18 Apr 2023
 | 18 Apr 2023
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

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

Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast

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.

Florian Baur et al.

Status: open (until 07 Jul 2023)

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Florian Baur et al.

Florian Baur et al.


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Short summary
This study extends MFASIS to simulate 1.6 μm NIR channel reflectances with a neural network, enabling its use in model evaluation and data assimilation. A two-layer model was developed for cloud structure with optimized reflectance errors using IFS forecasts and ICON-D2 hindcasts. Mean absolute reflectance error achieved was 0.01 or less, much smaller than typical differences between observations and models.