一种基于静止卫星的海面风矢量估测方法  

A Method to Estimate Sea Surface Wind Vectors Using Geostationary Satellites

作　　者：张云开 徐娜 翟晓春[2,3] 张鹏 Zhang Yunkai;Xu Na;Zhai Xiaochun;Zhang Peng(Chinese Academy of Meteorological Sciences,Beijing 100081;Innovation Center for Fengyun Meteorological Satellite(FYSIC),Beijing 100081;Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites,National Satellite Meteorological Center(National Center for Space Weather),CMA,Beijing 100081)

机构地区：[1]中国气象科学研究院,北京100081 [2]许健民气象卫星创新中心,北京100081 [3]中国气象局中国遥感卫星辐射测量和定标重点开放实验室/国家卫星气象中心(国家空间天气监测预警中心),北京100081

出　　处：《应用气象学报》2024年第2期225-236,共12页Journal of Applied Meteorological Science

基　　金：国家重点研发计划(2022YFB3903000,2022YFB3903003)。

摘　　要：参考大气动力学理论中风随高度、纬度的分布特征,提出一种基于静止卫星低层大气导风利用全连接神经网络估测海面风的新思路,构建基于卫星遥感数据的全连接神经网络海面风矢量估测模型,实现基于大气导风的海面风估测。基于GOES-16先进基线成像仪可见光通道0.5 km分辨率大气导风开展试验,并与2021年1月1日—12月31日北美近海岸和海上93个美国国家数据浮标中心浮标数据比对,结果表明:全连接神经网络估算得到基于大气导风的海面风风速均方根误差不大于1.5 m·s^(-1),较传统模型降低0.24 m·s^(-1)。将模型应用于飓风场景,通过与2022年3个北大西洋飓风和3个东太平洋飓风共13个时次的再分析数据比对表明:基于大气导风的海面风风速均方根误差不大于1.1 m·s^(-1),相较于传统经验模型降低0.04 m·s^(-1),在低风速区无系统性偏差。Sea surface wind(SSW)is an essential physical parameter in the ocean and atmosphere,playing an irreplaceable role in hydrological and energy cycles,as well as in global and local climate systems.Polar-orbiting satellite instruments can gather a large amount of SSW information by observing surface roughness or wave height.Although observations from polar-orbiting satellites can cover the globe,there is a significant temporal gap for observing a fixed region.However,a geostationary satellite enables a relatively high observation frequency to accomplish this mission.Due to limitations in resolution,power consumption,and other factors,it is difficult for geostationary satellites to directly retrieve SSW.Nevertheless,it can obtain wind vectors at different altitudes by tracking the movement of clouds or clear-sky water vapor gradients in continuous satellite imagery,which is called atmospheric motion vector(AMV).There is a strong correlation between low-level AMV and SSW,and SSW could be estimated from low-level AMV.Previous studies estimated SSW based on low-level AMV mainly by empirical methods,which is unable to take the variation of AMV with height and latitude into consideration.Therefore,a new method based on a fully-connected neural network(FCNN)is proposed to address this issue.The theory of atmospheric dynamics,which explains how wind varies with altitude and latitude,is referenced,and FCNN is constructed by selecting physical parameters with strong causal relationships from geostationary satellite AMV information.The experiment is performed using GOES-16 advanced baseline imager(ABI)visible band AMV with a resolution of 0.5 km.After completing the wind estimation and comparing it with data from 93 National Data Buoy Center buoys nearshore or offshore North America from 1 January to 31 December in 2021,results show that root mean square error(RMSE)of the estimated wind speed from FCNN is less than 1.5 m·s^(-1).This represents a reduction of up to 0.24 m·s^(-1)compared to the empirical model.Additionally,the

关 键 词：海面风矢量 大气导风 全连接神经网络 低层大气 

分 类 号：P714.2[天文地球—海洋科学]