CAJ | 학술논문

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，3．7μm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℃。
해문개소료위성관측해표온도（SST）산법적발전역정，급출료소용 SST 산법적회귀모형，병재 FY-3B／VIRR업무SST산법적기출상진행료개진。기우NOAA-19／AVHRR필배수거집，진행다산법건모분석급정도평고，백천최우산법위비선성SST（NL）산법，야간최우산법위삼통도SST（TC）산법，최우산법적학정여NESDIS／STAR일치。건립2012년8월—2013년3월FY-3B／VIRR필배수거집，병재차기출상진행다산법회귀건모급정도평고，백천화야간적최우균위NL산법，분석발현야간TC산법채용필배수거집판본2（MDB V2）시，3．7μm통도존재유사백협창적조대현상。이2012년10—12월FY-3B／VIRR필배수거집계산회귀계수，이2013년1—3월독립양본진행정도평고，여부표SST상비，NL산법백천화야간적균방근오차분별위0．41℃화0．43℃。여일평균최우삽치해온（OISST）상비，NL산법백천화야간적균방근오차분별위1．45℃화1．5℃；선택여OISST편차재2℃이내적양본， NL산법백천화야간균방근오차분별위0．82℃화0．84℃。
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 mete-orological 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 re-gression.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 se-lection more reasonable.On regression algorithm,based on least-square regression used for the early oper-ational 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-win-dow 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 day-time 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 .7μm band is smaller than split-window bands,and the calibration of 3 .7μm 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).