A new global gridded sea surface temperature product constructed from infrared and microwave radiometer data using the optimum interpolation method

被引:17
作者
Sun Weifu [1 ]
Wang Jin [2 ]
Zhang Jie [1 ]
Ma Yi [1 ]
Meng Junmin [1 ]
Yang Lei [3 ]
Miao Junwei [4 ]
机构
[1] State Ocean Adm, Inst Oceanog 1, Qingdao 266061, Peoples R China
[2] Qingdao Univ, Coll Phys, Qingdao 266071, Peoples R China
[3] China Univ Petr East China, Sch Geosci, Qingdao 266580, Peoples R China
[4] Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Peoples R China
关键词
sea surface temperature; radiometer; data fusion; optimum interpolation; IN-SITU; SATELLITE; VALIDATION; ATMOSPHERE; OCEAN;
D O I
10.1007/s13131-018-1206-4
中图分类号
P7 [海洋学];
学科分类号
0707 ;
摘要
A new 0.1 degrees gridded daily sea surface temperature (SST) data product is presented covering the years 2003-2015. It is created by fusing satellite SST data retrievals from four microwave (WindSat, AMSR-E, ASMR2 and HY-2A RM) and two infrared (MODIS and AVHRR) radiometers (RMs) based on the optimum interpolation (OI) method. The effect of including HY-2A RM SST data in the fusion product is studied, and the accuracy of the new SST product is determined by various comparisons with moored and drifting buoy measurements. An evaluation using global tropical moored buoy measurements shows that the root mean square error (RMSE) of the new gridded SST product is generally less than 0.5 degrees C. A comparison with US National Data Buoy Center meteorological and oceanographic moored buoy observations shows that the RMSE of the new product is generally less than 0.8 degrees C. A comparison with measurements from drifting buoys shows an RMSE of 0.52-0.69 degrees C. Furthermore, the consistency of the new gridded SST dataset and the Remote Sensing Systems microwave-infrared SST dataset is evaluated, and the result shows that no significant inconsistency exists between these two products.
引用
收藏
页码:41 / 49
页数:9
相关论文
共 33 条
[1]   Validation of GLI and other satellite-derived sea surface temperatures using data from the Rottnest Island ferry, Western Australia [J].
Barton, I ;
Pearce, A .
JOURNAL OF OCEANOGRAPHY, 2006, 62 (03) :303-310
[2]  
Bourles B, 2008, B AM METEOROL SOC, V89, P1111, DOI 10.1175/2008BAMS2462.1
[3]   Blending Sea Surface Temperatures from Multiple Satellites and In Situ Observations for Coastal Oceans [J].
Chao, Yi ;
Li, Zhijin ;
Farrara, John D. ;
Hung, Peter .
JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY, 2009, 26 (07) :1415-1426
[4]   Merging satellite infrared and microwave SSTs: Methodology and evaluation of the new SST [J].
Guan L. ;
Kawamura H. .
Journal of Oceanography, 2004, 60 (5) :905-912
[5]   Algorithm for estimating sea surface temperatures based on Aqua/MODIS global ocean data. 1. Development and validation of the algorithm [J].
Hosoda, Kohtaro ;
Qin, Huiling .
JOURNAL OF OCEANOGRAPHY, 2011, 67 (01) :135-145
[6]   Multi sensor validation and error characteristics of Arctic satellite sea surface temperature observations [J].
Hoyer, Jacob L. ;
Karagali, Ioanna ;
Dybkjaer, Gorm ;
Tonboe, Rasmus .
REMOTE SENSING OF ENVIRONMENT, 2012, 121 :335-346
[7]  
[胡晓悦 Hu Xiaoyue], 2015, [遥感学报, Journal of Remote Sensing], V19, P328
[8]   The HY-2 satellite and its preliminary assessment [J].
Jiang, Xingwei ;
Lin, Mingsen ;
Liu, Jianqiang ;
Zhang, Youguang ;
Xie, Xuetong ;
Peng, Hailong ;
Zhou, Wu .
INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2012, 5 (03) :266-281
[9]   Diurnal sea surface temperature variation and its impact on the atmosphere and ocean: A review [J].
Kawai, Yoshinu ;
Wada, Akiyoshi .
JOURNAL OF OCEANOGRAPHY, 2007, 63 (05) :721-744
[10]   Blending multi-resolution satellite sea surface temperature (SST) products using Bayesian maximum entropy method [J].
Li, Aihua ;
Bo, Yanchen ;
Zhu, Yuxin ;
Guo, Peng ;
Bi, Jian ;
He, Yaqian .
REMOTE SENSING OF ENVIRONMENT, 2013, 135 :52-63