The research fellow will join the satellite team in the Observation Modelling and Verification Section in the Numerical Weather Prediction (NWP) department at the German Weather Service (DWD).

She or he will work alongside DWD scientists responsible for the use of satellite data within the operational NWP systems, taking active part in the research and development aspects for both the global and high-resolution variational and ensemble-based data assimilation systems.

Additionally, the fellow will interact with relevant experts from the data assimilation and model physics sections and be in contact to EUMETSAT within the context of the EUMETSAT fellowship programme.

The work will focus on a systematic investigation of the influence of inhomogeneities of model fields within a satellite footprint which are currently mostly neglected in the forward calculation setups of operational data assimilation systems.

The analysis aims at the development of a method to account for this situation dependent part of the representativeness error using machine learning approaches.

The analysis will be based on radiative transfer simulations for a selection of typical satellite frequencies and footprint geometries of current and future satellite instruments in the visible, infrared and microwave ranges.

Duties

• Setup and extension of a radiative transfer computations framework using existing radiative transfer software (RTTOV, Radsim) and NWP model fields from DWD operational and experimental setups in order to allow the evaluation of the influence of inhomogeneities within the satellite footprints;
• Generation of data sets of simulated radiances and related model parameters for a variety of situations, typical frequencies (visible, infrared and microwave), satellite footprints sizes, and model resolutions;

• Statistical evaluation of the effects of scene inhomogeneity, as characterized by model parameters, on the radiances;
• Identification of the most relevant parameters for the development of an AI-based parameterization aimed at the use within a data assimilation system;
• Presentation and publication of results (meetings, conferences, journals).

Skills and Experience

• Applicants should have a University degree in Meteorology, Physics, Remote Sensing or Mathematics, and relevant research experience preferably at PhD level;
• Knowledge in one or more of the following areas would be a clear advantage: radiative transfer modelling, numerics and statistics, data assimilation, numerical weather forecasting;
• Experience in working with large amounts of data, the quantitative processing and analysis of observations from meteorological satellites and in applying AI methods is desirable;
• Good knowledge of scientific programming (Fortran, C/C++, Python) on Unix/Linux based systems are mandatory;
• Strong interpersonal and team working skills are required, along with strengths in analysis, synthesis and presentation, as well as a high level of commitment and interest in continuing education/training;
• Candidates must be able to work effectively in English, both verbally and written.