Metocean: Historical collections
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Dataset Title: | "LATEX CTD - d93g159.nc - 28.62N, 95.49W - 1993-11-17" |
Institution: | Texas A&M University, Department of Oceanography (Dataset ID: latex_d93g159) |
Information: | Summary | License | FGDC | ISO 19115 | Metadata | Background | Data Access Form | Files | Make a graph |
To view the map, check View : Map of All Related Data above.
WARNING: This may involve lots of data.
For some datasets, this may be slow.
Consider using this only when you need it and
have selected a small subset of the data.
To view the counts of distinct combinations of the variables listed above,
check View : Distinct Data Counts above and select a value for one of the variables above.
Distinct Data (Metadata) (Refine the data subset and/or download the data)
pressure | depth | temperature | potentialTemperature | conductivity | salinity | sigmat | descentRate | transmission | par | parv | fluorescence | backscattering |
---|---|---|---|---|---|---|---|---|---|---|---|---|
dBar | m | degree_C | degree_C | Siemens per meter | PSU | kg m-3 | meters per second | percent | umol m-2 s-1 | volts | volts | volts |
1.5 | 1.5 | 19.43090057373047 | 19.430599212646484 | 4.278600215911865 | 31.257200241088867 | 22.056100845336914 | 0.035999998450279236 | 2.6059999465942383 | 0.9300000071525574 | 0.25699999928474426 | 1.6770000457763672 | -9.0 |
2.0 | 2.0 | 19.4281005859375 | 19.42770004272461 | 4.277599811553955 | 31.250600814819336 | 22.051799774169922 | 0.26600000262260437 | 2.6059999465942383 | 0.7200000286102295 | 0.14499999582767487 | 1.7280000448226929 | -9.0 |
2.5 | 2.5 | 19.42770004272461 | 19.42729949951172 | 4.277599811553955 | 31.251399993896484 | 22.052499771118164 | 0.42800000309944153 | 2.6089999675750732 | 0.5299999713897705 | 0.009999999776482582 | 1.7039999961853027 | -9.0 |
3.0 | 3.0 | 19.428199768066406 | 19.42770004272461 | 4.2778000831604 | 31.252099990844727 | 22.052900314331055 | 0.6890000104904175 | 2.6019999980926514 | 0.5099999904632568 | 0.0 | 1.690000057220459 | -9.0 |
3.5 | 3.5 | 19.444900512695312 | 19.444299697875977 | 4.278900146484375 | 31.24850082397461 | 22.04599952697754 | 0.777999997138977 | 2.5940001010894775 | 0.5099999904632568 | 0.0 | 1.7089999914169312 | -9.0 |
4.0 | 4.0 | 19.450000762939453 | 19.44930076599121 | 4.279300212860107 | 31.24799919128418 | 22.04439926147461 | 0.6439999938011169 | 2.5889999866485596 | 0.5099999904632568 | 0.0 | 1.7269999980926514 | -9.0 |
4.5 | 4.5 | 19.42799949645996 | 19.427200317382812 | 4.27869987487793 | 31.25909996032715 | 22.058399200439453 | 0.3499999940395355 | 2.5910000801086426 | 0.5099999904632568 | 0.0 | 1.7699999809265137 | -9.0 |
5.0 | 5.0 | 19.421600341796875 | 19.420700073242188 | 4.277699947357178 | 31.255599975585938 | 22.05739974975586 | 0.5149999856948853 | 2.5999999046325684 | 0.5099999904632568 | 0.0 | 1.7960000038146973 | -9.0 |
5.5 | 5.5 | 19.46739959716797 | 19.466400146484375 | 4.287099838256836 | 31.297700881958008 | 22.077899932861328 | 0.703000009059906 | 2.5950000286102295 | 0.5099999904632568 | 0.0 | 1.7740000486373901 | -9.0 |
