Position correction in dust storm forecasting using LOTOS-EUROS v2.1: Grid-distorted data assimilation v1.0

When calibrating simulations of dust clouds, both the intensity and the position are important. Intensity errors arise mainly from uncertain emission and sedimentation strengths, while position errors are attributed either to imperfect emission timing or to uncertainties in the transport. Though many studies have been conducted on the...

Source backtracking for dust storm emission inversion using an adjoint method: Case study of Northeast China

Emission inversion using data assimilation fundamentally relies on having the correct assumptions about the emission background error covariance. A perfect covariance accounts for the uncertainty based on prior knowledge and is able to explain differences between model simulations and observations. In practice, emission uncertainties are...

Dust Emission Inversion Using Himawari-8 AODs Over East Asia: An Extreme Dust Event in May 2017

Aerosol optical depths (AODs) from the new Himawari-8 satellite instrument have been assimilated in a dust simulation model over East Asia. This advanced geostationary instrument is capable of monitoring the East Asian dust storms which usually have great spatial and temporal variability. The quality of the data has been verified through a...

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...