Feng, J.; Li, J.; Zhong, W.; Wu, J.; Li, Z.; Kong, L.; Guo, L. Daily-Scale Prediction of Arctic Sea Ice Concentration Based on Recurrent Neural Network Models. J. Mar. Sci. Eng.2023, 11, 2319.
Feng, J.; Li, J.; Zhong, W.; Wu, J.; Li, Z.; Kong, L.; Guo, L. Daily-Scale Prediction of Arctic Sea Ice Concentration Based on Recurrent Neural Network Models. J. Mar. Sci. Eng. 2023, 11, 2319.
Feng, J.; Li, J.; Zhong, W.; Wu, J.; Li, Z.; Kong, L.; Guo, L. Daily-Scale Prediction of Arctic Sea Ice Concentration Based on Recurrent Neural Network Models. J. Mar. Sci. Eng.2023, 11, 2319.
Feng, J.; Li, J.; Zhong, W.; Wu, J.; Li, Z.; Kong, L.; Guo, L. Daily-Scale Prediction of Arctic Sea Ice Concentration Based on Recurrent Neural Network Models. J. Mar. Sci. Eng. 2023, 11, 2319.
Abstract
Arctic sea ice prediction holds significant importance for facilitating Arctic route planning, optimizing fisheries management, and advancing the field of sea ice dynamics research. While various deep learning models have been developed for sea ice prediction, they predominantly operate at the seasonal or sub-seasonal scale, often focusing on localized areas, and few cater to full-region daily scale prediction. This study introduces the use of spatiotemporal sequence data prediction models, namely, the convolutional LSTM (ConvLSTM) and predictive recurrent neural network (PredRNN), for the prediction of sea ice concentration (SIC). Our analysis reveals that, when solely utilizing SIC historical data as the input, the ConvLSTM model outperforms the PredRNN model in SIC prediction. To enhance the model's capacity to capture spatiotemporal relationships between multiple variables, we expanded the range of input data types to form the ConvLSTM-multi and PredRNN-multi models. Experimental findings demonstrate that the ConvLSTM-multi model excels in assimilating the influence of reanalysis data on sea ice within the sea ice edge region, thus exhibiting superior performance in predicting daily Arctic SIC over the subsequent 10 days. Furthermore, sensitivity tests on various model parameters highlight the substantial impact of sea surface temperature and prediction date on the accuracy of daily sea ice prediction. Additionally, meteorological and oceanographic parameters primarily affect the prediction accuracy of the thin ice region at the edge of the sea ice.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
