Articles | Volume 11, issue 7
Model description paper
10 Jul 2018
Model description paper |  | 10 Jul 2018

An update on the RTTOV fast radiative transfer model (currently at version 12)

Roger Saunders, James Hocking, Emma Turner, Peter Rayer, David Rundle, Pascal Brunel, Jerome Vidot, Pascale Roquet, Marco Matricardi, Alan Geer, Niels Bormann, and Cristina Lupu

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.

Short summary
This paper describes a fast atmospheric radiative transfer model, RTTOV, which is widely used in the satellite retrieval and weather forecast model assimilation communities. It computes top-of-atmosphere radiances for visible, infrared and microwave downward-viewing satellite radiometers. It enables the satellite data, which are a key part of the observing system, to be optimally used with forecast models. The developments made to RTTOV over the past 20 years are summarised.