five

Monthly Mean Global Surface Ocean Variables, 2003 (U.S. JGOFS Synthesis & Modeling Phase project results)

收藏
DataONE2016-08-20 更新2024-06-26 收录
下载链接:
https://search.dataone.org/view/sha256:4d6bafa2d2a9a532d139caed35d77d25c9511b4596766af739a48555a9d1b6c4
下载链接
链接失效反馈
官方服务:
资源简介:
<h3>JPL Atlas of Monthly Mean Global Surface Ocean Variables (1987-1999 satellite data)</h3> <h4>Description copied from: <a href=\"http://usjgofs.whoi.edu/mzweb/smpdatadocs/halpern.html\">SMP site</a><br /> Data access: <a href=\"http://usjgofs.whoi.edu/las/servlets/dataset?dset=Climatology/JPL+Atlas+of+Monthly+Mean+Global+Surface+Ocean+Variables+%281987-1999+satellite+data%29\">via SMP Live Access Server</a></h4> <p>These climatologies are part of the Product Number 001 of the Multi-parameter data products made available from the NASA Jet Propulsion Laboratory Physical Oceanography Distributed Active Archive Center (PODAAC)</p> <p>The Monthly Mean Global Surface Ocean Variables data set consists of monthly mean averages of global sea surface temperature, sea surface height, significant wave height, chlorophyll-a concentration, surface wind speed, surface wind velocity and near-surface current. These data sets are associated with a series of printed atlases by Halpern et al. (1991, 1992a, 1992b, 1993a, 1993b, 1994, 1995, 1998, 1999, 2000). Please note that the years over which the monthly averages were calculated and the distance between measurements differ for each data set.</p> <p>see the full description at the SMP site: <a href=\"http://usjgofs.whoi.edu/mzweb/smpdatadocs/halpern.html\">SMP site</a></p> <p><strong>Investigators</strong><br /> Dr. David Halpern, Principal Investigator, JPL<br /> Dr. O. Brown, U of Miami<br /> Dr. D. Dixon, Colorado College<br /> Mr. W. Knauss, JPL<br /> Dr. M. Freilich, Oregon State University<br /> Dr. L. Fu, JPL<br /> Ms. J. Newman, JPL<br /> Mr. G. Pihos, JPL<br /> Dr. F. Wentz, Remote Sensing Systems<br /> Dr. V. Zlotnicki, JPL<br /> Dr. G. Feldman, NASA GSFC</p>
创建时间:
2021-12-05
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作