Incorporating physical constraints in a deep learning framework for short-term daily prediction of sea ice concentration

Grasping the rapidly changing sea ice patterns has become the key to Arctic navigation. Starting from meteorology and oceanography, this study combines the empirical orthogonal function (EOF) analysis with convolutional long short-term memory (ConvLSTM) model to realize short-term sea ice prediction. To further improve the effectiveness and predictability of the ConvLSTM model, we adopt the EOF analysis to incorporate the spatial and temporal patterns of sea ice changes into the ConvLSTM model in three ways, namely, model input factors, model internal network structure, and model loss function. Through the mean absolute error (MAE), anomaly correlation coefficient (ACC), and anomaly metrics, we find that embedding the EOF physical constraints into the model's internal structure fully employs the spatial pattern and time series information, affording an optimal prediction effect. Additionally, the impact of the weight proportion between the physical constraint and the base loss and the effect of combining the three ways are discussed. The experimental results reveal that tuning the weight proportion in the loss function can improve the model's training effect. However, multiple combinations of the same physical law make it difficult to introduce new effective information.