A novel and theoretically consistent method for correcting systematic errors in earth observation data and earth system model results

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For a correct interpretation of satellite-based earth observation data and/or Earth system model results, it is very important that these data are free of systematic errors, commonly referred to as bias. It is well known that both these data sources are prone to a significant bias, which is currently neglected in many environmental impact and prediction studies. This project will present a method to develop models for these biases. A state update technique, the Ensemble Kalman Filter, will be adapted to correctly take into account bias in the merging of the two data sources. The project outcomes will be of high importance for long-term environmental studies, since these strongly rely on physically-based models and remote sensing data.