Due to the spatio-temporal complexity and correlation of atmospheric motion, meteorological forecasting is an extremely challenging task. Over the past few decades, with the continuous development of traditional numerical prediction methods based on the physical model, the forecasting quality has been significantly improved. But, technically, the numerical prediction methods have reached the extreme stage. Thanks to
the rapid development of meteorological observation technology, the meteorological industry has accumulated massive meteorological data, which provides an opportunity to build new data-driven meteorological forecasting methods. Meanwhile, the deep learning methods have been successfully applied in various areas, such as visual understanding, natural language processing, speech recognition, and time series prediction,
and so on. In the past years, many researchers have tried to introduce deep learning methods into meteorological forecasting. Nowadays, meteorological forecasting based on deep learning has gradually become a popular research issue. However, due to the long-term dependence and large-scale spatial correlation hidden in meteorological data,
and additionally due to the complex coupling relationship between different modalities, Actually, it is difficult to further improve the performance by directly applying the existing deep learning models. For example, the spatio-temporal variance of local meteorological patterns does not meet the inductive bias of typical convolutional recurrent neural network (namely, the hypothesis about spatio-temporal sharing kernels).
Therefore, based on the multi-modal meteorological data, under the framework of convolutional recurrent neural network and Transformer, this thesis carries out the model construction, model training and model validation of deep neural networks to improve the prediction accuracy for meteorological forecasting.
The main contributions of this thesis are described as follows:
1. We proposed a new convolutional recurrent neural unit, namely, Spatiol-Temporal Adaptive Convolutional Gated Recurrent Unit (STAConvGRU). Technically, the spatio-temporal adaptive convolution operation is embedded into the Gated gated recurrent unit. The key motivation behind STAConvGRU is to develop additional convolution
layers to learn simultaneously the sampling positions and weights of convolutional kernels, when updating the internal state of the convolution recurrent unit. As a result, the adaptive convolution could select the positions and adjust the weights according to the spatio-temporal information to the meteorological, which matches well the local meteorological transformation patterns. Comparative experiments are conducted on four types of meteorological datasets, including temperature, isobaric relative humidity, wind, and radar echo. The experimental results demonstrate the effectiveness and superiority of our proposed model.
2. We proposed a deep learning model based on Transformer for meteorological forecasting. Specifically, gating mechanism is developed to achieve the multi-mode fusion of isobaric temperature, relative humidity and wind speed; Transformer mechanism is implemented by spatio-temporal axial attention to effectively learn long-term dependence and large-scale spatial correlation. Architecturally, the Transformer encoder-decoder framework is employed as the overall framework. Extensive comparative experiments have been conducted on the regional ERA5 reanalysis dataset, demonstrating that the proposed model method is effective and superior in the prediction of temperature, relative humidity and wind.