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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.

Abstract. The goals of this study are the evaluation of current fast radiative transfer models (RTMs) and line-by-line (LBL) models. The intercomparison focuses on the modeling of 11 representative sounding channels routinely used at numerical weather prediction centers: 7 HIRS (High-resolution Infrared Sounder) and 4 AMSU (advanced microwave sounding unit) channels. Interest in this topic was evident by the participation of 24 scientists from 16 institutions. An ensemble of 42 diverse atmospheres was used and results compiled for 19 infrared models and 10 microwave models, including several LBL RTMs. For the first time, not only radiances but also Jacobians (of temperature, water vapor, and ozone) were compared to various LBL models for many channels. In the infrared, LBL models typically agree to within 0.05-0.15 K (standard deviation) in terms of top-of-the-atmosphere brightness temperature (BT). Individual differences up to 0.5 K still exist, systematic in some channels, and linked to the type of atmosphere in others.The best fast models emulate LBL BTs to within 0.25 K, but no model achieves this desirable level of success for all channels. The ozone modeling is particularly challenging. In the microwave, fast models generally do quite well against the LBL model to which they were tuned. However, significant differences were noted among LBL models. Extending the intercomparison to the Jacobians proved very useful in detecting subtle or more obvious modeling errors. In addition, total and single gas optical depths were calculated, which provided additional insight on the nature of differences.

objective is to provide a good estimate of the microwave surface emissivity to improve the retrieval of atmospheric profiles or the direct assimilation of radiances in numerical weather prediction (NWP) models using microwave sounder data over land. TELSEM provides emissivity estimates and error-covariance matrices for all land surfaces between 19 and 100 GHz and for all angles and linear polarizations. It is based on a pre-calculated monthly-mean emissivity climatology derived from Special Sensor Microwave/Imager (SSM/I) observations. Results show that when TELSEM is used, radiative-transfer simulations are closer to real observations. This is important when RTTOV is used to generate simulated datasets, to analyze new instrument concepts or for assimilation schemes. Experiments also show that TELSEM can be applied to provide a first guess for the surface emissivity down to 6 GHz and up to 190 GHz (extrapolating the SSM/I emissivities). These emissivities are essential for atmospheric profile retrievals over land: results for water-vapour retrieval show that surface-contaminated channels can be utilized and that the retrieval is improved, in particular for the lower troposphere. Furthermore, TELSEM emissivity first guesses can be improved in emissivity-retrieval schemes.