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A Note on Reviving the Goddard Satellite-Based Surface Turbulent Fluxes (GSSTF) Dataset


doi: 10.1007/s00376-009-8138-z

  • Accurate sea surface flux measurements are crucial for understanding the global water and energy cycles. The oceanic evaporation, which is a major component of the global oceanic fresh water flux, is useful for predicting oceanic circulation and transport. The global Goddard Satellite-based Surface Turbulent Fluxes Version-2 (GSSTF2; July 1987--December 2000) dateset that was officially released in 2001 has been widely used by scientific community for global energy and water cycle research, and regional and short period data analyses. We have recently been funded by NASA to resume processing the GSSTF dataset with an objective of continually producing a uniform dataset of sea surface turbulent fluxes, derived from remote sensing data. The dataset is to be reprocessed and brought up-to-date (GSSTF2b) using improved input datasets such as a recently upgraded NCEP/DOE sea surface temperature reanalysis, and an upgraded surface wind and microwave brightness temperature V6 dataset (Version 6) from the Special Sensor Microwave Imager (SSM/I) produced by Remote Sensing Systems (RSS). A second new product (GSSTF3) is further proposed with a finer temporal (12-h) and spatial (0.25ox0.25o) resolution. GSSTF2b (July 1987--December 2008) and GSSTF3 (July 1999--December 2009) will be released for the research community to use by late 2009 and early 2011, respectively.
  • [1] WANG Aihui, ZENG Xubin, 2009: Improving the Treatment of the Vertical Snow Burial Fraction over Short Vegetation in the NCAR CLM3, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 877-886.  doi: 10.1007/s00376-009-8098-3
    [2] Xin HAO, Shengping HE, Tingting HAN, Huijun WANG, 2018: Impact of Global Oceanic Warming on Winter Eurasian Climate, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1254-1264.  doi: 10.1007/s00376-018-7216-5
    [3] Fu Congbin, Xie Li, 1998: Global Oceanic Climate Anomalies in 1980’s, ADVANCES IN ATMOSPHERIC SCIENCES, 15, 167-178.  doi: 10.1007/s00376-998-0037-1
    [4] Nan GE, Lei ZHONG, Yaoming MA, Yunfei FU, Mijun ZOU, Meilin CHENG, Xian WANG, Ziyu HUANG, 2021: Estimations of Land Surface Characteristic Parameters and Turbulent Heat Fluxes over the Tibetan Plateau Based on FY-4A/AGRI Data, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-020-0169-5
    [5] Jiang Shangcheng, Ye Qian, Yang Xifeng, An Gang, Xiangqiang Wu, 2000: Climatological Features of the Global Tropical Subsidence Region Based on Satellite Observations, ADVANCES IN ATMOSPHERIC SCIENCES, 17, 391-402.  doi: 10.1007/s00376-000-0031-8
    [6] Ruiyao CHEN, Ralf BENNARTZ, 2021: Rainfall Algorithms Using Oceanic Satellite Observations from MWHS-2, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1367-1378.  doi: 10.1007/s00376-020-0258-5
    [7] Peng LIU, Chung-Hsiung SUI, 2014: An Observational Analysis of the Oceanic and Atmospheric Structure of Global-Scale Multi-decadal Variability, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 316-330.  doi: 10.1007/s00376-013-2305-y
    [8] Bo HAN, Shihua LÜ, Ruiqing LI, Xin WANG, Lin ZHAO, Cailing ZHAO, Danyun WANG, Xianhong MENG, 2017: Global Land Surface Climate Analysis Based on the Calculation of a Modified Bowen Ratio, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 663-678.  doi: 10.1007/s00376-016-6175-y
    [9] WANG Hesong, JIA Gensuo, 2012: Satellite-Based Monitoring of Decadal Soil Salinization and Climate Effects in a Semi-arid Region of China, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 1089-1099.  doi: 10.1007/s00376-012-1150-8
    [10] HOU Jiangtao, JIA Gensuo, ZHAO Tianbao, WANG Hesong, TANG Bohui, , 2014: Satellite-Based Estimation of Daily Average Net Radiation under Clear-Sky Conditions, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 705-720.  doi: 10.1007/s00376-013-3047-6
    [11] Yi-Xuan SHOU, Feng LU, Hui LIU, Peng CUI, Shaowen SHOU, Jian LIU, 2019: Satellite-based Observational Study of the Tibetan Plateau Vortex: Features of Deep Convective Cloud Tops, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 189-205.  doi: 10.1007/s00376-018-8049-y
    [12] Chunlin HUANG, Hongrong SHI, Ling GAO, Mengqi LIU, Qixiang CHEN, Disong FU, Shu WANG, Yuan YUAN, Xiang′ao XIA, 2022: Fengyun-4 Geostationary Satellite-Based Solar Energy Nowcasting System and Its Application in North China, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1316-1328.  doi: 10.1007/s00376-022-1464-0
    [13] PENG Jie, ZHANG Hua, Zhanqing LI, 2014: Temporal and Spatial Variations of Global Deep Cloud Systems Based on CloudSat and CALIPSO Satellite Observations, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 593-603.  doi: 10.1007/s00376-013-3055-6
    [14] Li Jun, Zhou Fengxian, 1992: On Accurate Detection of Oceanic Features from Satellite IR Data Using ICSED Method, ADVANCES IN ATMOSPHERIC SCIENCES, 9, 373-382.  doi: 10.1007/BF02656948
    [15] Pucai WANG, N. F. ELANSKY, Yu. M. TIMOFEEV, Gengchen WANG, G. S. GOLITSYN, M. V. MAKAROVA, V. S. RAKITIN, Yu. SHTABKIN, A. I. SKOROKHOD, E. I. GRECHKO, E.V. FOKEEVA, A. N. SAFRONOV, Liang RAN, Ting WANG, 2018: Long-Term Trends of Carbon Monoxide Total Columnar Amount in Urban Areas and Background Regions: Ground- and Satellite-based Spectroscopic Measurements, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 785-795.  doi: 10.1007/s00376-017-6327-8
    [16] WANG Shaoying, ZHANG Yu, LU Shihua, LIU Heping, SHANG Lunyu, 2013: Estimation of Turbulent Fluxes Using the Flux-Variance Method over an Alpine Meadow Surface in the Eastern Tibetan Plateau, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 411-424.  doi: 10.1007/s00376-012-2056-1
    [17] Li Xingsheng, Bian Xindi, Zhong Shiyuan, 1985: A NUMERICAL INVESTIGATION ON THE INTERACTION OF TURBULENT AND LONG-WAVE RADIATIVE FLUXES IN THE SURFACE LAYER, ADVANCES IN ATMOSPHERIC SCIENCES, 2, 522-530.  doi: 10.1007/BF02678750
    [18] Ye Zhuojia, Li Jun, Fan Sihong, 1997: Turbulent Fluxes over Inhomogeneous Landscape, ADVANCES IN ATMOSPHERIC SCIENCES, 14, 399-408.  doi: 10.1007/s00376-997-0059-0
    [19] MA Yaoming, Massimo MENENTI, Reinder FEDDES, 2010: Parameterization of Heat Fluxes at Heterogeneous Surfaces by Integrating Satellite Measurements with Surface Layer and Atmospheric Boundary Layer Observations, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 328-336.  doi: 10.1007/s00376-009-9024-4
    [20] Shutao CHEN, Jianwen ZOU, Zhenghua HU, Yanyu LU, 2019: Climate and Vegetation Drivers of Terrestrial Carbon Fluxes: A Global Data Synthesis, ADVANCES IN ATMOSPHERIC SCIENCES, , 679-696.  doi: 10.1007/s00376-019-8194-y

