A tropopause-based a priori for IASI-SOFRID Ozone retrievals: improvements and validation

Abstract

The Metop/IASI instruments provide data for operational meteorology and document atmospheric composition since 2007. IASI Ozone (O 3 ) data have been used extensively to characterize the seasonal and interannual variabilities and the evolution of tropospheric O 3 at the global scale. The SOFRID (SOftware for a Fast Retrieval of IASI Data) is a fast retrieval algorithm that provides IASI O 3 profiles for the whole IASI period. Up to now SOFRID O 3 retrievals (v1.5 and 1.6) were performed 5 with a single a priori profile which resulted in important biases and probably a too low variability. For the first time we have implemented a dynamical a priori profile for spaceborne O 3 retrievals which takes the pixel location, time and tropopause height into account for SOFRID-O3 v3.5 retrievals. In the present study we validate SOFRID-O3 v1.6 and v3.5 with ECC ozonesonde profiles from the global WOUDC database for the 2008-2017 period. Our validation is based on a thorough statistical analysis using Taylor diagrams. Furthermore we compare our retrievals with ozonesonde profiles both smoothed by the IASI averaging 10 kernels and raw. This methodology is essential to evaluate the inherent usefulness of the retrievals to assess O 3 variability and trends. The use of a dynamical a priori largely improves the retrievals concerning two main aspects: (i) it corrects high biases for low-tropospheric O 3 regions such as the southern hemisphere (ii) it increases the retrieved O 3 variability leading to a better agreement with ozonesonde data. Concerning UTLS and stratospheric O 3 the improvements are less important and the biases are very similar for both versions. The SOFRID Tropospheric Ozone Columns (TOC) display no significant drifts (< 2.5%) 15 for the northern hemisphere and significant negative ones (9.5% for v1.6 and 4.3% for v3.5) for the southern hemisphere . We have compared our validation results to those of the FORLI retrieval software from the litterature for smoothed ozonesonde data only. This comparison highlights three main differences: (i) FORLI retrievals contain more theoretical information about tropospheric O 3 than SOFRID (ii) RMSDs are smaller and correlation coefficients are higher for SOFRID than for FORLI (iii) in the northern hemisphere, no significant temporal drift is detected in SOFRID contrarily to FORLI (∼8%).

Impact of IASI thermal infrared measurements on global ozone reanalyses

“…In particular, the difference between IASI-r and ozonesondes columns remains mostly below 10% whereas it It is highly likely that the large improvements in the extra tropics with respect to IASI-a are a consequence of the direct assimilation of IASI L1 radiances, which represents the main upgrade in the methodology. This interpretation is supported also by the results of Barret et al (2020), who found a significant improvement in O 3 columns in the extra tropics when using a dynamical a-priori in the retrieval algorithm instead of the constant one used originally Barret et al (2011). We remind that IASI-a was computed via the assimilation of the O 3 retrievals described in Barret et al (2011) after the application of an empirical bias correction of 10% (Emili et al, 2014;Peiro et al, 2018).…”

“…Within IASI-r, no observational bias correction has been performed and this was not found to be detrimental for the reanalysis. Barret et al (2020) showed that the O 3 prior information used in L2 retrievals was likely a reason for some of the tropospheric biases found previously with IASI assimilation.…”

“…It might be interesting in the future to evaluate the assimilation of improved Level 2 retrievals (Barret et al, 2020) and see wether this also solves the issues encountered previously with IASI-a.…”

Assessment of the contribution of IRS for the characterisation of ozone over Europe

“…Consequently, it measures at 8461 wavelengths (or channels). Thanks to its fine spectral resolution, signal-to-noise ratio and wide spectrum range, it is a precious resource for detecting trace gases like ozone, methane and carbon monoxide, as well as clouds, aerosols and greenhouse gases [Phulpin et al (2002); Clerbaux et al (2009); Hilton et al (2012); Barret et al (2020)].…”