男女羞羞视频在线观看,国产精品黄色免费,麻豆91在线视频,美女被羞羞免费软件下载,国产的一级片,亚洲熟色妇,天天操夜夜摸,一区二区三区在线电影
Global EditionASIA 中文雙語Fran?ais
China
Home / China / Innovation

Chinese scientists use machine learning for precise Antarctic sea ice prediction

Xinhua | Updated: 2024-03-26 14:56
Share
Share - WeChat

BEIJING -- 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.

Top
BACK TO THE TOP
English
Copyright 1994 - . All rights reserved. The content (including but not limited to text, photo, multimedia information, etc) published in this site belongs to China Daily Information Co (CDIC). Without written authorization from CDIC, such content shall not be republished or used in any form. Note: Browsers with 1024*768 or higher resolution are suggested for this site.
License for publishing multimedia online 0108263

Registration Number: 130349
FOLLOW US
 
主站蜘蛛池模板: 景泰县| 赞皇县| 逊克县| 澎湖县| 清河县| 宾川县| 桐庐县| 会东县| 拜泉县| 博乐市| 鹤庆县| 通榆县| 玉山县| 闻喜县| 繁昌县| 老河口市| 大同县| 阿城市| 云浮市| 台东市| 镇坪县| 庆阳市| 藁城市| 红安县| 沈阳市| 昂仁县| 会昌县| 潼南县| 卓资县| 蒙自县| 庆阳市| 梅河口市| 无棣县| 巫山县| 江城| 额尔古纳市| 白水县| 保山市| 宁明县| 洛南县| 象州县| 汝城县| 贡嘎县| 西吉县| 四川省| 酉阳| 温泉县| 洪泽县| 喀什市| 金山区| 阿合奇县| 洛阳市| 彩票| 阜宁县| 蓝山县| 巴青县| 息烽县| 贡山| 乌什县| 新昌县| 廊坊市| 镇康县| 桃园市| 乌鲁木齐县| 太谷县| 灵台县| 禹城市| 怀来县| 万源市| 鸡泽县| 湘潭县| 新晃| 无棣县| 益阳市| 曲水县| 武强县| 建瓯市| 内乡县| 临泉县| 当涂县| 建昌县| 和顺县|