Influence of Absorption Cross-Sections on Retrieving the Ozone Vertical Distribution at the Siberian Lidar Station

The purpose of this paper is to study how the application of different sets of absorption cross-sections influence the ozone vertical distribution (OVD) in the upper layers of the troposphere—stratosphere in the altitude range ~(5–45) km, using a differential absorption lidar (DIAL), operating at the sensing wavelengths 299/341 nm and 308/353 nm. We analyzed the results of lidar measurements of OVD obtained in 2021 using meteorological data from the IASI/MetOp satellite at the Siberian Lidar Station (SLS). The retrieval was performed using the data of four groups concerning the absorption cross-sections: Gorshelev et al., Malicet et al., SCIAMACHY, and GOME. To estimate how the absorption cross-sections influence the OVD retrieval from lidar measurements, we calculated the average deviations between the profiles retrieved using different sets both in a particular case on 2 January 2021 and throughout 2021. Our study showed that, out of the four absorption cross-section sets, the data of Gorshelev et al. should be used for long-term lidar monitoring of the ozone. These data show a more discrete dependence of the absorption cross-sections on the temperature values, which is more urgent for tropospheric and stratospheric ozone measurements.

Ground-based assessment of the bias and long-term stability of fourteen limb and occultation ozone profile data records

The ozone profile records of a large number of limb and occultation satellite instruments are widely used to address several key questions in ozone research. Further progress in some domains depends on a more detailed understanding of these data sets, especially of their long-term stability and their mutual consistency. To this end, we made a systematic assessment of fourteen limb and occultation sounders that, together, provide more than three decades of global ozone profile measurements. In particular, we considered the latest operational Level-2 records by SAGE II, SAGE III, HALOE, UARS MLS, Aura MLS, POAM II, POAM III, OSIRIS, SMR, GOMOS, MIPAS, SCIAMACHY, ACE-FTS and MAESTRO. Central to our work is a consistent and robust analysis of the comparisons against the ground-based ozonesonde and stratospheric ozone lidar networks. It allowed us to investigate, from the troposphere up to the stratopause, the following main aspects of satellite data quality: long-term stability, overall bias, and short-term variability, together with their dependence on geophysical parameters and profile representation. In addition, it permitted us to quantify the overall consistency between the ozone profilers. Generally, we found that between 20-40 km the satellite ozone measurement biases are smaller than ±5 %, the short-term variabilities are less than 5-12% and the drifts are at most ±5% decade^-1 (or even ±3 % decade^-1 for a few records). The agreement with ground-based data degrades somewhat towards the stratopause and especially towards the tropopause where natural variability and low ozone abundances impede a more precise analysis. In part of the stratosphere a few records deviate from the preceding general conclusions; we identified biases of 10% and more (POAM II and SCIAMACHY), markedly higher single-profile variability (SMR and SCIAMACHY), and significant long-term drifts (SCIAMACHY, OSIRIS, HALOE, and possibly GOMOS and SMR as well). Furthermore, we reflected on the repercussions of our findings for the construction, analysis and interpretation of merged data records. Most notably, the discrepancies between several recent ozone profile trend assessments can be mostly explained by instrumental drift. This clearly demonstrates the need for systematic comprehensive multi-instrument comparison analyses.

Ground-based assessment of the bias and long-term stability of fourteen limb and occultation ozone profile data records

The ozone profile records of a large number of limb and occultation satellite instruments are widely used to address several key questions in ozone research. Further progress in some domains depends on a more detailed understanding of these data sets, especially of their long-term stability and their mutual consistency. To this end, we made a systematic assessment of fourteen limb and occultation sounders that, together, provide more than three decades of global ozone profile measurements. In particular, we considered the latest operational Level-2 records by SAGE II, SAGE III, HALOE, UARS MLS, Aura MLS, POAM II, POAM III, OSIRIS, SMR, GOMOS, MIPAS, SCIAMACHY, ACE-FTS and MAESTRO. Central to our work is a consistent and robust analysis of the comparisons against the ground-based ozonesonde and stratospheric ozone lidar networks. It allowed us to investigate, from the troposphere up to the stratopause, the following main aspects of satellite data quality: long-term stability, overall bias, and short-term variability, together with their dependence on geophysical parameters and profile representation. In addition, it permitted us to quantify the overall consistency between the ozone profilers. Generally, we found that between 20-40 km the satellite ozone measurement biases are smaller than ±5 %, the short-term variabilities are less than 5-12% and the drifts are at most ±5% decade^-1 (or even ±3 % decade^-1 for a few records). The agreement with ground-based data degrades somewhat towards the stratopause and especially towards the tropopause where natural variability and low ozone abundances impede a more precise analysis. In part of the stratosphere a few records deviate from the preceding general conclusions; we identified biases of 10% and more (POAM II and SCIAMACHY), markedly higher single-profile variability (SMR and SCIAMACHY), and significant long-term drifts (SCIAMACHY, OSIRIS, HALOE, and possibly GOMOS and SMR as well). Furthermore, we reflected on the repercussions of our findings for the construction, analysis and interpretation of merged data records. Most notably, the discrepancies between several recent ozone profile trend assessments can be mostly explained by instrumental drift. This clearly demonstrates the need for systematic comprehensive multi-instrument comparison analyses.