Harmonized dataset of ozone profiles from satellite limb and occultation measurements

Sofieva, Viktoria; Rahpoe, N.; Tamminen, J.; Kyrölä, E.; Kalakoski, Niilo; Weber, Mark; Rozanov, A.; Savigny, Christian von; Laeng, Alexandra; Clarmann, T. von; Stiller, G. P.; Lossow, Stefan; Degenstein, D. A.; Bourassa, A. E.; Adams, C.; Roth, Chris; Lloyd, Nicholas; Bernath, P. F.; Hargreaves, Robert J.; Urbán, J.; Murtagh, Donal; Hauchecorne, Alain; Dalaudier, Francis; Roozendaël, Michel Van; Kalb, N.; Zehner, Claus

doi:10.5194/essd-5-349-2013

Cited by 51 publications

(52 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GOMOS has performed about 150-200 nighttime occultations per day with global coverage. Typical examples of GOMOS data coverage and distribution over the globe are shown, for example, in Bertaux et al (2010), Kyrölä et al (2010), Sofieva et al (2013), and Tamminen et al (2010). The vertical resolution (including the smoothing properties of the inversion) of GOMOS ozone profiles is 2 km below 30 km and 3 km above 40 km; it is the same for all occultations.…”

Section: F Sofieva Et Al: Validation Of Gomos Ozone Precision Esmentioning

confidence: 95%

Validation of GOMOS ozone precision estimates in the stratosphere

et al. 2014

Self Cite

View full text Add to dashboard Cite

Abstract. Accurate information about uncertainties is required in nearly all data analyses, e.g., inter-comparisons, data assimilation, combined use. Validation of precision estimates (viz., the random component of estimated uncertainty) is important for remote sensing measurements, which provide the information about atmospheric parameters by solving an inverse problem. For the Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument, this is a real challenge, due to the dependence of the signal-to-noise ratio (and thus precision estimates) on stellar properties, small number of self-collocated measurements, and growing noise as a function of time due to instrument aging. The estimated ozone uncertainties are small in the stratosphere for bright star occultations, which complicates validation of precision values, given the natural ozone variability.In this paper, we discuss different methods for geophysical validation of precision estimates and their applicability to GOMOS data. We propose a simple method for validation of GOMOS precision estimates for ozone in the stratosphere. This method is based on comparisons of differences in sample variance with differences in uncertainty estimates for measurements from different stars selected in a region of small natural variability.For GOMOS, the difference in sample variances for different stars at tangent altitudes 25-45 km is well explained by the difference in squared precisions, if the stars are not dim. Since this is observed for several stars, and since normalized χ 2 is close to 1 for these occultations in the stratosphere, we conclude that the GOMOS precision estimates are realistic in occultations of sufficiently bright stars. For dim stars, errors are overestimated due to improper accounting of the dark charge correction uncertainty in the error budget. The proposed method can also be applied to stratospheric ozone data from other instruments, including multi-instrument analyses.

show abstract

Section: F Sofieva Et Al: Validation Of Gomos Ozone Precision Esmentioning

confidence: 95%

Validation of GOMOS ozone precision estimates in the stratosphere

et al. 2014

Self Cite

View full text Add to dashboard Cite

show abstract

“…To achieve this goal, ground-based, balloon-borne, airborne and satellite instruments have been used to monitor ozone abundances in the atmosphere during the last decades. For satellite instruments, different observation techniques including solar/stellar occultation measurements (e.g., SAGE -Stratospheric Aerosol and Gas Experiment (McCormick et al, 1989); HALOE -Halogen Occultation Experiment (Russell et al, 1994); ACE -Atmospheric Chemistry Experiment (McElroy et al, 2007); GOMOSGlobal Ozone Monitoring by Occultation of Stars (Bertaux et al, 2010)), limb scatter/emission measurements (e.g., MLS -Microwave Limb Sounder (Waters et al, 2006); MIPAS -Michelson Interferometer for Passive Atmospheric Sounding (Fischer et al, 2008); OSIRIS -Optical Spectrograph and InfraRed Imager System (Llewellyn et al, 2004)) and nadir measurements (e.g., GOME/GOME2 -Global Ozone Monitoring Experiment Callies et al, 2000); OMI -Ozone Monitoring Instrument (Levelt et al, 2006); IASI -Infrared Atmospheric Sounding Interferometer (Clerbaux et al, 2009)) are used (see, e.g., Sofieva et al, 2013;Hassler et al, 2014, and references therein). The passive imaging spectrometer used in this study, SCIA-MACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY), provided vertical distributions of atmospheric trace gases by employing the limb-scattering measurement technique (Burrows et al, 1995;Bovensmann et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

Global validation of SCIAMACHY limb ozone data (versions 2.9 and 3.0, IUP Bremen) using ozonesonde measurements

Jia¹,

Rozanov²,

Ladstätter-Weißenmayer³

et al. 2015

Atmos. Meas. Tech.

