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IASI-A, B and C (Infrared Atmospheric Sounding Interferometer) are nadir looking thermal infrared sensors which are monitoring the atmospheric composition since 2008. Atmospheric Carbon monoxide (CO) is retrieved from IASI radiances with two algorithms: the SOftware for a Fast Retrieval of IASI Data (SOFRID) and the Fast Optimal Retrievals on Layers for IASI (FORLI). The airborne in-situ observations from the In-service Aircraft for a Global Observing System (IAGOS) European Research Infrastructure have been used to validate the IASI CO retrievals. The validation study of IASI CO data performed in 2011 whith IAGOS data was limited to two airports (Frankfurt and Windhoek) and 2 years because of the limited sampling at the other IAGOS sites. The extension of the IAGOS infrastructure during the last decade enables a validation with enough temporal sampling at 33 airports worldwide over the whole IASI-A period (2008–2020). The retrievals provide between 1.5 and 3 independent pieces of information about the CO vertical profile and we have selected to validate the surface-600 hPa and 600–200 hPa partial columns in addition to the total column. The ability of the retrievals to capture the CO variabilities is slightly different for the two retrieval algorithms. The correlation coefficients are generally larger for SOFRID, especially for the total and lower tropospheric columns, meaning a better representation of the phase of the variability, while the amplitude of the variations of FORLI are in better agreement with IAGOS in the mid-upper troposphere. On average SOFRID and FORLI retrievals are underestimating the IAGOS total columns of CO (TCC) by 8±16 % and 6±14 % respectively. This global TCC agreement between the algorithms is hiding significant vertical and geographical differences. In the lower troposphere (Surface-600 hPa) the bias is larger for FORLI (-11±27 %) than for SOFRID (-4±24 %). In the mid-upper troposphere the situation is reversed with a bias of -6±15 % for FORLI and of -11±13 % for SOFRID. The largest differences between the retrievals are detected south of Bangkok where SOFRID underestimation is systematically larger for the TCC and mid-upper tropospheric column. North of Philadelphia FORLI biases are significantly larger than SOFRID ones for the TCC and the lower tropospheric columns. Our validation results will provide a better characterisation of IASI-CO data to the users and help improve the retrievals for future versions. [ABSTRACT FROM AUTHOR]

The Metop/IASI instruments provide data for operational meteorology and document atmospheric composition since 2007. IASI Ozone (O3) data have been used extensively to characterize the seasonal and interrannual variabilities and the evolution of tropospheric O3 at the global scale. The SOFRID (SOftware for a Fast Retrieval of IASI Data) is a fast retrieval algorithm that provides IASI O3 profiles for the whole IASI period. Up to now SOFRID O3 retrievals (v1.5 and 1.6) were performed 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 O3 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 kernels and raw. This methodology is essential to evaluate the inherent usefulness of the retrievals to assess O3 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 O3 regions such as the southern hemisphere (ii) it increases the retrieved O3 variability leading to a better agreement with ozonesonde data. Concerning UTLS and stratospheric O3 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 %) 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 literature for smoothed ozonesonde data only. This comparison highlights three main differences: (i) FORLI retrievals contain more theoretical information about tropospheric O3 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 %). [ABSTRACT FROM AUTHOR]

The prior information used for Level 2 (L2) retrievals in the thermal infrared can influence the quality of the retrievals themselves and, therefore, their further assimilation in atmospheric composition models. In this study we evaluate the differences between assimilating L2 ozone profiles and Level 1 (L1) radiances from the Infrared Atmospheric Sounding Interferometer (IASI). We minimized potential differences between the two approaches by employing the same radiative transfer code (RTTOV) and a very similar setup for both the L2 retrievals (1D-Var) and the L1 assimilation (3D-Var). We computed hourly 3D-Var reanalyses assimilating respectively L1 and L2 data in the chemical transport model MOCAGE and compared the resulting O3 fields among each other and against ozonesondes. We also evaluated the joint assimilation of limb measurements from the Microwave Limb Sounder (MLS) on top of IASI to assess the impact of stratospheric O3 on tropospheric reanalyses. Results indicate that significant differences can arise between L2 and L1 assimilation, especially in regions where the L2 prior is biased (at the tropics and in the southern hemisphere in this study). L1 and L2 assimilation give instead very similar results in the northern hemisphere, especially when MLS measurements are used to constrain the stratospheric O3 column. We conclude this study by listing remaining issues that are common to both the L1 and L2 approaches and that deserve further research. [ABSTRACT FROM AUTHOR]