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8 th Atmospheric Composition Constellation Meeting (ACC-8), 18-19 April 2012, Columbia, MD

8 th Atmospheric Composition Constellation Meeting (ACC-8), 18-19 April 2012, Columbia, MD Smoothing and Sampling Issues Affecting Data Comparisons Jean-Christopher Lambert (1)

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8 th Atmospheric Composition Constellation Meeting (ACC-8), 18-19 April 2012, Columbia, MD

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  1. 8th Atmospheric Composition Constellation Meeting (ACC-8), 18-19 April 2012, Columbia, MD Smoothing and Sampling Issues Affecting Data Comparisons Jean-Christopher Lambert (1) with contributions by C. De Clercq (1), Q. Errera (1), J. Granville (1), and T. von Clarmann (2)(1) Belgian Institute for Space Aeronomy, Brussels, Belgium(2) Karlsruhe Institute of Technology, Karlsruhe, Germany

  2. Smoothing and sampling issues affecting data comparisons • Intro: The ideal observation operator? • Illustrations • Assessment of smoothing errors • Interpretation of comparisons • Optimised co-location criteria • Trace-tracer correlations, hydrogen budget… • Conclusion • Reported work funded by EC FP4 ESMOS and SCUVS, EC ECRP4 COSE, BELSPO/ProDEx SECPEA and EC FP6 GEOmon. Continuation within BELSPO/ProDEx A3C and EC FP7 NORS.

  3. The ideal observation operator? For ideal co-location criteria and ideal data assimilation…

  4. in the vertical AND horizontal dimensions The ideal observation operator? Air masses probed by satellites and by NDACC ground-based instruments

  5. Observation operators used in chemical data assimilation Errera et al., Atmos. Chem. Phys., 8, 2008 Underlying assumption: H(x(ti)) reproduces perfectly smoothing and sampling characteristics of the observation.

  6. Horizontal smoothing by MIPAS: O32-D horizontal Averaging Kernels for a 1D profile retrieval MIPAS processor settings ESA IPF 4.61/nominal mode von Clarmann et al, AMT, 2, 47-54, 2009

  7. Horizontal smoothing by MIPAS: T and H2O2-D horizontal Averaging Kernels for a 1D profile retrieval MIPAS processor settings ESA IPF 4.61/nominal mode von Clarmann et al, AMT, 2, 47-54, 2009

  8. Illustrations (1) Smoothing errors

  9. Tarawa (Kiribati, 1°N / 173°E) Kerguelen (Indian Ocean, 49°S / 70°E) Dumont d’Urville (French Antarctica, 66°S / 140°E) Smoothing error for O3 column measurements –Ground-based zenith-sky observation at twilight Lambert, ULB, 2006

  10. Illustrations (2) Interpretation of comparisons

  11. Error budget of a data comparison Error budget of MIPAS validation vs. ozonesondes Method described in Section 4.1 of Cortesi et al., ACP 2007

  12. Error budget of a data comparison Error budget of MIPAS validation vs. lidar Method described in Section 4.1 of Cortesi et al., ACP 2007

  13. Error budget of a data comparison Sampling differences between MIPAS and radiosondes Lambert et al., ISSI, 2012

  14. Error budget of a data comparison Lambert et al., ISSI, 2012

  15. Illustrations (3) Co-location criteria

  16. Co-location for satellite NO2 validation Selection within 500km radius: pollution, meridian gradients, diurnal cycle… Lauder, New Zealand (45°S) – Pure stratospheric signal => meridian gradients, diurnal cycle Bauru, Brazil (22°S) – 500 km radius influenced by pollution from Sao Paulo area UVVIS data courtesy: CNRS / UNESP (Bauru) and NIWA (Lauder) Lambert et al., EUMETSAT O3M-SAF TN, 2007

  17. Co-location for satellite NO2 validation Selection based on observation operators Lauder, New Zealand (45°S) – Pure stratospheric signal => meridian gradients, diurnal cycle Bauru, Brazil (22°S) – Zenith-sky air mass is over the Atlantic UVVIS data courtesy: CNRS / UNESP (Bauru) and NIWA (Lauder) Lambert et al., EUMETSAT O3M-SAF TN, 2007

  18. Illustrations (4) Tracer-tracer correlations, hydrogen budget…

  19. Tracer-tracer correlations and hydrogen budget Lambert et al., ISSI, 2012 (with figures adapted from von Clarmann et al., AMT 2009)

  20. Conclusion • Bias and noise introduced by differences in smoothing and sampling can spoil the value of a data comparison. • The problem is a combined effect of measurement properties (measurement + retrieval) and of atmospheric properties. • The problem can be multi-dimensional. • Observation operators have been/are being published for major remote sensing techniques and a few key molecules. • Consideration of smoothing/sampling issues enables: • Optimization of co-location criteria • Assessment of smoothing errors of individual observation systems • Assessment of discrepancies due to differences in smoothing and sampling of atmospheric field • More compact tracer-tracer correlations, hydrogen budgets… • Propagation of smoothing/sampling properties in L3/L4/merged data sets ?

  21. THANK YOU !

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