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  • Author or Editor: Bernhard Mayer x
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Leonhard Scheck
,
Martin Weissmann
, and
Bernhard Mayer

Abstract

Visible satellite images contain high-resolution information about clouds that would be well suited for convective-scale data assimilation. This application requires a forward operator to generate synthetic images from the output of numerical weather prediction models. Only recently have 1D radiative transfer (RT) solvers become sufficiently fast for this purpose. Here computationally efficient methods are proposed to increase the accuracy and consistency of an operator based on the Method for Fast Satellite Image Synthesis (MFASIS) 1D RT. Two important problems are addressed: the 3D RT effects related to inclined cloud tops and the overlap of subgrid clouds. It is demonstrated that in a rotated frame of reference, an approximate solution for the 3D RT problem can be obtained by solving a computationally much cheaper 1D RT problem. Several deterministic and stochastic schemes that take the overlap of subgrid clouds into account are discussed. The impact of the inclination correction and the overlap schemes is evaluated for synthetic 0.6-μm SEVIRI images computed from operational forecasts of the German-focused COSMO (COSMO-DE) Model for a test period in May–June 2016. The cloud-top inclination correction increases the information content of the synthetic images considerably and reduces systematic errors, in particular for larger solar zenith angles. Taking subgrid cloud overlap into account is essential to avoid large systematic errors. The results obtained using several different 2D cloud overlap schemes are very similar, whereas small but significant differences are found for the most consistent 3D method, which accounts for the fact that the RT problem is solved for columns tilted toward the satellite.

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Philipp M. Kostka
,
Martin Weissmann
,
Robert Buras
,
Bernhard Mayer
, and
Olaf Stiller

Abstract

Operational numerical weather prediction systems currently only assimilate infrared and microwave satellite observations, whereas visible and near-infrared reflectances that comprise information on atmospheric clouds are not exploited. One of the reasons for that is the absence of computationally efficient observation operators. To remedy this issue in anticipation of the future regional Kilometer-Scale Ensemble Data Assimilation (KENDA) system of Deutscher Wetterdienst, we have developed a version that is fast enough for investigating the assimilation of cloudy reflectances in a case study approach. The operator solves the radiative transfer equation to simulate visible and near-infrared channels of satellite instruments based on the one-dimensional (1D) discrete ordinate method. As input, model output of the operational limited-area Consortium for Small-Scale Modeling (COSMO) model of Deutscher Wetterdienst is used. Assumptions concerning subgrid-scale processes, calculation of in-cloud values of liquid water content, ice water content, and cloud microphysics are summarized, and the accuracy of the 1D simulation is estimated through comparison with three-dimensional (3D) Monte Carlo solver results. In addition, the effects of a parallax correction and horizontal smoothing are quantified. The relative difference between the 1D simulation in “independent column approximation” and the 3D calculation is typically less than 9% between 0600 and 1500 UTC, computed from four scenes during one day (with local noon at 1115 UTC). The parallax-corrected version reduces the deviation to less than 6% for reflectance observations with a central wavelength of 810 nm. Horizontal averaging can further reduce the error of the 1D simulation. In all cases, the bias is less than 1% for the model domain.

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