Self Cite

View full text Add to dashboard Cite

Abstract. In this paper, the latest SCIAMACHY limb ozone scientific vertical profiles, namely the current V2.9 and the upcoming V3.0, are extensively compared with ozonesonde data from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) database. The comparisons are made on a global scale from 2003 to 2011, involving 61 sonde stations. The retrieval processors used to generate V2.9 and V3.0 data sets are briefly introduced. The comparisons are discussed in terms of vertical profiles and stratospheric partial columns. Our results indicate that the V2.9 ozone profile data between 20 and 30 km are in good agreement with ground-based measurements, with less than 5 % relative differences in the latitude range of 90 • S-40 • N (with the exception of the tropical Pacific region, where an overestimation of more than 10 % is observed), which corresponds to less than 5 DU partial-column differences. In the tropics the differences are within 3 %. However, this data set shows a significant underestimation northwards of 40 • N (up to ∼ 15 %). The newly developed V3.0 data set reduces this bias to below 10 % while maintaining a good agreement southwards of 40 • N with slightly increased relative differences of up to 5 % in the tropics.

show abstract

“…Creating an accurate record of stratospheric ozone profiles is a non-trivial task and much work has been done at every stage, from design, construction, and operation during flight, to post-processing and combining datasets into composites McPeters et al, 2013;Sofieva et al, 2013;Sioris et al, 2014;Froidevaux et al, 2015;Davis et al, 2016). Recently, several composites were published by multiple groups in connection with the SI2N initiative (SPARC (Stratosphere-troposphere Processes And their Role in Climate)/IO3C (International Ozone Commission)/IGACO-O3 (Integrated Global Atmospheric Chemistry ObservationsOzone)/NDACC (Network for the Detection of Atmospheric Composition Change)) .…”

mentioning

confidence: 99%

Reconciling differences in stratospheric ozone composites

et al. 2017

View full text Add to dashboard Cite

Abstract. Observations of stratospheric ozone from multiple instruments now span three decades; combining these into composite datasets allows long-term ozone trends to be estimated. Recently, several ozone composites have been published, but trends disagree by latitude and altitude, even between composites built upon the same instrument data. We confirm that the main causes of differences in decadal trend estimates lie in (i) steps in the composite time series when the instrument source data changes and (ii) artificial sub-decadal trends in the underlying instrument data. These artefacts introduce features that can alias with regressors in multiple linear regression (MLR) analysis; both can lead to inaccurate trend estimates. Here, we aim to remove these artefacts using Bayesian methods to infer the underlying ozone time series from a set of composites by building a joint-likelihood function using a Gaussian-mixture density to model outliers introduced by data artefacts, together with a data-driven prior on ozone variability that incorporates knowledge of problems during instrument operation. We apply this Bayesian self-calibration approach to stratospheric ozone in 10 • bands from 60 • S to 60 • N and from 46 to 1 hPa (∼ 21-48 km) for 1985-2012. There are two main outcomes: (i) we independently identify and confirm many of the data problems previously identified, but which remain unaccounted for in existing composites; (ii) we construct an ozone composite, with uncertainties, that is free from most of these problems -we call this the BAyeSian Integrated and Consolidated (BASIC) composite. To analyse the new BASIC composite, we use dynamical linear modelling (DLM), which provides a more robust estimate of long-term changes through Bayesian inference than MLR. BASIC and DLM, together, provide a step forward in improving estimates of decadal trends. Our results indicate a significant recovery of ozone since 1998 in the upper stratosphere, of both northern and southern midlatitudes, in all four composites analysed, and particularly in the BASIC composite. The BASIC results also show no hemispheric difference in the recovery at midlatitudes, in contrast to an apparent feature that is present, but not consistent, in the four composites. Our overall conclusion is that it is possible to effectively combine different ozone composites and account for artefacts and drifts, and that this leads to a clear and significant result that upper stratospheric ozone levels have increased since 1998, following an earlier decline.

show abstract

Harmonized dataset of ozone profiles from satellite limb and occultation measurements

Cited by 51 publications

References 38 publications

Validation of GOMOS ozone precision estimates in the stratosphere

Validation of GOMOS ozone precision estimates in the stratosphere

Global validation of SCIAMACHY limb ozone data (versions 2.9 and 3.0, IUP Bremen) using ozonesonde measurements

Reconciling differences in stratospheric ozone composites

Contact Info

Product

Resources

About