Efficient generation of synthetic near-infrared satellite images for model evaluation and data assimilation

Satellite images in the solar spectrum provide high-resolution cloud and aerosol information and present promising observations for data assimilation and model evaluation. While visible channels contain information on the cloud distribution and cloud optical thickness, near-infrared channels are in addition more sensitive to cloud microphysical properties and can be used to distinguish between water and ice clouds. The assimilation of these channels is therefore expected to improve the vertical cloud structure and correct errors in effective droplet and ice particle radii.

The direct assimilation of satellite radiance in operational systems has so far been restricted to infrared and microwave observations. This is because sufficiently fast and accurate forward operators for visible and near-infrared radiances were not yet available, which is related to the fact that multiple scattering makes radiative transfer at solar wavelengths complicated and standard radiative transfer solvers computationally expensive. MFASIS, a 1D radiative transfer method based on compressed look-up tables, is sufficiently accurate and orders of magnitude faster, but limited to visible channels and clouds.

After discussing the limitations in the current version of MFASIS that prevent it from simulating near-infrared channels accurately, we present an alternative approach that increase the accuracy significantly for near-infrared channels. In this novel approach, the look-up tables are replaced by a neural network reducing the computational costs and allowing for additional input parameters. Those parameters enable us to describe the vertical distribution of cloud parameters, in particular the effective radius profiles, more accurately. We will demonstrate that the errors are reduced considerably, compared to the original MFASIS method. The new approach is tested for the SEVIRI 1.6mu channel using model output from IFS and the convective-scale data assimilation system KENDA, which is based on the ICON-D2 model.