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International audience; A method is presented to compare the L1c products (calibrated radiance spectra) of the two IASI (In- frared Atmospheric Sounding Interferometer) instruments on board the two Metop satellites Metop-A and Metop-B. After defining categories of homogenous geophysical/geographical conditions (day/night, ocean/night, clear/overcast), one can produce grand average spectra with the corresponding statistical diagnostics on the L1c spectral grid (8461 channels between 645 and 2760 cm-1 with a spacing of 0.250 cm-1 ). The corresponding spectra are generated monthly for the 30 scan positions (SP) of the scene acquisition mirror and for the 4 pixels (PN or IFOV) of the two IR sounders. These average spectra (with a much reduced noise as compared to one individual IASI spectrum) and the corresponding uncertainty on a channel-by-channel basis (with a Gaussian character check) can then be used to compare the two instruments on the short time scale (month or shorter periods). A pixel-by-pixel comparison of IASI-A and IASI-B is not possible because the two instruments can never "see" the same footprint at the same time, since they are flying 50 min apart on the same orbit. But due to this small time difference, the set of sampled IFOVs used for generating the averages (over given homogeneous categories) should obey the approximation ≅ - . The left term of the equation would be the hypothetical pixel-by-pixel spectral radiance difference of the two sounders if they were sampling the same footprint at the same time. The right hand side term is the difference calculated from the mean of the individual sounders calculated for the various categories over a given time period. A status on the efforts to implement this tool in the EPS monitoring environment and the potential for instrument as well as climate monitoring is presented.

Geophysical parameters from the IASI instrument on Metop-A are essential products provided from EUMETSAT's Central Facility in near real time. They include vertical profiles of temperature and humidity, related cloud information, surface emissivity and temperature, and atmospheric composition parameters (CO, ozone and several other trace gases). The IASI L2 processor version 5 has been running routinely since September 2010. We give here a short summary of the most recent related validation studies, including results from the Metop-B/IASI L2 Cal/Val activities which started end January 2013, after the conclusion of the first phase of the L1c commissioning. We then introduce the current developments and the future evolutions of the IASI L2 processor operated at EUMETSAT towards the version 6 (v6), which release is scheduled second half of 2013. In particular we will address the integration of new atmospheric composition products from ULB/LATMOS in the scope of O3M-SAF CDOP-2, with first the retrieval of CO profiles. Only the CO total column has been retrieved until version 5. The cloud screening has been improved with the addition of a third detection algorithm based on artificial neural networks and independent from NWP forecasts. We will discuss here the use of concurrent cloud detection methods and the relationship between the cloudiness estimate and the sensing products yield and quality. A quality indicator is associated to the retrieved geophysical parameters in v6. Lastly, new retrieval methods for temperature and water-vapour sounding have been developed, including joint use of micro-wave (MW) and infrared (IR) measurements. The statistical MW+IR retrieval scheme, developed for v6, takes advantage of AMSU and MHS measurements collocated with IASI L1c spectra. It allows for sounding below the clouds in most cloud contaminated IFOVs with improved performances (typically about 1K rms) as compared to the current operational IR-based partly-cloudy retrieval. The MWIR-derived temperature and humidity profiles are used to constrain the retrieval of temperature, humidity and ozone 1D-Var retrievals in clear sky. The latter are performed with reconstructed radiances from a new channel selection and a scene-dependent bias correction. Initial results with the version 6 using ECMWF analyses as reference have shown that the temperature profiles are improved by 0.2 to 0.5K rms in the lower troposphere and that the precision in water-vapour profiles is increased by 30 to 50% in the boundary layer.

International audience; An assimilation system allowing combining the forecasts of a CTM with observations is used to assess the quality of GOMOS ozone measurements. Only a sub-sample of all GOMOS observations performed so far were available in the validation dataset that was available to the ACVT community. This allowed us to find that the average precision of GOMOS local O3 density retrievals is of the order of 5% in the low stratosphere. It has not been possible to draw any significant conclusions on the bias using this dataset and further investigations are required

A method for assimilating observations of long-lived species such as ozone (O3) and nitrous oxide (N2O) in a three-dimensional chemistry transport model (3D-CTM) is described. The model is forced by the temperature and wind analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF). The O3 and N2O fields used in this study are obtained from the Sub-Millimeter Radiometer (SMR) aboard the Odin satellite. The assimilation technique used is the sequential statistical interpolation approach. The parametrization of the error covariance matrix of the model forecast field is described. A sensitivity study of the system parameters is done in terms of the OMF (observation minus forecast) vector also called “innovation” vector and in terms of the χ2 (chi-square) test. The effect of the correlation distances is critical for the assimilated field. The RMS (root mean square) of the OMF for the correlation distances is minimal for values of 1500 km in the meridional direction and 500 km in the zonal direction for both O3 and N2O. The treatment of the meridional distance as a function of latitude does not reveal an important improvement. The χ2 diagnostic shows that the asymptotic value of the model error (the model error of saturation) is optimal for the value of 12.5% for O3 and 18% for N2O. We demonstrate the applicability of the developed assimilation method for the Odin/SMR data. We also present first results of the assimilation of Odin/SMR ozone and nitrous oxide for the period from 22 December 2001 to 17 January 2002.