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(yǔ)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: 黎城县| 孟津县| 阜平县| 德州市| 城固县| 大埔区| 哈密市| 桑日县| 嫩江县| 冀州市| 绥芬河市| 包头市| 松江区| 漳平市| 华安县| 龙川县| 平湖市| 大安市| 黑龙江省| 若尔盖县| 海南省| 四平市| 图木舒克市| 五家渠市| 长子县| 施秉县| 安仁县| 石家庄市| 江西省| 天峨县| 大同市| 五原县| 玛曲县| 沧源| 沧源| 兖州市| 东方市| 德惠市| 张家川| 闽清县| 淮安市| 北京市| 三原县| 泰顺县| 湖口县| 富顺县| 闵行区| 金华市| 滨州市| 永康市| 太和县| 栖霞市| 石棉县| 塔城市| 永川市| 恩平市| 永川市| 沂南县| 历史| 蚌埠市| 邮箱| 彝良县| 七台河市| 垫江县| 大同市| 民权县| 手机| 灵宝市| 苗栗市| 阳城县| 仙游县| 庆安县| 涡阳县| 海南省| 义乌市| 宜兴市| 手机| 绥德县| 云阳县| 南安市| 广安市| 盐山县|