Bias correction of aircraft temperature observations in the Korean Integrated Model based on a deep learning approach

The aircraft-based observation is one of the important anchor data used in the numerical weather prediction (NWP) models. Nevertheless, the bias has been noted in the temperature observation through several previous studies. As the performance on the hybrid four-dimensional ensemble variational (hybrid-4DEnVar) data assimilation (DA) system of the Korean Integrated Model (KIM) ⸺ the operational model in the Korea Meteorological Administration (KMA) ⸺ has been advanced, the need for the aircraft temperature bias correction (BC) has been confirmed. Accordingly, as a preliminary study on the BC, the static BC method based on the linear regression was applied to the KIM Package for Observation Processing (KPOP) system. However, the results showed there were limitations of a spatial discontinuity and a dependency on the calculation period of BC coefficients.

In this study, we tried to develop the machine learning-based bias estimation model to overcome these limitations. The MultiLayer Perceptron (MLP) based learning was performed to consider the vertical, spatial and temporal characteristics of each observation by flight IDs and phases, and at the same time to consider the correlation among observation variables. As a result of removing the predicted bias from the bias estimation model, the mean of the background innovation (O-B) decreases from 0.2217 K to 0.0136 K in a given test period. Afterwards, in order to verify the analysis field impact for BC, the bias estimation model will be grafted onto the KPOP system and then several DA cycle experiments will be conducted in the KIM.