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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

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    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).
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  • 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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    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
    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
    NL_N 515003 0.04 0.71 0.84
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    • Received : 2014-03-11
    • Accepted : 2014-09-14
    • Published : 2014-11-29
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