| 利用西北印度洋船测数据评估基于卫星的海表面温度 |
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| 引用本文： | 杨广,何海伦,王渊,韩喜球,王叶剑.利用西北印度洋船测数据评估基于卫星的海表面温度[J].海洋学报(英文版),2016,35(11):52-58. |
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| 作者姓名： | 杨广 何海伦 王渊 韩喜球 王叶剑 |
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| 作者单位： | 浙江大学海洋学院, 舟山, 316021;卫星海洋环境动力学国家重点实验室, 国家海洋局第二海洋研究所, 杭州, 310012,卫星海洋环境动力学国家重点实验室, 国家海洋局第二海洋研究所, 杭州, 310012,卫星海洋环境动力学国家重点实验室, 国家海洋局第二海洋研究所, 杭州, 310012,海底科学重点实验室, 国家海洋局第二海洋研究所, 杭州, 310012,海底科学重点实验室, 国家海洋局第二海洋研究所, 杭州, 310012 |
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| 摘 要： | 本文描述了一次夏季在西北印度洋进行的调查船水文测量，用船测数据评估卫星海面表温度，并寻找影响海表面温度误差的主要因素。我们考虑了两种卫星数据，第一种是微波遥感产品——热带降雨测量任务微波成像仪TMI数据，另外一种是融合了微波，红外线，以及少部分观测数据的融合数据产品——可处理海表温度和海冰分析OSTIA数据。结果表明融合数据的日平均海表面温度的平均误差和均方根误差都比微波遥感小。这一结果证明了融合红外线遥感，微波遥感以及观测数据来提高海表面温度数据质量的必要性。此外，我们分析了海表面温度误差与各项水文参数之间的相关关系，包括风速，大气温度，想对湿度，大气压力，能见度。结果表明风速与TMI海表面温度误差的相关系数最大。而大气温度是影响OSTIA海表面温度误差最重要的因素；与此同时，想对湿度与海表面温度误差的相关系数也很高。
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| 关 键 词： | 调查船测量 海表面温度 西北印度洋 热带降雨测量任务微波成像仪 可处理海表温度和海冰分析 |
| 收稿时间： | 2015/10/26 0:00:00 |
| 修稿时间： | 2016/1/27 0:00:00 |
Evaluating a satellite-based sea surface temperature by shipboard survey in the Northwest Indian Ocean |
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| YANG Guang,HE Hailun,WANG Yuan,HAN Xiqiu and WANG Yejian.Evaluating a satellite-based sea surface temperature by shipboard survey in the Northwest Indian Ocean[J].Acta Oceanologica Sinica,2016,35(11):52-58. |
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| Authors: | YANG Guang HE Hailun WANG Yuan HAN Xiqiu and WANG Yejian |
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| Institution: | 1.Ocean College, Zhejiang University, Zhoushan 316021, China;State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China2.State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China3.Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China |
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| Abstract: | A summer-time shipboard meteorological survey is described in the Northwest Indian Ocean. Shipboard observations are used to evaluate a satellite-based sea surface temperature (SST), and then find the main factors that are highly correlated with errors. Two satellite data, the first is remote sensing product of a microwave, which is a Tropical Rainfall Measuring Mission Microwave Imager (TMI), and the second is merged data from the microwave and infrared satellite as well as drifter observations, which is Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). The results reveal that the daily mean SST of merged data has much lower bias and root mean square error as compared with that from microwave products. Therefore the results support the necessary of the merging infrared and drifter SST with a microwave satellite for improving the quality of the SST. Furthermore, the correlation coefficient between an SST error and meteorological parameters, which include a wind speed, an air temperature, a relative humidity, an air pressure, and a visibility. The results show that the wind speed has the largest correlation coefficient with the TMI SST error. However, the air temperature is the most important factor to the OSTIA SST error. Meanwhile, the relative humidity shows the high correlation with the SST error for the OSTIA product. |
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| Keywords: | shipboard survey sea surface temperature Northwest Indian Ocean Tropical Rainfall Measuring Mission Microwave Imager Operational Sea Surface Temperature and Sea Ice Analysis |
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