| 基于AHI观测的全天气条件海表温度反演 |
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| 引用本文： | 王朋,诸葛小勇,陈宝君,陆一磊,肖宇昕,夏凡.基于AHI观测的全天气条件海表温度反演[J].气象科学,2020,40(2):249-256. |
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| 作者姓名： | 王朋 诸葛小勇 陈宝君 陆一磊 肖宇昕 夏凡 |
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| 作者单位： | 南京大学 大气科学学院, 南京 210023;南京大学 大气科学学院, 南京 210023;南京市高淳区气象局, 南京 211300 |
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| 基金项目： | 国家自然科学基金青年基金资助项目（41505086）；南京大学大学生创新训练资助计划（X201610284006） |
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| 摘 要： | 基于搭载在日本新一代静止气象卫星Himawari-8上的先进葵花成像仪(Advanced Himawari Imager,AHI)观测资料,研究了高时空分辨率的、全天气条件的海表温度(Sea Surface Temperature,SST)反演算法。本算法包括两步:第一步,根据云检测算法划分晴空和云区,然后利用非线性SST(NLSST)方程由红外亮温估计晴空SST;第二步,在有云区,先由前5 d同一时刻的晴空SST进行初步补缺,然后再利用Barnes插值完善云区SST估计和进行异常点平滑。最终得到时间分辨率为10 min、空间分辨率为0.05°的全天气条件海温分布。利用移动浮标的观测SST验证,晴空区SST估计的均方根误差(Root Mean Square Error,RMSE)和平均误差(Mean Error,ME)分别为0.857 K和0.017 K。全天气条件SST估计的RMSE和ME分别为0.872 K和-0.005 K。
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| 关 键 词： | 海表温度 反演 Himawari-8 全天气条件 |
| 收稿时间： | 2018/9/3 0:00:00 |
| 修稿时间： | 2018/11/24 0:00:00 |
Retrieval of sea surface temperature in all sky conditions based on AHI observations |
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| WANG Peng,ZHUGE Xiaoyong,CHEN Baojun,LU Yilei,XIAO Yuxin,XIA Fan.Retrieval of sea surface temperature in all sky conditions based on AHI observations[J].Scientia Meteorologica Sinica,2020,40(2):249-256. |
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| Authors: | WANG Peng ZHUGE Xiaoyong CHEN Baojun LU Yilei XIAO Yuxin XIA Fan |
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| Institution: | School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China;School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China;Gaochun District Meteorological Bureau of Nanjing, Nanjing 211300, China |
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| Abstract: | Based on the observations of Advanced Himawari Imager (AHI) carried by the Japanese new generation of geostationary meteorological satellite, Himawari-8, the Sea Surface Temperature (SST) retrieval algorithm with a high spatiotemporal resolution in all sky conditions was studied. The algorithm consists of two steps. Firstly, the clear sky and cloudy sky are separated using a cloud mask algorithm, and then the non-linear SST equation is used to estimate the clear-sky SST from the AHI infrared brightness temperature. Secondly, the clear-sky SST at the same time of previous five days are used as a first guess of the cloudy-sky SST, and then the Barnes Interpolation is employed to further conduct the cloudy-sky SST estimation and perform an anomalous point smoothing. Finally, the SST distributions in all sky conditions with 10 min temporal and 0.05° spatial resolutions are obtained. Validated with the buoy SST, the Root Mean Square Error (RMSE) and Mean Error (ME) for the clear-sky SSTs are 0.857 K and 0.017 K, respectively. The RMSE and ME for SSTs in all sky conditions algorithm are 0.872 K and -0.005 K, respectively. |
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| Keywords: | SST retrieval Himawari-8 all-sky |
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