CEOS ACC-8, Columbia, MD, April 18-19, 2012

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

CEOS ACC-8, Columbia, MD, April 18-19, 2012

Smoothing and sampling issues affecting data comparisons

1. Intro: The ideal observation operator?2. Illustrations

1. Assessment of smoothing errors2. Interpretation of comparisons3. Optimised co-location criteria4. Trace-tracer correlations, hydrogen budget…

3. 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.

CEOS ACC-8, Columbia, MD, April 18-19, 2012

The ideal observation operator?

For ideal co-location criteria and ideal data assimilation…

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The ideal observation operator?Air masses probed by satellites and by NDACC ground-based

instruments

Tatmossmoothing AASAAf ).().( 21.21 in the vertical AND horizontal dimensions

CEOS ACC-8, Columbia, MD, April 18-19, 2012

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.

CEOS ACC-8, Columbia, MD, April 18-19, 2012

von Clarmann et al, AMT, 2, 47-54, 2009

Horizontal smoothing by MIPAS: O3 2-D horizontal Averaging Kernels for a 1D profile

retrieval

MIPAS processor settings ESA IPF 4.61/nominal mode

CEOS ACC-8, Columbia, MD, April 18-19, 2012

Horizontal smoothing by MIPAS: T and H2O

2-D horizontal Averaging Kernels for a 1D profile retrieval

von Clarmann et al, AMT, 2, 47-54, 2009

MIPAS processor settings ESA IPF 4.61/nominal mode

CEOS ACC-8, Columbia, MD, April 18-19, 2012

Illustrations (1)

Smoothing errors

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Smoothing error for O3 column measurements –

Ground-based zenith-sky observation at twilightTarawa (Kiribati, 1°N / 173°E)

Dumont d’Urville (French Antarctica, 66°S / 140°E)

Kerguelen (Indian Ocean, 49°S / 70°E)

Lambert, ULB, 2006

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Interpretation of comparisons

Illustrations (2)

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Method described in Section 4.1 of Cortesi et al., ACP 2007

Error budget of a data comparison Error budget of MIPAS validation vs. ozonesondes

CEOS ACC-8, Columbia, MD, April 18-19, 2012

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

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Lambert et al., ISSI, 2012

Error budget of a data comparison Sampling differences between MIPAS and

radiosondes

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Lambert et al., ISSI, 2012

Error budget of a data comparison

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Co-location criteria

Illustrations (3)

CEOS ACC-8, Columbia, MD, April 18-19, 2012

UVVIS data courtesy: CNRS / UNESP (Bauru)and NIWA (Lauder)

Co-location for satellite NO2 validationSelection 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

Lambert et al., EUMETSAT O3M-SAF TN, 2007

CEOS ACC-8, Columbia, MD, April 18-19, 2012

Co-location for satellite NO2 validationSelection based on observation operators

UVVIS data courtesy: CNRS / UNESP (Bauru)and NIWA (Lauder)

Bauru, Brazil (22°S) – Zenith-sky air mass is over the Atlantic

Lauder, New Zealand (45°S) – Pure stratospheric signal => meridian gradients, diurnal cycle

Lambert et al., EUMETSAT O3M-SAF TN, 2007

CEOS ACC-8, Columbia, MD, April 18-19, 2012

Tracer-tracer correlations, hydrogen budget…

Illustrations (4)

CEOS ACC-8, Columbia, MD, April 18-19, 2012Lambert et al., ISSI, 2012 (with figures adapted from von Clarmann et al., AMT 2009)

Tracer-tracer correlations and hydrogen budget

CEOS ACC-8, Columbia, MD, April 18-19, 2012

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 ?

CEOS ACC-8, Columbia, MD, April 18-19, 2012

THANK YOU !