Multi-sensor data merging with stacked neural networks for the creation of satellite long-term climate data records

Loyola, Diego; Coldewey‐Egbers, Melanie

doi:10.1186/1687-6180-2012-91

Cited by 22 publications

(24 citation statements)

References 31 publications

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“…For the evaluation of total column ozone, we use the NIWA combined total column ozone data set over the period 1998-2010 as the reference data set and the data set GOME-type total ozone -essential climate variable (GTO-ECV), combining data from the satellite sensors GOME, SCIAMACHY and GOME-2, as the alternative for the same period (Loyola and Coldewey-Egbers, 2012;Loyola et al, 2009). The NIWA data set is an assimilated database that combines TOMS (Total ozone mapping spectrometer), GOME and SBUV (Solar backscatter ultra-violet radiometer) data.…”

Section: Total Column Ozonementioning

confidence: 99%

Quantitative evaluation of ozone and selected climate parameters in a set of EMAC simulations

et al. 2015

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Abstract. Four simulations with the ECHAM/MESSy Atmospheric Chemistry (EMAC) model have been evaluated with the Earth System Model Validation Tool (ESMValTool) to identify differences in simulated ozone and selected climate parameters that resulted from (i) different setups of the EMAC model (nudged vs. free-running) and (ii) different boundary conditions (emissions, sea surface temperatures (SSTs) and sea ice concentrations (SICs)). To assess the relative performance of the simulations, quantitative performance metrics are calculated consistently for the climate parameters and ozone. This is important for the interpretation of the evaluation results since biases in climate can impact on biases in chemistry and vice versa. The observational data sets used for the evaluation include ozonesonde and aircraft data, meteorological reanalyses and satellite measurements. The results from a previous EMAC evaluation of a model simulation with nudging towards realistic meteorology in the troposphere have been compared to new simulations with different model setups and updated emission data sets in free-running time slice and nudged quasi chemistrytransport model (QCTM) mode. The latter two configurations are particularly important for chemistry-climate projections and for the quantification of individual sources (e.g., the transport sector) that lead to small chemical perturbations of the climate system, respectively. With the exception of some specific features which are detailed in this study, no large differences that could be related to the different setups (nudged vs. free-running) of the EMAC simulations were found, which offers the possibility to evaluate and improve the overall model with the help of shorter nudged simulations. The main differences between the two setups is a better representation of the tropospheric and stratospheric temperature in the nudged simulations, which also better reproduce stratospheric water vapor concentrations, due to the improved simulation of the temperature in the tropical tropopause layer. Ozone and ozone precursor concentrations, on the other hand, are very similar in the different model setups, if similar boundary conditions are used. Different boundary conditions however lead to relevant differences in the four simulations. Biases which are common to all simulations are the underestimation of the ozone hole and the overestimation of tropospheric column ozone, the latter being significantly reduced when lower lightning emissions of nitrogen oxides are used. To further investigate possible other reasons for such bias, two sensitivity simulations with an updated scavenging routine and the addition of a newly proposed HNO 3 -forming channel of the HO 2 + NO reaction were performed. The update in the scavenging routine resulted in a slightly better representation of ozone compared to the reference simulation. The introduction of the new HNO 3 -forming channel significantly reduces the overestimation of tropospheric ozone. Therefore, including the new reaction rate could potentially...

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Section: Total Column Ozonementioning

confidence: 99%

Quantitative evaluation of ozone and selected climate parameters in a set of EMAC simulations

et al. 2015

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“…The GTO merged ozone data record combines those measurements, and a continuous and homogeneous monthly mean time series is generated (Loyola et al, 2009;Loyola and Coldewey-Egbers, 2012). The first GTO version was created using products obtained with the GOME Data Processor (GDP) version 4.x algorithm (Van Roozendael et al, 2006;Lerot et al, 2009;Loyola et al, 2011), which is based on the differential optical absorption spectroscopy approach.…”

Section: Gto Ozone Data Recordmentioning

confidence: 99%

Comparison of profile total ozone from SBUV (v8.6) with GOME-type and ground-based total ozone for a 16-year period (1996 to 2011)

et al. 2014

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Abstract. This paper describes the comparison of the variability of total column ozone inferred from the three independent multi-year data records, namely, (i) Solar Backscatter Ultraviolet Instrument (SBUV) v8.6 profile total ozone, (ii) GTO (GOME-type total ozone), and (iii) ground-based total ozone data records covering the 16-year overlap period (March 1996 It has been found that, on average, the differences in monthly zonal mean total ozone vary between −0.3 and 0.8 % and are well within 1 %.For GTO minus SBUV, the standard deviations and ranges (maximum minus minimum) of the differences regarding monthly zonal mean total ozone vary between 0.6-0.7 % and 2.8-3.8 % respectively, depending on the latitude band. The corresponding standard deviations and ranges regarding the differences in monthly zonal mean anomalies show values between 0.4-0.6 % and 2.2-3.5 %. The standard deviations and ranges of the differences ground-based minus SBUV regarding both monthly zonal means and anomalies are larger by a factor of 1.4-2.9 in comparison to GTO minus SBUV.The ground-based zonal means demonstrate larger scattering of monthly data compared to satellite-based records. The differences in the scattering are significantly reduced if seasonal zonal averages are analyzed.The trends of the differences GTO minus SBUV and ground-based minus SBUV are found to vary between −0.04 and 0.1 % yr −1 (−0.1 and 0.3 DU yr −1 ). These negligibly small trends have provided strong evidence that there are no significant time-dependent differences among these multiyear total ozone data records.Analyses of the annual deviations from pre-1980 level indicate that, for the 15-year period of 1996 to 2010, all three data records show a gradual increase at 30-60 • N from −5 % in 1996 to −2 % in 2010. In contrast, at 50-30 • S and 30 • S-30 • N there has been a levelling off in the 15 years after 1996. The deviations inferred from GTO and SBUV show agreement within 1 %, but a slight increase has been found in the differences during the period 1996-2010.

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“…The preceding GTO-ECV GDP data record (Loyola et al, 2009a;Loyola and Coldewey-Egbers, 2012) is based on GOME, SCIAMACHY, and GOME-2A total ozone columns obtained with the GDP 4.X retrieval algorithm (Van Roozendael et al, 2006;Lerot et al, 2009;Loyola et al, 2011;Hao et al, 2014). The first version of GTO-ECV GDP covered the period from 1995 to 2008, but this has now been extended to June 2013.…”

Section: Gto-ecv Gdpmentioning

confidence: 99%

“…Predecessors of this algorithm are described in Loyola et al (2009a) and Loyola and Coldewey-Egbers (2012). We apply an external adjustment to SCIAMACHY and GOME-2A results with respect to the GOME results in order to account for inter-sensor differences, which possibly remain from the GODFIT_V3 level 2 algorithm, albeit these differences are small and the intersensor consistency is high .…”

Section: Level 3 Algorithm Description and Merging Approachmentioning

confidence: 99%

The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) data record from the ESA Climate Change Initiative

et al. 2015

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Multi-sensor data merging with stacked neural networks for the creation of satellite long-term climate data records

Cited by 22 publications

References 31 publications

Quantitative evaluation of ozone and selected climate parameters in a set of EMAC simulations

Quantitative evaluation of ozone and selected climate parameters in a set of EMAC simulations

Comparison of profile total ozone from SBUV (v8.6) with GOME-type and ground-based total ozone for a 16-year period (1996 to 2011)

The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) data record from the ESA Climate Change Initiative

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