martin Stengel, Marc Schröder,Nathalie Courcoux, Karsten Fennig, Rainer Hollmann

2. Outline • CM SAF overview • CM SAF ATOVS datasets (processing, examples, validation) • CM SAF SSM/I datasets (processing, examples, validation) • Summary and future activities

3. Outline • CM SAF overview • CM SAF ATOVS datasets (processing, examples, validation) • CM SAF SSM/I datasets (processing, examples, validation) • Summary and future activities

4. CM SAF overview • EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF)

5. CM SAF overview CM-SAF observation datasets mitigation adaptation climate trends and variability processes understanding projection modelling validation / improvement • CM SAF’s role in climate monitoring and research

6. CM SAF overview • The aim of the Satellite Application Facility on Climate Monitoring is to generate, archive and distribute widely recognized high-quality satellite-derived products and services relevant for climate monitoring. • CM SAF provides medium- and long-term term cloud, radiation, water vapour and temperature products derived from different instruments (Schulz et al., 2009). • CM SAF water vapour products: ATOVS, SSM/I • CM SAF data products can be distinguished in operational monitoring products and retrospectively produced data sets.

7. CM SAF overview • Operational monitoring products are disseminated with high timeliness (max 8 weeks after the obs.) to support operational climate monitoring applications of national meteorological and hydrological services. • Because of the timeliness requirement it is not possible to monitor inter-annual variability and trends. Bias error due to orbit shift and decay, as well as inter-satellite biases are not corrected for the operational products. • For the retrospective produced data sets errors due orbit changes and inter-satellite biases are minimized. • In general, CM SAF humidity products have to meet the service specifications that are defined for each products. The service specifications compliance is assessed on a regular basis, e.g. by validation against radiosonde observations of the GCOS Upper Air Network (GUAN).

8. Outline • CM SAF overview • CM SAF ATOVS datasets (processing, examples, validation) • CM SAF SSM/I datasets (processing, examples, validation) • Summary and future activities

9. ATOVS processing • ATOVS instruments: HIRS/3, AMSU-A/B, MHS, HIRS/4 • CM SAF ATOVS products: • total columnar water vapour • layered columnar water vapour (5 tropospheric layers) • Time coverage: 01/01/2004 - today • Spatial resolution: (90 km)² • Products are available as global daily and monthly means. • Processing system: The ATOVS level l1d data generated by the ATOVS and AVHRR Processing Package (AAPP) are used as input for the IAPP (Lee at al., 2000). • Output of the Deutscher Wetterdienst Global-Modell (GME) are used as first guess input to the retrieval. • A Kriging routine is used to determine daily and monthly means on a global grid from the swath based retrievals, as well as uncertainties estimates. (Lindau and Schröder, 2010) • Advantage: land and sea, day and night, clear-sky and cloudy regions

10. ATOVS processing • Layer definitions: • Satellites and AAPP/IAPP versions used: Table: Layer definitions for ATOVS water vapour and temperature products. Table: Summary of the different versions of the CM SAF ATOVS products with the correspondingdates, software and hardware updates, as well as the updates in the satellite observations used.

11. ATOVS example • Layered vertically integrated water vapour for the 5 layers. Monthly means for July 2005.

12. ATOVS example TPW kgm-2 Observations per grid Extra daily standard deviation kgm-2 • TCWV, number of observations, extra daily standard deviation October 2004.

13. ATOVS validation • Comparison ATOVS TCWV vs. GUAN radiosondes Fig: Time series of the bias and bias corrected RMSEof ATOVS TPW against GUAN radiosondes.

14. ATOVS validation • Comparison ATOVS LCWV vs. GUAN radiosondes Fig: Time series of the bias (left) and bias corrected RMSE (right) of ATOVS LPW 1-5 fromATOVS and GUAN radiosondes.

