FY-4A/AGRI Sea Surface Temperature Product and Quality Validation

Cui Peng; Wang Sujuan; Lu Feng; Xiao Meng

doi:10.11898/1001-7313.20230301

Vol.34, NO.3, 2023

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Article Navigation > Journal of Applied Meteorological Science > 2023 > 34(3): 257-269

Cui Peng, Wang Sujuan, Lu Feng, et al. FY-4A/AGRI sea surface temperature product and quality validation. J Appl Meteor Sci, 2023, 34(3): 257-269. DOI: 10.11898/1001-7313.20230301.

Citation:

Cui Peng, Wang Sujuan, Lu Feng, et al. FY-4A/AGRI sea surface temperature product and quality validation. J Appl Meteor Sci, 2023, 34(3): 257-269. DOI: 10.11898/1001-7313.20230301.

Cui Peng, Wang Sujuan, Lu Feng, et al. FY-4A/AGRI sea surface temperature product and quality validation. J Appl Meteor Sci, 2023, 34(3): 257-269. DOI: 10.11898/1001-7313.20230301.

Citation:

Cui Peng, Wang Sujuan, Lu Feng, et al. FY-4A/AGRI sea surface temperature product and quality validation. J Appl Meteor Sci, 2023, 34(3): 257-269. DOI: 10.11898/1001-7313.20230301.

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FY-4A/AGRI Sea Surface Temperature Product and Quality Validation

DOI: 10.11898/1001-7313.20230301

Cui Peng^{1, 2, 3},
Wang Sujuan^{1, 2, 3
,
,},
Lu Feng^{1, 2, 3},
Xiao Meng^{1, 2, 3}

1.
National Satellite Meteorological Center/National Center for Space Weather, Beijing 100081
2.
Innovation Center for Fengyun Meteorological Satellite, Beijing 100081
3.
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing 100081

Received Date: 2022-12-30
Rev Recd Date: 2023-03-13
Publish Date: 2023-05-31

Abstract

Abstract

Fengyun-4A (FY-4A) is the first satellite of China's second-generation geostationary orbit meteorological satellites. The advanced geostationary radiation imager (AGRI), a multiple channel radiation imager, is one of the primary payloads onboard FY-4A. As one basic quantitative remote sensing product, the operational sea surface temperature (SST) is derived with the split-window nonlinear SST (NLSST) algorithm in real time. The operational NLSST procedures and products are described in detail. The FY-4A/AGRI SST products provide full-disk SST with spatial resolution of 4 km at the nadir. The quality level information is delivered with each pixel to provide information about the conditions of the processing. Quality level for each pixel information is defined as follows: Excellent, good, bad and unprocessed (cloud, land, no satellite data etc.). The users can select the SST data with certain quality level according to their application purposes (e.g., for climate-related studies, only the SST data with the excellent quality level in the time series are used, and for identifying and tracking specific ocean features, users may be more tolerant of lower-quality SST data). The accuracy of FY-4A/AGRI SST algorithm is assessed by determining the standard deviation and bias errors from the regression procedure of the matchup database between satellite data and quality controlled SST data from NOAA in situ SST quality monitor, from July 2021 to June 2022. The validation methods and results are described in detail. The matchup database combining FY-4A/AGRI measurements and in situ SST have been built on a routine basis. At the stage of the matchup database in association with the drifter temperatures, the matchup database is composed of pixels under clear sky conditions. The FY-4A/AGRI SST data with excellent quality level are compared with drifting and tropical moored buoy data. The matchup space-time window is 4 km and 30 min from the buoy location to the center of the SST pixel. The comparison shows a bias of -0.45 to -0.42℃ and a standard deviation of 0.81-0.88℃ for FY-4A/AGRI SST with excellent quality. The correlation coefficients between FY-4A/AGRI SST and buoy SST are better than 0.985. The FY-4A/AGRI SST are also compared with the ACSPO SST produced at NOAA/STAR from the Himawari-8/AHI (advanced Himawari imager). The comparison shows a bias of -0.26 to -0.07℃ and a standard deviation of 0.68-0.82℃ with excellent quality level. The correlation coefficients between FY-4A/AGRI SST and Himawari-8/AHI are better than 0.985. The correlation coefficients shows that there is a good correlation between FY-4A/AGRI SST and Himawari-8/AHI SST.
- FY-4A/AGRI;
- sea surface temperature;
- quality control

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References(36)

Figures and Tables

Fig. 1 Histogram of difference between FY-4A/AGRI SST and buoy SST from Jul 2021 to Jun 2022

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Fig. 2 Scatter density map of FY-4A/AGRI SST and buoy SST from Jul 2021 to Jun 2022

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Fig. 3 Biases in 5°×5° square of FY-4A/AGRI SST against buoy SST from Jul 2021 to Jun 2022

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Fig. 4 Time series of bias and standard deviation of FY-4A/AGRI SST relative to buoy SST from Jul 2021 to Jun 2022

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Fig. 5 Time series of bias and standard deviation of excellent quality FY-4A/AGRI SST relative to Himawari-8/AHI SST from Jul 2021 to Jun 2022

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图 6 FY-4A/AGRI海表温度和Himawari-8/AHI海表温度散点图

Fig. 6 Scatter density map of FY-4A/AGRI SST and Himawari-8/AHI SST

(a)1 Jul, 1 Aug and 1 Sep in 2021, (b)1 Oct, 1 Nov and 1 Dec in 2021, (c)1 Jan, 1 Feb and 1 Mar in 2022, (d)1 Apr, 1 May and 1 Jun in 2022

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Table 1 SST deviation of FY-4A/AGRI NLSST algorithm

统计量	白天	夜间
偏差/℃	0.01	0.05
绝对偏差/℃	0.5	0.53
标准差/℃	0.61	0.66
样本量	7381	7803

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Table 2 Error information of FY-4A/AGRI SST relative to buoy SST

时段	统计量	优	良	差
白天	平均偏差/℃	-0.45	-1.00	-2.25
	标准差/℃	0.81	0.94	1.94
	样本量	171473	81570	35607
夜间	平均偏差/℃	-0.42	-0.99	-2.94
	标准差/℃	0.88	1.03	1.99
	样本量	199364	92084	76844
晨昏	平均偏差/℃	-0.42	-1.02	-2.80
	标准差/℃	0.85	0.99	1.97
	样本量	28501	12386	8598

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