【摘要】 Argo浮标数据由于采样机制不足造成离散且不连续,同时缺乏表层观测,测量数据直接与卫星反演温度产品像元尺度匹配会存在较大误差。对Argo浮标观测数据进行预处理及表层温度推算生成实测的海表温度产品,对有效利用浮标观测数据进行算法与产品精度评价具有重要意义。验证主要是利用MODIS SST产品验证Argo浮标SST产品的精度。验证结果表明,匹配数据的线性关系显著,R~2为0.990,但是在夏季和低高纬地区离散度较差,主要是夏季风速低,日照强引起海表热分层,同时纬度较高或较低对大气水汽含量的影响大,从而影响卫星SST的反演产品精度,导致浮标与MODIS匹配精度降低。更多还原

【Abstract】 Argo float data are discrete and discontinuous due to insufficient sampling mechanism and lack of surface observations, and there are large errors in matching the measured data directly with the image element scale of the satellite inversion SST products.The pre-processing of Argo float observations and surface temperature derivation to generate sea surface temperature products based on Argo are important for the effective use of the float observations for algorithm and product accuracy evaluation.The validation mainly verifies the accuracy of Argo float SST products using MODIS SST products.The validation results show that the linearity of the matched data is significant with R~2 of 0.990,but the dispersion is poor in summer and low and high latitude areas.It is mainly due to the low wind speed and strong insolation in summer that cause thermal stratification at the sea surface.Meanwhile, higher or lower latitude has a great effect on the atmospheric water vapor content, which affects the inversion product accuracy of satellite SST and lowers the float and MODIS matching accuracy.更多还原