Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiote...Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.展开更多
基于2002-2020年的Jason系列卫星数据,利用一种高风速计算方法得到431次飓风的风速信息。在此基础上,利用基于再分析的美国飓风中心(The National Hurricane Center, NHC)大西洋和东北太平洋飓风最佳路径数据集进行比对分析,对高风速计...基于2002-2020年的Jason系列卫星数据,利用一种高风速计算方法得到431次飓风的风速信息。在此基础上,利用基于再分析的美国飓风中心(The National Hurricane Center, NHC)大西洋和东北太平洋飓风最佳路径数据集进行比对分析,对高风速计算方法进行了综合评估。文中计算和评估结果显示,8.03~66.93 m/s飓风风速RMSE优于4 m/s;卫星观测风速和NHC飓风最佳路径数据相关系数在0.9以上。这表明文中方法是可靠的,具备热带气旋高风速观测能力。同时,文中结果显示,飓风观测期间几乎都伴随着不同程度的降雨,当风速大于50 m/s时,卫星观测点均处于中到暴雨的环境下。文中研究证明了利用卫星雷达高度计和校正辐射计这对主被动微波遥感器联合获取极端海洋环境下风速信息的可行性,这为提升台风或飓风风速观测能力提供了一种有潜力的技术手段。另外,统计结果显示飓风期间风速和气压也具备很好的线性相关性,利用这种关系可以基于卫星获取的高风速信息来快速计算得到热带气旋中心气压,这将形成卫星对热带气旋风速和中心气压的同步获取能力。展开更多
1999–2021年,我国历经12次北极科学考察,获取了大量宝贵的极地海洋水文调查资料。历年以来的CTD调查设备为MARKⅢC CTD(第1、2次北极科学考察)和SBE911 Plus CTD(第3-12次北极科学考察),通过定点投放的方式获取海洋垂直断面的电导率、...1999–2021年,我国历经12次北极科学考察,获取了大量宝贵的极地海洋水文调查资料。历年以来的CTD调查设备为MARKⅢC CTD(第1、2次北极科学考察)和SBE911 Plus CTD(第3-12次北极科学考察),通过定点投放的方式获取海洋垂直断面的电导率、温度和深度数据,调查范围包含北极中央航道、楚科奇海、楚科奇海台、加拿大海盆、白令海峡、白令海、北欧海、北冰洋太平洋扇区等重点海域。通过数据格式转换、数据编辑、滤波和滞后处理、电导修正、起伏校正、衍生参数计算、生成等深间距文件、输出ASCII文件等过程对原始数据进行处理和质量控制,形成垂向分辨率为1 m,包含时间、经度、纬度、压强、深度、温度、盐度、密度、声速和位温等要素的数据集。通过比较同一变量两个传感器之间的数据差值和绘制T-S点聚图进一步评估处理后的数据质量。经评估,处理后的数据质量良好,剔除异常值后的双温盐传感器之间的差值在合理范围内。本数据集为北冰洋水团分布、环流研究、海洋环境变化及全球气候变化等提供了宝贵的现场资料。展开更多
基金supported by the National Key Research and Development Program of China[grant number 2022YFE0106800]an Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 311024001]+3 种基金a project supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number SML2023SP209]a Research Council of Norway funded project(MAPARC)[grant number 328943]a Nansen Center´s basic institutional funding[grant number 342624]the high-performance computing support from the School of Atmospheric Science at Sun Yat-sen University。
文摘Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.
文摘基于2002-2020年的Jason系列卫星数据,利用一种高风速计算方法得到431次飓风的风速信息。在此基础上,利用基于再分析的美国飓风中心(The National Hurricane Center, NHC)大西洋和东北太平洋飓风最佳路径数据集进行比对分析,对高风速计算方法进行了综合评估。文中计算和评估结果显示,8.03~66.93 m/s飓风风速RMSE优于4 m/s;卫星观测风速和NHC飓风最佳路径数据相关系数在0.9以上。这表明文中方法是可靠的,具备热带气旋高风速观测能力。同时,文中结果显示,飓风观测期间几乎都伴随着不同程度的降雨,当风速大于50 m/s时,卫星观测点均处于中到暴雨的环境下。文中研究证明了利用卫星雷达高度计和校正辐射计这对主被动微波遥感器联合获取极端海洋环境下风速信息的可行性,这为提升台风或飓风风速观测能力提供了一种有潜力的技术手段。另外,统计结果显示飓风期间风速和气压也具备很好的线性相关性,利用这种关系可以基于卫星获取的高风速信息来快速计算得到热带气旋中心气压,这将形成卫星对热带气旋风速和中心气压的同步获取能力。
文摘1999–2021年,我国历经12次北极科学考察,获取了大量宝贵的极地海洋水文调查资料。历年以来的CTD调查设备为MARKⅢC CTD(第1、2次北极科学考察)和SBE911 Plus CTD(第3-12次北极科学考察),通过定点投放的方式获取海洋垂直断面的电导率、温度和深度数据,调查范围包含北极中央航道、楚科奇海、楚科奇海台、加拿大海盆、白令海峡、白令海、北欧海、北冰洋太平洋扇区等重点海域。通过数据格式转换、数据编辑、滤波和滞后处理、电导修正、起伏校正、衍生参数计算、生成等深间距文件、输出ASCII文件等过程对原始数据进行处理和质量控制,形成垂向分辨率为1 m,包含时间、经度、纬度、压强、深度、温度、盐度、密度、声速和位温等要素的数据集。通过比较同一变量两个传感器之间的数据差值和绘制T-S点聚图进一步评估处理后的数据质量。经评估,处理后的数据质量良好,剔除异常值后的双温盐传感器之间的差值在合理范围内。本数据集为北冰洋水团分布、环流研究、海洋环境变化及全球气候变化等提供了宝贵的现场资料。
