Abstract. This study presents an extension to the method for fast satellite image synthesis (MFASIS) to allow simulating reflectances for the 1.6 μm near-infrared channel based on a computationally efficient neural network with improved accuracy. Such a fast forward operator enables using 1.6 μm channels from different satellite instruments in applications like model evaluation or operational data assimilation. It thus paves the way for the exploitation of additional information at this frequency, e.g. on cloud phase and particle sizes, which is complementary to the visible and thermal infrared range. To achieve similar accuracy for 1.6 μm NIR as for the visible channels 0.4–0.8 μm, it is important to represent vertical gradients of effective cloud particle radii, as well as mixed-phase clouds and molecular absorption. A comprehensive dataset sampled from IFS forecasts is used to develop the method. A new approach for describing the complex vertical cloud structure with a two layer model of water, ice and mixed-phase clouds optimized to obtain small reflectance errors is described and the relative importance of the different input parameters describing the idealized profiles is analyzed. Additionally, a different parameterization of the effective water and ice particle radii was used for testing. Further evaluation uses a month of ICON-D2 hindcasts with effective radii directly determined by the two-moment microphysics scheme of the model. The fast neural network approach itself does not add any significant additional error compared to the profile simplifications. In all cases, the mean absolute reflectance error achieved is about 0.01 or smaller, which is an order of magnitude smaller than typical differences between reflectance observations and corresponding model values.
<p>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.</p>
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