The Improvement of FY-3B/VIRR SST Algorithm and Its Accuracy

Wang Sujuan; Cui Peng; Zhang Peng; Ran Maonong; Lu Feng; Wang Weihe

Vol.25, NO.6, 2014

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2014 > 25(6): 701-710

Wang Sujuan, Cui Peng, Zhang Peng, et al. The improvement of FY-3B/VIRR SST algorithm and its accuracy. J Appl Meteor Sci, 2014, 25(6): 701-710.

Citation:

Wang Sujuan, Cui Peng, Zhang Peng, et al. The improvement of FY-3B/VIRR SST algorithm and its accuracy. J Appl Meteor Sci, 2014, 25(6): 701-710.

Wang Sujuan, Cui Peng, Zhang Peng, et al. The improvement of FY-3B/VIRR SST algorithm and its accuracy. J Appl Meteor Sci, 2014, 25(6): 701-710.

Citation:

Wang Sujuan, Cui Peng, Zhang Peng, et al. The improvement of FY-3B/VIRR SST algorithm and its accuracy. J Appl Meteor Sci, 2014, 25(6): 701-710.

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The Improvement of FY-3B/VIRR SST Algorithm and Its Accuracy

1.
National Satellite Meteorological Center, Beijing 100081
2.
Beijing Huayun Shinetek Satellite Application Engineering Technology Company Limited, Beijing 100081

Received Date: 2014-03-12
Rev Recd Date: 2014-09-15
Publish Date: 2014-11-30

Abstract

Abstract

The evolution of sea surface temperature (SST) algorithms is introduced and a set of SST regression formalisms are given. Some improvements are made based on operational SST algorithm from FY-3B meteorological satellite visible and infrared radiometer (VIRR) data. On matching algorithm, quality controlled in situ data from the in situ quality monitor (iQUAM) is used to improve the input data precision of regression. Fields of matchup database (MDB) are enlarged to provide the convenience for error analysis. Pixels with "confident clear" flag in FY-3B/VIRR cloud mask (CLM) products are matched up to form gross matchups, and then tightly filtered by some tests to form tight matchups, which make the sample selection more reasonable. On regression algorithm, based on least-square regression used for the early operational SST product, the robust regression is developed, and its performance is tested by NOAA-19/AVHRR MDB of 2010. It shows that the precision of SST is increased by 21% in daytime with split-window non-linear SST (NL) algorithm and 30% in nighttime with triple-window MC (TC) algorithm. On retrieval algorithm, the spatial uniformity test and climate reference test are introduced, the unidentified cloud (especially at night) is excluded and the SST retrieval precision is improved.A set of SST regression formalisms are tested based on NOAA-19/AVHRR 2010 MDB. It shows NL is the best algorithm for daytime while TC is the best algorithm for nighttime, which is accordant with NESDIS/STAR. The monthly MDB is created from FY-3B/VIRR measurements paired with coincident SST measurements from buoys data.The same regression analysis method is also used on FY-3B/VIRR MDB. Comparing three daytime SST algorithms and five nighttime SST algorithms, the best algorithm to retrieve FY-3B/VIRR SST is NL both in daytime and nighttime. It shows for FY-3B/VIRR nighttime TC, the contribution of 3.7 μm band is smaller than split-window bands, and the calibration of 3.7 μm band has stripe phenomenon. A three-month MDB from October to December in 2012 is used to derive coefficients. An independent MDB from January to March in 2013 is used to access the accuracy of the best NL algorithm for FY-3B/VIRR. Based on matchup analyses, the root mean square error (RMSE) between FY-3B/VIRR SST and in situ SST is 0.41℃ (NL_D) and 0.43℃ (NL_N). Compare with Daily Optimum Interpolation SST (OISST), the RMSE of FY-3B/VIRR SST is 1.45℃ (NL_D) and 1.5℃ (NL_N). When the absolute difference between FY-3B/VIRR SST and OISST is within 2℃, the RMSE is 0.82℃ (NL_D) and 0.84℃ (NL_N).
- FY-3B meteorological statellite;
- visible and infrared radiometer (VIRR);
- sea surface temperature;
- algorithm

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References(26)

Figures and Tables

Fig. 1 Images of FY-3B/VIRR at 1100 UTC 30 April 2013(a) bright temperature image of 10.8 μm, (b) cloud image, (c) SST image

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Fig. 2 Statistics time series of three daytime SST algorithms with respect to in situ SST from August 2012 to March 2013

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Fig. 3 Statistics time series of five nighttime SST algorithms with respect to in situ SST from August 2012 to March 2013

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图 4 2013年4月26日11:55 SST图像

(a) 基于MDB_V2的TC算法，(b) 基于MDB_V1的NL算法

Fig. 4 SST images at 1155 UTC 26 April 2013

(a) TC based on MDB_V2, (b) NL based on MDB_V1

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Table 1 List of acronyms of FY-3B/VIRR SST Algorithm

中文名称	英文名称	算法缩写
分裂窗多通道海温算法	split-window multichannel SST	MC
分裂窗带二次项的多通道海温算法	split-window quadratic term multichannel SST	QD
分裂窗非线性海温算法	split-window nonlinear SST	NL
三窗多通道海温算法	triple-window multichannel SST	TC
双窗非线性海温算法	dual-window nonlinear SST	DN

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Table 2 The regression information of TCSST Algorithm

卫星名称	时间	样本量	a₀	a₁	a₂	a₃	a₄	a₅	均方根误差/℃	R²
NOAA-19	2010-12	6759	-276.284	0.54182	1.05278	-0.57961	0.14744	1.27217	0.21070	0.99950
FY-3B (MDB_V1)	2012-12	8445	-268.239	3.99199	-0.04842	-2.97015	0.06730	1.14176	0.55932	0.99242
FY-3B (MDB_V2)	2012-12	5496	-283.496	1.81021	0.82827	-1.59888	0.08934	1.59769	0.36817	0.99272

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Table 3 Validation statistics of FY-3B/VIRR SST algorithms in April 2013

样本类型	样本量	算法	偏差/℃	绝对偏差/℃	均方根误差/℃
全部样本	524459	MC_D	-0.53	1.17	1.56
	516443	QD_D	-0.51	1.14	1.54
	555289	NL_D	-0.62	1.05	1.45
偏差在2℃以内的样本	436538	MC_D	-0.20	0.74	0.90
	431521	QD_D	-0.18	0.71	0.87
	474085	NL_D	-0.24	0.66	0.82
全部样本	567287	DN_N	-0.17	1.41	1.71
	547859	MC_N	-0.20	1.20	1.57
	534992	QD_N	-0.18	1.19	1.56
	501166	TC_N	-0.26	1.15	1.52
	618061	NL_N	-0.43	1.14	1.50
偏差在2℃以内的样本	422498	DN_N	0.15	0.91	1.04
	442431	MC_N	0.12	0.76	0.91
	434138	QD_N	0.16	0.75	0.89
	410531	TC_N	0.06	0.73	0.88
	515003	NL_N	0.04	0.71	0.84

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Table 4 Monthly validation statistics for FY-3B/VIRR NL in April 2013

样本类型	算法	平均有效样本量	偏差/℃	绝对偏差/℃	均方根误差/℃
全部样本	NL_D	555289	-0.62	1.05	1.45
全部样本	NL_N	618061	-0.43	1.14	1.50
偏差在2℃以内的样本	NL_D	474085	-0.24	0.66	0.82
偏差在2℃以内的样本	NL_N	515003	0.04	0.71	0.84

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