Development of a Temperature Prediction Method Combining Deep Neural Networks and a Kalman Filter

Abstract

Numerical weather forecast models have biases caused by insufficient grid resolution and incomplete physical processes, especially near the land surface. Therefore, the Japan Meteorological Agency (JMA) has been operationally post-processing the forecast model outputs to correct biases. The operational post-processing method uses a Kalman filter (KF) algorithm for surface temperature prediction. Recent reports have shown that deep convolutional neural networks (CNNs) outperform the JMA operational method in correcting temperature forecast biases. This study combined the CNN-based bias correction scheme with the JMA operational KF algorithm. We expected that the combination of CNNs and a KF would improve the post-processing performance, as the CNNs modify large horizontal structures, and then, the KF corrects minor spatiotemporal deviations. As expected, we confirmed that the combination outperformed both CNNs and the KF alone. This study demonstrated the advantages of the new method in correcting coastal fronts, heat waves, and radiative cooling biases.