Validation of sea surface temperature from the geostationary meteorological satellite Meteosat-8/SEVIRI over the Indian Ocean

doi:10.3969/j.issn.1001-909X.2024.02.003

Abstract

Abstract:

Sea surface temperature (SST) is a key climate variable in oceanographic and meteorological research, widely applied in studies of ocean-atmosphere interactions, ocean mixing, boundary layer processes, and ocean state forecasting. The hourly SST data provided by the European geostationary meteorological satellite Meteosat-8/SEVIRI (M8) is an important data source for these studies. However, the spatiotemporal variations in the errors of SST data from M8 are not yet clear. To assess the reliability and applicability of SST data from M8, this study uses in-situ SST data from the iQuam quality monitoring platform which includes data from ships, drifting buoys, and Argo floats, to validate the hourly SST data from M8 in the Indian Ocean region. The results show that the average bias between M8 and the three types of in-situ data ranges from -0.06 to -0.10 ℃, the root mean square error ranges from 0.48 to 1.03 ℃, and the coefficient of determination ranges from 0.96 to 0.99. Among these, drifting buoys have the most matchups with M8 and the widest coverage, making them an ideal validation data source. Analysis of the spatiotemporal distribution of SST data biases from M8 reveals a -0.6 ℃ bias at night in the northwestern Arabian Sea and northwestern Bay of Bengal, with larger negative biases during the day in these areas, and a bias exceeding -1.0 ℃ during the day in parts of the 40°S-60°S region. SST data from M8 tends to show maximum positive biases in summer and minimum negative biases during the spring-to-summer transition period.

Key words: Indian Ocean, Meteosat-8/SEVIRI, sea surface temperature, validation

CLC Number:

P731.11

KANG Zhengwu, TU Qianguang, YAN Yunwei, XING Xiaogang. Validation of sea surface temperature from the geostationary meteorological satellite Meteosat-8/SEVIRI over the Indian Ocean[J]. Journal of Marine Sciences, 2024, 42(2): 26-39.

Figures/Tables 11

Fig.1 Spatial distribution of the number of matched SST data points between M8 and three in-situ platforms

Fig.2 Histograms of SST (a), latitude (b) and longitude (c) distributions for paired M8 and three in-situ platforms (Grouped by 1 ℃ for SST and 3° for latitude and longitude,histograms normalized to a sum of 1.)

Fig.3 Monthly mean variation (a, c, e) and hourly mean variation (b, d, f) of matching amount between M8 and three in-situ platforms

Tab.1 Statistical table of SST errors between M8 and three in-situ platforms

实测平台	匹配量/组	平均偏差/℃	均方根误差/℃	决定系数
船只	124 089	-0.10	1.03	0.96
漂流浮标	1 208 438	-0.06	0.48	0.99
Argo浮标	8 067	-0.10	0.53	0.99

Fig.4 Scatter density map of SST between M8 and three in-situ platforms

Fig.5 Spatial distribution map of average deviation and root mean square error of SST between M8 and three in-situ platforms

Fig.6 Statistical graphs of monthly and hourly mean deviation and root mean square error of SST between M8 and three in-situ platforms

Tab.2 Statistics of SST errors between M8 and drifting buoys for night and day

时段	匹配量/组	平均偏差/℃	偏差中位数/℃	均方根误差/℃	鲁棒标准差/℃
夜间	470 166	-0.08	-0.05	0.47	0.41
白天	566 952	-0.04	0.00	0.48	0.40

Fig.7 Spatial distribution of matched number, deviation median, and Robust standard deviation of SST between M8 and drifting buoys during night and day

Fig.8 Monthly average variation of SST errors between M8 and drifting buoys during night and day

Fig.9 Variations in mean deviation, root mean square error and percentage of matched pairs between M8 and drifting buoys as functions of measured SST, secant of satellite zenith angle, longitude and latitude

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