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The Improvement of FY-3B/VIRR SST Algorithm and Its Accuracy
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摘要: 该文介绍了卫星观测海表温度 (SST) 算法的发展历程，给出了所用SST算法的回归模型，并在FY-3B/VIRR业务SST算法的基础上进行了改进。基于NOAA-19/AVHRR匹配数据集，进行多算法建模分析及精度评估，白天最优算法为非线性SST (NL) 算法，夜间最优算法为三通道SST (TC) 算法，最优算法的确定与NESDIS/STAR一致。建立2012年8月—2013年3月FY-3B/VIRR匹配数据集，并在此基础上进行多算法回归建模及精度评估，白天和夜间的最优均为NL算法，分析发现夜间TC算法采用匹配数据集版本2(MDB_V2) 时，3.7 μm通道存在类似百叶窗的条带现象。以2012年10—12月FY-3B/VIRR匹配数据集计算回归系数，以2013年1—3月独立样本进行精度评估，与浮标SST相比，NL算法白天和夜间的均方根误差分别为0.41℃和0.43℃。与日平均最优插值海温 (OISST) 相比，NL算法白天和夜间的均方根误差分别为1.45℃和1.5℃; 选择与OISST偏差在2℃以内的样本，NL算法白天和夜间均方根误差分别为0.82℃和0.84℃。
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关键词:
- FY-3B气象卫星;
- 可见光红外扫描辐射计 (VIRR);
- 海表温度;
- 算法
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). -
图 1 FY-3B/VIRR 2013年4月30日11:00 10.8 μm通道亮温图像 (a)、云检测产品 (b) 及改进后SST产品 (c)
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
图 2 2012年8月—2013年3月3种白天SST算法的精度曲线
Fig. 2 Statistics time series of three daytime SST algorithms with respect to in situ SST from August 2012 to March 2013
图 3 2012年8月—2013年3月5种夜间SST算法的精度曲线
Fig. 3 Statistics time series of five nighttime SST algorithms with respect to in situ SST from August 2012 to March 2013
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
表 1 FY-3B/VIRR SST算法命名表
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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表 2 TC_N算法回归统计信息
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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表 3 2013年4月FY-3B/VIRR SST算法的误差统计
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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表 4 2013年4月FY-3B/VIRR NL算法误差统计结果
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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