BEIJING, March 26 (Xinhua) --Chinese scientists made accurate predictions regarding Antarctic sea ice for December 2023 to February 2024 using deep learning methods.
The research team utilized a Convolutional Long Short-Term Memory (ConvLSTM) neural network to construct a seasonal-scale Antarctic sea ice prediction model.
Their forecast indicated that Antarctic sea ice would remain close to historical lows in February 2024, but there was less indication of it reaching a new record low. The predicted sea ice area (SIA) and sea ice extent (SIE) for February 2024 were 1.441 million square kilometers and 2.105 million square kilometers, respectively, slightly higher than the historic lows observed in 2023.
The team, led by researchers from Sun Yat-sen University and the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), submitted their prediction results in December. The results were published in the journal Advances in Atmospheric Sciences in early February.
Their prediction was then validated by the latest satellite observations for February. The observed SIA and SIE values for February 2024 are 1.510 million square kilometers and 2.142 million square kilometers, respectively.
According to the researchers, the comparison between the predictions and observations indicates a remarkably close alignment. Furthermore, the sea ice area and extent from December to February fall within one standard deviation of the predicted values, underscoring the reliability of the forecasting system.
The successful comparison between the prediction and observation data validates the accuracy of the ConvLSTM model and its potential for reliable Antarctic sea ice forecasting, said the researchers.
"Our successful prediction not only underscores the significance of strengthening Antarctic sea ice prediction research but also demonstrates the substantial application potential of deep learning methods in this critical area," said Yang Qinghua, a professor of Sun Yat-sen University.
Link to: https://english.news.cn/20240326/7cfdc07d0b804c46901b0095c5c431d2/c.html