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Manuscript History

Manuscript received: 10 November 2009
Manuscript revised: 10 November 2009
通讯作者: 陈斌, bchen63@163.com
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A Note on Reviving the Goddard Satellite-Based Surface Turbulent Fluxes (GSSTF) Dataset

  • 1. UMBC/GEST, NASA/GSFC,GMU/CEOSR, Fairfax, Virginia, USA, CUHK/ISEIS, Shatin NT, Hong Kong,UMCP/ESSIC, NASA/GSFC,Code 613.1, NASA/GSFC, Greenbelt, Maryland, USA, SSAI, Lanham, Maryland, USA,NTU, Taipei, Taiwan,NOAA/CPC, Camp Springs, Maryland, USA,CWB, Taipei, Chinese Taiwan,GMU/CEOSR, Fairfax, Virginia, USA,Code $613.1,$ NASA/GSFC, Greenbelt, Maryland, USA, UMBC/JCET, Baltimore, Maryland, USA,UMBC/GEST, Baltimore, Maryland, USA, Code $613.2,$ NASA/GSFC, Greenbelt, Maryland, USA

Abstract: Accurate sea surface flux measurements are crucial for understanding the global water and energy cycles. The oceanic evaporation, which is a major component of the global oceanic fresh water flux, is useful for predicting oceanic circulation and transport. The global Goddard Satellite-based Surface Turbulent Fluxes Version-2 (GSSTF2; July 1987--December 2000) dateset that was officially released in 2001 has been widely used by scientific community for global energy and water cycle research, and regional and short period data analyses. We have recently been funded by NASA to resume processing the GSSTF dataset with an objective of continually producing a uniform dataset of sea surface turbulent fluxes, derived from remote sensing data. The dataset is to be reprocessed and brought up-to-date (GSSTF2b) using improved input datasets such as a recently upgraded NCEP/DOE sea surface temperature reanalysis, and an upgraded surface wind and microwave brightness temperature V6 dataset (Version 6) from the Special Sensor Microwave Imager (SSM/I) produced by Remote Sensing Systems (RSS). A second new product (GSSTF3) is further proposed with a finer temporal (12-h) and spatial (0.25ox0.25o) resolution. GSSTF2b (July 1987--December 2008) and GSSTF3 (July 1999--December 2009) will be released for the research community to use by late 2009 and early 2011, respectively.

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