Neural network-based methods for generating synthetic satellite images in the solar spectral range

Solar satellite channels of instruments onboard geostationary or polar orbiting satellites provide high resolution information on clouds and aerosols that is valuable for numerical weather prediction. The solar channels are sensitive to the microphysical properties of cloud and aerosol particles and contain better information on water content than the thermal channels. The direct assimilation of solar satellite images or their application for the evaluation of numerical weather prediction (NWP) models requires sufficiently fast and accurate forward operators, which solve radiative transfer (RT) problems to compute synthetic images from the NWP model output. As multiple scattering complicates the solution of radiative transfer problems in the solar spectral range, standard RT methods are too slow for this purpose. Faster methods have been developed for cloud-affected visible channels, but are limited to non-absorbing channels and do not take aerosols into account. Machine learning methods provide a promising way to accelerate the complex radiative transfer computations in satellite forward operators and to overcome the limitations of previous approaches. Here we report on experiments based on deep feed forward neural network. It is demonstrated that using neural networks the amount of training data that has to be computed with standard radiative transfer methods can be reduced by several orders of magnitude, compared to previous approaches, while increasing the speed by an order of magnitude and improving accuracy. Moreover, tangent linear and adjoint versions required for variational data assimilation can easily be implemented and do not have to be adapted when network structure or training data are changed. We discuss optimizations to reduce the computational effort and provide examples for applications that require more input parameters than cloud-affected visible channels and have only become feasible with the new approach.