Machine learning for observation bias correction with application to dust storm data assimilation
Data assimilation algorithms rely on a basic assumption of an unbiased observation error.However, the presence of inconsistent measurements with nontrivial biases or inseparable baselines is unavoidable in practice.Assimilation analysis might diverge from reality since the data assimilation itself cannot distinguish whether the differences between