Bernhard Mayer Atmospheric physicist
Research area

Assimilation of visible satellite observations.

Fast radiative-transfer operators for the use of visible satellite observations in numerical weather prediction.

Overview

In close collaboration with the Deutscher Wetterdienst (DWD), we develop fast radiative-transfer operators that make it possible to assimilate visible satellite observations in numerical weather prediction.

MFASIS (Method for Fast Satellite Image Synthesis) efficiently simulates satellite images in the visible and near-infrared spectral range. The work developed from early DWD-funded studies into an operational forward operator: since March 2023, DWD has assimilated observations from the 0.6 µm SEVIRI channel in its ICON-D2 forecasting system.

DWD announcement of the operational assimilation of visible SEVIRI observations in ICON-D2
Operational implementation at DWD: assimilation of the 0.6 µm SEVIRI visible channel in ICON-D2, effective 15 March 2023.
Timeline showing the development from early extramural work through PASTAT and MFASIS to operational use at DWD
Development of MFASIS, from the initial DWD-funded work to operational use.

Key publications

  1. 2016 Scheck, L., Frèrebeau, P., Buras-Schnell, R., & Mayer, B. A fast radiative transfer method for the simulation of visible satellite imagery ↗. Journal of Quantitative Spectroscopy and Radiative Transfer, 175, 54–67.
  2. 2018 Scheck, L., Weissmann, M., & Mayer, B. Efficient methods to account for cloud-top inclination and cloud overlap in synthetic visible satellite images ↗. Journal of Atmospheric and Oceanic Technology, 35(3), 665–685.
  3. 2020 Scheck, L., Weissmann, M., & Bach, L. Assimilating visible satellite images for convective-scale numerical weather prediction: A case-study ↗. Quarterly Journal of the Royal Meteorological Society, 146(732), 3165–3186.
  4. 2021 Scheck, L. A neural network based forward operator for visible satellite images and its adjoint ↗. Journal of Quantitative Spectroscopy and Radiative Transfer, 274, 107841.
  5. 2023 Baur, F., Scheck, L., Stumpf, C., Köpken-Watts, C., & Potthast, R. A neural-network-based method for generating synthetic 1.6 µm near-infrared satellite images ↗. Atmospheric Measurement Techniques, 16, 5305–5326.