A neural network-based method for generating synthetic 1.6 μm near-infrared satellite images

Abstract

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.

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“…Similar approaches based on MLP were presented in (Stegmann et al, 2022). They were also used to emulate 3D effects on RT (Meyer et al, 2022) and to generate near-infrared satellite images (ρ 1 : 1.6 µm) in (Baur et al, 2023). Recently, Liang et al (2023) used ML to assimilate different bands of BT from Advanced Microwave Sounding Unit-A.…”

“…Shi et al (2018) and Geiss et al (2021) analyzed the influence of clouds related parametrization on visible and infrared satellite images and found that the accuracy is closely related to the cloud representation. A further study about the impact of surface and air pressure and water and ice clouds on visible and near-infrared bands can be found in Baur et al (2023).…”