ERA-Interim is the latest global atmospheric reanalysis produced by the EuropeanCentre for Medium-Range Weather Forecasts (ECMWF). The ERA-Interim project was conducted in part to prepare for a new atmospheric reanalysis to replace ERA-40, which will extend back to the early part of the twentieth century. This article describes the forecast model, data assimilation method, and input datasets used to produce ERA-Interim, and discusses the performance of the system. Special emphasis is placed on various difficulties encountered in the production of ERA-40, including the representation of the hydrological cycle, the quality of the stratospheric circulation, and the consistency in time of the reanalysed fields. We provide evidence for substantial improvements in each of these aspects. We also identify areas where further work is needed and describe opportunities and objectives for future reanalysis projects at ECMWF.

The Infrared Atmospheric Sounding Interferometer (IASI) forms the main infrared sounding component of the European Organisation for the Exploitation of Meteorological Satellites's (EUMETSAT's) Meteorological Operation (MetOp)-A satellite (Klaes et al. 2007), which was launched in October 2006. This article presents the results of the first 4 yr of the operational IASI mission. The performance of the instrument is shown to be exceptional in terms of calibration and stability. The quality of the data has allowed the rapid use of the observations in operational numerical weather prediction (NWP) and the development of new products for atmospheric chemistry and climate studies, some of which were unexpected before launch. The assimilation of IASI observations in NWP models provides a significant forecast impact; in most cases the impact has been shown to be at least as large as for any previous instrument. In atmospheric chemistry, global distributions of gases, such as ozone and carbon monoxide, can be produced in near–real time, and short-lived species, such as ammonia or methanol, can be mapped, allowing the identification of new sources. The data have also shown the ability to track the location and chemistry of gaseous plumes and particles associated with volcanic eruptions and fires, providing valuable data for air quality monitoring and aircraft safety. IASI also contributes to the establishment of robust long-term data records of several essential climate variables. The suite of products being developed from IASI continues to expand as the data are investigated, and further impacts are expected from increased use of the data in NWP and climate studies in the coming years. The instrument has set a high standard for future operational hyperspectral infrared sounders and has demonstrated that such instruments have a vital role in the global observing system.

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Abstract. This paper gives an update of the RTTOV (Radiative Transfer for TOVS) fast radiative transfer model, which is widely used in the satellite retrieval and data assimilation communities. RTTOV is a fast radiative transfer model for simulating top-of-atmosphere radiances from passive visible, infrared and microwave downward-viewing satellite radiometers. In addition to the forward model, it also optionally computes the tangent linear, adjoint and Jacobian matrix providing changes in radiances for profile variable perturbations assuming a linear relationship about a given atmospheric state. This makes it a useful tool for developing physical retrievals from satellite radiances, for direct radiance assimilation in NWP models, for simulating future instruments, and for training or teaching with a graphical user interface. An overview of the RTTOV model is given, highlighting the updates and increased capability of the latest versions, and it gives some examples of its current performance when compared with more accurate line-by-line radiative transfer models and a few selected observations. The improvement over the original version of the model released in 1999 is demonstrated.

To assimilate atmospheric and surface radiance measurements from satellites in a numerical weather prediction model, a fast radiative transfer model is required to compute radiances from the model first guess fields at every observation point. Such a model for satellite infrared and microwave radiance measurements is used operationally for the assimilation of TIROS operational vertical sounder radiances at the European Centre for Medium-Range Weather Forecasts. An improved version of this model has been developed which requires ozone, in addition to temperature and water vapour, in the input profile and it has been generalized to compute radiances for other satellite radiometers using the same code. Instruments such as the high resolution infrared radiation sounder and the advanced microwave sounding unit on the National Oceanic and Atmospheric Administration polar orbiters, the METEOSAT water vapour imager and the Geostationary Operational Environmental Satellite infrared sounder have been simulated. It is demonstrated, by comparisons with line-by-line model computed radiances, that the fast model can reproduce the line-by-line model radiances for the TIROS operational vertical sounder stratospheric temperature sounding channels to an accuracy below the instrumental noise. The tropospheric, surface sensing, water vapour and ozone channel radiances cannot be predicted to such an accuracy, but still accurately enough for numerical weather prediction assimilation. A comparison of measured TIROS operational vertical sounder radiances with predicted values from numerical weather prediction model analyses gives larger differences than would be expected from the combination of the fast model and instrument related errors for most channels. The validity of the tangent linear approximation of the model gradient for typical radiance departures is also explored, with several examples, for the high resolution infrared radiation sounder/advanced microwave sounding unit instrument combination. The tangent-linear approximation is valid for temperature but significant departures from linearity about the first guess profile are observed for water vapour and ozone. Cloud affected infrared radiances have a highly non-linear response.

This article investigates the use of an updated observation-error covariance matrix for the Infrared Atmospheric Sounding Interferometer (IASI) in the European Centre for Medium-Range Weather Forecasts (ECMWF) system. The new observation-error covariance matrix is based on observation-space diagnostics and includes interchannel error correlations, but also assigns significantly altered error standard deviations. The update is investigated in detail in assimilation experiments, including an assessment of the role of error inflation and taking interchannel error correlations into account.The updated observation-error covariance leads to a significant improvement in the use of IASI data, especially in the Tropics and the stratosphere and particularly for humidity and ozone. The benefits are especially strong for short-range forecasts, whereas the impact in the medium range is less pronounced.The study highlights the benefits of taking interchannel error correlations into account, which allows the use of an observation-error covariance for IASI that is overall more consistent with departure statistics. At the same time, the study also demonstrates that error inflation can be used to compensate partially, though not fully, for neglected error correlations. Adjustments such as scaling of the originally diagnosed observation-error estimates are also found to be beneficial when the diagnosed interchannel error correlations are taken into account.

SUMMARYAn improved version of the RTTOV (Radiative Transfer for the Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder) fast radiative transfer model used operationally at the European Centre for Medium-Range Weather Forecasts (ECMWF) for the assimilation of Advanced TIROS Operational Vertical Sounder (ATOVS) radiances has been developed. This new model computes radiances for the Atmospheric Infrared Sounder and reproduces line-by-line radiances and Jacobians for the surface-sensing, water vapour and ozone channels of the ATOVS with signi cantly improved accuracy. The pro le-dependent predictors used by the improved model to parametrize the atmospheric optical depths are based on the approach followed by the ECMWF fast radiative transfer model for the Infrared Atmospheric Sounding Interferometer (RTIASI). To improve the accuracy of the fast model in reproducing line-by-line radiances for the Infrared Atmospheric Sounder, modi cations have been made to the predictors used in RTIASI by introducing a revised set of predictors for ozone and adding new predictors to model the water vapour continuum type absorption. To eliminate discontinuities in the water vapour Jacobians observed in RTIASI, data are now weighted prior to performing the regression.

SUMMARYThe development of an assimilation system for radiance data from the Atmospheric InfraRed Sounder (AIRS) is described, in particular the identification of cloud contamination, bias correction and the characterization of errors in the measured radiances and radiative-transfer model. The results of assimilation experiments are presented. These show that a conservative use of AIRS radiance data (in a system already extensively observed with other satellite data) results in a small, but consistent, improvement in the quality of analyses and forecasts. Larger impacts of AIRS are found in hypothetical experiments that test the use of radiances from only a single sounding instrument. In these, the use of AIRS is found to outperform the use of data either from a single Advanced Microwave Sounding Unit-A (AMSU-A) or from a single High-resolution InfraRed Sounder (HIRS). In this hypothetical context the relative forecast performance of each sensor is found to correlate with the size and vertical scale of increments caused by the assimilation of the radiances.