15. ATOVS validation ocean land • Work done in the frame of a federate activity by Claudia Stubenrauch, LMD Open symbols: July Plain symbols: January TOVS-B1987-1995 AIRS-L22003-2009 AIRS-L22008/2009ATOVS2008/2009 (IAPP)‏

16. Outline • CM SAF overview • CM SAF ATOVS datasets (processing, examples, validation) • CM SAF SSM/I datasets (processing, examples, validation) • Summary and future activities

17. CM SAF SSM/I processing • Transition of HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite data; http://www.hoaps.org/) into CM-SAF • CM SAF SSM/I product: 20-year Thematic Climate Data Record (TCDR) of total column integrated water vapour derived from SSM/I • Satellites used: F08, F10, F11, F13, F14, F15 • Radiance homogenization, reference sensor F11 • Statistical retrieval (Schlüssel, P. and Emery W.J., 1990) • A Kriging routine is used to determine daily and monthly means on a global grid from the swath based retrievals, as well as uncertainties estimates. (Lindau and Schröder, 2010) • Advantages: day and night, clear-sky and cloudy regions • Disadvantage: over ocean only

18. CM SAF SSM/I example • Example Fig.EUMETSAT CM SAF SSM/I derived total column water vapour (top) and associated variability (bottom), averaged over the time series from 1987-2006.

19. CM SAF SSM/I evaluation • Evaluation against Wentz (RSS) results (Sohn and Smith, 2003)

20. CM SAF SSM/I evaluation • Evaluation against Wentz (RSS) results:

21. CM SAF SSM/I evaluation • Evaluation against ATOVS results

22. CM SAF SSM/I application • Comparison to NWP

23. CM SAF SSM/I application • Trend analysis Fig. Trends in total column water vapour over the ice-free ocean determined from CM SAF SSM/I derived TCWV.

24. Outline • CM SAF overview • CM SAF ATOVS datasets (processing, examples, validation) • CM SAF SSM/I datasets (processing, examples, validation) • Summary and future activities

25. Summary and Outlook • CM SAF ATOVS: • The ATOVS humidity products exhibit high quality (comparisons against GUAN stations). Comparison against other data sets are also promising. • Reprocessing from 1998 to now is ongoing work. • Updated, constant retrieval system • Time period will be extended with 1998 today • Switch from GME to ERA-Interim • SNO to be used for homogenization of L1 radiances • CM SAF SSM/I: • Provides a highly accurate dataset enabling long-term monitoring of TCWV over ocean • Reprocessing will be done using improved SSM/I FCDR • Improved satellite sensor calibration and intercalibration • FCDR is also used in other projects, e.g. ESA DUE GlobVapour • Possibly 1D-Var system used in the future (retrieval error estimates)* • Extension of time period covered by including SSMIS sensors*

26. Li, J., W. Wolf, W. P. Menzel, W. Zhang, H.-L. Huang, and T. H. Achtor, 2000: Global soundings of the atmosphere from ATOVS measurements: The algorithm and validation. J. Appl. Meteo., 39, 1248-1268. • Lindau R., M. Schröder, 2010, Algorithm Theoretical Basis Document: Objective analysis (Kriging) for water vapour. CM SAF ATBD, Ref Nr. SAF/CM/DWD/ATBD/KRIGING, version 1.1, 25 June 2010. • Schulz, J. and P. Albert, H.D. Behr. D. Caprion,, H. Deneke, S. Dewitte, B. Dürr, P. Fuchs, A. Gratzki, P. Hechler, P. et al., 2009: Operational climate monitoring from space: the EUMETSAT Satellite Application Facility on Climate Monitoring (CM-SAF), Atmos. Chem. Phys., 9, 1-23. • Schluessel, P. and Emery W.J., 1990: Atmospheric water-vapor over oceans from SSM/I measurements, International Journal of Remote Sensing 11/5, 753-766. • Sohn, B.J. and E.A. Smith 2003: Explaining sources of discrepancy in SSM/I water vapour algorithms. Journal of climate, 16, 3229-3255. www.cmsaf.eu Thank you