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

为了正确解释基于卫星的地球观测数据和/或地球系统模型结果，这些数据必须没有系统误差，即通常所说的偏差。众所周知，这两个数据来源都容易产生重大偏差，目前许多环境影响和预测研究都忽略了这一点。这个项目将提供一种方法来开发这些偏差的模型。将采用一种状态更新技术--集合卡尔曼滤波，以正确考虑两个数据源合并时的偏差。项目成果将对长期环境研究具有高度重要性，因为这些研究强烈依赖基于物理的模型和遥感数据。

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.