[1] The Infrared Atmospheric Sounding Interferometer (IASI) is a nadir-viewing remote sensor due for launch on board the European Metop satellites (to be launched in 2005, 2010, and 2015). It is dedicated to the study of the troposphere and the lower stratosphere to support operational meteorology as well as atmospheric chemistry and climate studies. For this purpose, it will record high resolution atmospheric spectra in the thermal infrared, allowing the measurement of several infrared absorbing species. This paper describes the clear-sky retrieval scheme developed in the framework of the preparation of the IASI mission for the operational, near real time, retrieval of O 3 , CH 4 , and CO concentrations. It includes the inversion module, based on a neural network approach, as well as an error analysis module. The studies undertaken on test simulations have shown that a performance of the order of 1.5%, 2%, and 5% for the retrieval of total columns of O 3 , CH 4 , and CO, respectively, can be achieved, and of the order of 28%, 15%, and 9% for the retrieval of partial columns of O 3 between the surface and 6, 12, and 16 km high, respectively. The efficiency of the algorithm is demonstrated on the atmospheric measurements provided by the Interferometric Monitor for Greenhouse Gases (IMG)/ADEOS, allowing to obtain the first remote-sensing simultaneous distributions of ozone and its two precursors, CO and CH 4 .

SUMMARYThe estimation-error covariance matrix associated with the analysis of the atmospheric state is used in one particular meteorological situation to examine the potential benefit of radiance data for numerical weather prediction. The gain of information content obtained from simulated Infrared Atmospheric Sounding Interferometer (IASI) data is studied and compared with the current information content present in the TIROS Operational Vertical Sounder (TOVS) radiances.Nineteen independent items of information on a typical temperaturehumidity profile are available from the IASI data, compared to six from the TOW data. In terms of temperature, fine-scale structures associated with a vertical resolution of 1 km are estimated with a 0.7 K error standard deviation. The gain of information for specific humidity is of the same order of magnitude as for temperature. Typically, humidity structures associated with 1 km vertical resolution are estimated with a relative error of 16%.The projection of the analysis-error covariance matrix on atmospheric-error structures (relevant for numerical weather prediction) gives a measure of the impact of the use of radiances on the observability of such structures. The results indicate that IASI data would be a decisive source of information for the analysis of such structures.Finally, a preliminary sensitivity study suggests that the degradation due to radiance noise associated with possible modifications of the IASI instrument, hardly affects the quality of the analysis.

MEthane Remote LIdar missioN (MERLIN) is a German-French space mission, scheduled for launch in 2024 and built around an innovative light detecting and ranging instrument that will retrieve methane atmospheric weighted columns. MERLIN products will be assimilated into chemistry transport models to infer methane emissions and sinks. Here the expected performance of MERLIN to reduce uncertainties on methane emissions is estimated. A first complete error budget of the mission is proposed based on an analysis of the plausible causes of random and systematic errors. Systematic errors are spatially and temporally distributed on geophysical variables and then aggregated into an ensemble of 32 scenarios. Observing System Simulation Experiments are conducted, originally carrying both random and systematic errors. Although relatively small (±2.9 ppb), systematic errors are found to have a larger influence on MERLIN performances than random errors. The expected global mean uncertainty reduction on methane emissions compared to the prior knowledge is found to be 32%, limited by the impact of systematic errors. The uncertainty reduction over land reaches 60% when the largest desert regions are removed. At the latitudinal scale, the largest uncertainty reductions are achieved for temperate regions (84%) and then tropics (56%) and high latitudes (53%). Similar Observing System Simulation Experiments based on error scenarios for Greenhouse Gases Observing SATellite reveal that MERLIN should perform better than Greenhouse Gases Observing SATellite for most continental regions. The integration of error scenarios for MERLIN in another inversion system suggests similar results, albeit more optimistic in terms of uncertainty reduction. Plain Language SummaryAtmospheric methane is the second most important anthropogenic greenhouse gas. Its evolution in the atmosphere reflects the balance between its emissions and its sinks, both being still very uncertain. Observations and models are necessary to improve this situation and reduce the uncertainties associated to the global methane cycle, which is critical considering climate change. In this context, the MEthane Remote LIdar missioN (MERLIN) German-French space satellite mission, scheduled for launch in 2023, will retrieve methane atmospheric columns. MERLIN products will be integrated into atmospheric models to improve estimates of methane emissions and sinks. In this paper, we establish the first complete error budget of the future MERLIN instrument and use it to estimate the reduction of uncertainties on methane emissions that can be expected once the satellite is launched. The two main findings are that the uncertainties should be reduced on average by 60% over land, where most methane emissions are located, and that MERLIN should perform better than the main methane sounder currently on orbit for most continental regions.