Ozone cci experience on producing and assessing harmonised...
Transcript of Ozone cci experience on producing and assessing harmonised...
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Ozone_cci experience on producing and assessing harmonised long-
term ECV series
7th CCI Collocation meeting, ESA/ESRIN, Frascati, 4-6 October 2016
M. Van Roozendael, BIRA-IASB on behalf of the Ozone_cci consortium
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Outline
• GCOS requirements for ozone
• Building consistent and stable multi-sensor data products
Ø The total ozone case
• Assessing drift and biases on existing multi-sensor data sets
Ø The limb ozone profile case
• Summary and conclusion
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GCOS requirements for ozone
Target Requirements
Variable/ Parameter Horizontal Resolution
Vertical Resolution
Temporal Resolution Accuracy Stability
Ozone profile in upper stratosphere and mesosphere
100-200 km 3 km Daily 5-20 % < 1%
Ozone profile in upper troposphere and lower stratosphere
100-200 km 1-2 km 4h 10 % 1%
Total ozone 20-50 km N/A 4h Max (2%; 5 DU) < 1%
Tropospheric ozone 20-50 km 5km 4h 10-15% 1%
GCOS-154, Dec 2011
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Total ozone case • Requirement on stability: < 1%/decade • Sensors: GOME, SCIAMACHY, GOME-2, OMI, OMPS, …
Nadir UV hyperspectral spectrometers, all belonging to the same family of instruments but with different design and different individual performances
• Approach adopted for CCI: 1. Use single retrieval baseline applicable to all sensors à direct-
fitting scheme selected based on Round-Robin conducted ahead of CCI programme
2. Apply level-1 corrections on individual sensors before level-2 processing (soft-calibrations)
3. Further adjust (small) inter-sensor residual bias before merging
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Level-1 issues
• Different sensors treated with identical retrieval baseline sometimes (still) show significant discrepancies
• Problem is sensor-dependent (largest for SCIAMACHY) • Related to level-1 errors • Two types of radiance errors
• Broadband
• Spectral features • Erroneous key data (e.g. ISRF) • All can affect retrievals
à Need for more corrections
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Dynamical adjustment of ISRF • ISRF (slit function) is a critical parameter for trace gas retrieval • Measured in the lab (pre-launch), but can evolve in-flight due to
instrumental instabilities • Dynamical adjustment as part of the level-2 based on analysis of
solar lines width, impact ozone product at percent level
Dikty and Richter, 2011
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Soft-calibration of level-1 (1) • Principle: use simulated radiances over well characterised
reference sites to identify systematic radiance errors
Lerot et al., 2014
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Soft-calibration of level-1 (2)
• Examples of level-1 systematic errors identified and corrected in a level-2 pre-processing step
GOME radiance degradation
East Nadir West
SCIAMACHY spectral features
Lerot et al., 2014
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GOME, SCIAMACHY, GOME-2 reprocessing
• Full reprocessing of 3 sensors show excellent consistency with independent NASA SBUV v8.6 data set
• Still small (~1%) residual biases between SCIAMACHY and other sensors at high latitudes
• OMI recently added to the picture, shows excellent stability in time without soft-calibration need (à new reference)
• Phase-2 level-2 reprocessing currently ongoing
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Data merging step
• Additional empirical small corrections applied by reference to one sensor (GOME)
• Merging to create on single multi-sensor level-3 data sets (GTO-ECV CCI)
Coldewey–Egbers et al., 2015
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Koukouli et al., 2015
Chiou et al., 2014
Final assessment (validation)
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Limb ozone profile case • Stability requirements in stratosphere: < 1%/decade • Sensors: MIPAS, SCIAMACHY, GOMOS, OSIRIS, SMR,
ACE, MLS Large variety of instrument designs and measurement concepts (UV, TIR, MW), limb scattering, limb emission, solar and star occultation
• Approach adopted for CCI: q Minimise impact of inter-sensor bias on trend analysis through
use of anomalies in ozone (instead of vmr) – cf. WMO report q Systematic analysis of drift and biases to characterise the
stability performance of individual sensors Ø Pair-wise comparison of sensors Ø Comparison against independent reference data sets (validation)
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Strength of ozone anomalies
Remove biases but not drifts…
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Bias and drift analysis
Drift analysis limited in significance by length of data series (10 years)
Drifts smallest in 25-40 km altitude range.
altit
ude
(km
)
From (Rahpoe et al., 2015): 30S -30N
bias ( %) drift ( %/dec)
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SCIAMACHY
• Bias in US + LS depends on profile representation
IUP V2.9
ESA/SGP 5.02
Hubert et al., 2016
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Overview Level-2 CRDP decadal drift
Unphysical? to be confirmed (SAGE II, ACE-FTS)
Hubert et al., 2016
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Overview drift assessment
Hubert et al. (2016), Atmos. Meas. Tech., 9(6), 2497–2534.
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Summary and conclusion
• Tracking drift and bias sources is an essential part of the work for scientists working on CDR generation
• There is generally not one single and generic approach to drift and bias minimisation, instead a combination of different ‘good practices’:
• Algorithm harmonisation and FCDR (level-1) understanding should always be the first step where possible
• Data set customisation can be useful for trend analysis (cf. bias-removal through use of ozone anomalies)
• Do not stick to one method but think about multiple ways to assess drifts and biases (is there a convergence?)