文章摘要
王颖超,柳青青,李洪平,赵 红.海表面盐度的高精度预测模型[J].海洋科学进展,2021,39(1):37-44
  
DOI:10.3969/j.issn.1671-6647.2021.01.005
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海表面盐度的高精度预测模型
High-Precision Prediction Model for Sea Surface Salinity
作者单位
王颖超1,柳青青2,李洪平1,赵 红3 1. 中国海洋大学 信息科学与工程学院山东 青岛 266100
2. 青岛大学 商学院山东 青岛 266100
3. 中国海洋大学 数学科学学院山东 青岛 266100 
中文摘要:
      为了建立高精度的海洋表面盐度预测模型,采用BP神经网络的方法,针对SMOS卫星level 1C级亮度温度数据和辅助数据建立了一种海表面盐度预测模型,以ARGO浮标观测值作为海表盐度实测值来检验新模型预测结果的准确度,同时利用验证集对模型的精度进行验证。结果表明:通过新模型预测的海表盐度(SSS0)比SMOS卫星的3个粗糙度模型盐度产品(SSS1,SSS2,SSS3)精度高;SSS0,SSS1,SSS2,SSS3与ARGO浮标实测盐度(SSSARGO)的均方根误差分别为0.847 3,2.041 7,2.028 8和2.080 5,平均绝对误差分别为0.755 3,1.422 6,1.421 6和1.456 6,SSS0与SSSARGO的均方根误差和绝对平均误差值都明显小于SSS1,SSS2和SSS3与SSSARGO的;由此可见,建立的海表盐度预测模型精度较高。新模型为海表盐度的反演算法提供了新思路。
中文关键词: 海表盐度  BP神经网络  SMOS卫星  ARGO浮标
英文摘要:
      To build a high precision ocean surface salinity prediction model, the back propagation (BP) neural network method is utilized to establish a sea surface salinity prediction model based on soil moisture and ocean salinity (SMOS) satellite level 1C brightness and temperature data and auxiliary data. The array for real time geostrophic oceanography (ARGO) buoy observations are used as the measured value of sea surface salinity to test the accuracy of the new modelʼs prediction results, and the verification set is used to verify the accuracy of the model. The results show that the sea surface salinity predicted by the new model (referred to as SSS0) is more accurate than the three roughness model salinity products of soil moisture ocean salinity (SMOS) satellites (referred to as SSS1, SSS2, and SSS3). The accuracy of the root mean square errors of SSS0, SSS1, SSS2, SSS3 compared to SSSARGO are 0.847 3, 2.041 7, 2.028 8 and 2.080 5, and the absolute average errors are 0.755 3, 1.422 6, 1.421 6 and 1.456 6. Both of the root mean square error and absolute average error of SSS0 are significantly smaller than SSS1, SSS2, and SSS3. Therefore, it shows that the sea surface salinity prediction model established in this paper has higher accuracy, and it provides a novel way for generating the sea surface salinity inversion algorithm.
英文关键词: sea surface salinity  the back propagation (BP) neural network  the soil moisture and ocean salinity (SMOS) satellite  the array for real time geostrophic oceanography (ARGO) buoy
基金项目:国家自然科学基金项目——稳健主成分回归的数值方法研究(11871444)
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