6.0 | 6.0 | 19.550199508666992 | 19.549100875854492 | 4.305300235748291 | 31.38360023498535 | 22.122400283813477 | 0.7440000176429749 | 2.5910000801086426 | 0.5099999904632568 | 0.0 | 1.753000020980835 | -9.0 |
6.5 | 6.5 | 19.607799530029297 | 19.606599807739258 | 4.323299884796143 | 31.486799240112305 | 22.186399459838867 | 0.6570000052452087 | 2.5920000076293945 | 0.5099999904632568 | 0.0 | 1.781999945640564 | -9.0 |
7.0 | 7.0 | 19.702999114990234 | 19.70170021057129 | 4.354300022125244 | 31.66710090637207 | 22.29949951171875 | 0.5320000052452087 | 2.5880000591278076 | 0.5099999904632568 | 0.0 | 1.7719999551773071 | -9.0 |
7.599999904632568 | 7.5 | 19.899499893188477 | 19.8981990814209 | 4.417200088500977 | 32.02750015258789 | 22.523500442504883 | 0.48899999260902405 | 2.562000036239624 | 0.5099999904632568 | 0.0 | 1.7719999551773071 | -9.0 |
8.100000381469727 | 8.0 | 19.934999465942383 | 19.933500289916992 | 4.426799774169922 | 32.07820129394531 | 22.55299949645996 | 0.4339999854564667 | 2.5179998874664307 | 0.5099999904632568 | 0.0 | 1.7640000581741333 | -9.0 |
8.600000381469727 | 8.5 | 20.14430046081543 | 20.1427001953125 | 4.492599964141846 | 32.45050048828125 | 22.782400131225586 | 0.4650000035762787 | 2.4539999961853027 | 0.5099999904632568 | 0.0 | 1.7330000400543213 | -9.0 |
9.100000381469727 | 9.0 | 20.142499923706055 | 20.14080047607422 | 4.491799831390381 | 32.44499969482422 | 22.77869987487793 | 0.656000018119812 | 2.424999952316284 | 0.5099999904632568 | 0.0 | 1.7230000495910645 | -9.0 |
9.600000381469727 | 9.5 | 20.13949966430664 | 20.137699127197266 | 4.490600109100342 | 32.43769836425781 | 22.77389907836914 | 0.7760000228881836 | 2.364000082015991 | 0.5099999904632568 | 0.0 | 1.715000033378601 | -9.0 |
10.100000381469727 | 10.0 | 20.139699935913086 | 20.137800216674805 | 4.490600109100342 | 32.4375 | 22.773700714111328 | 0.7319999933242798 | 2.3529999256134033 | 0.5099999904632568 | 0.0 | 1.7239999771118164 | -9.0 |
10.600000381469727 | 10.5 | 20.13990020751953 | 20.13800048828125 | 4.490699768066406 | 32.4375 | 22.773700714111328 | 0.48100000619888306 | 2.3459999561309814 | 0.5099999904632568 | 0.0 | 1.7230000495910645 | -9.0 |
11.100000381469727 | 11.0 | 20.142200469970703 | 20.140199661254883 | 4.491499900817871 | 32.44260025024414 | 22.777000427246094 | 0.3569999933242798 | 2.312000036239624 | 0.5099999904632568 | 0.0 | 1.7200000286102295 | -9.0 |
11.600000381469727 | 11.5 | 20.1481990814209 | 20.146099090576172 | 4.493800163269043 | 32.45600128173828 | 22.785600662231445 | 0.5789999961853027 | 2.305999994277954 | 0.5099999904632568 | 0.0 | 1.7120000123977661 | -9.0 |
12.100000381469727 | 12.0 | 20.154199600219727 | 20.152000427246094 | 4.496500015258789 | 32.47309875488281 | 22.797199249267578 | 0.7139999866485596 | 2.296999931335449 | 0.5099999904632568 | 0.0 | 1.7109999656677246 | -9.0 |
12.600000381469727 | 12.5 | 20.160499572753906 | 20.158199310302734 | 4.4994001388549805 | 32.49169921875 | 22.80970001220703 | 0.7450000047683716 | 2.2730000019073486 | 0.5099999904632568 | 0.0 | 1.7200000286102295 | -9.0 |
13.100000381469727 | 13.0 | 20.1643009185791 | 20.16189956665039 | 4.501299858093262 | 32.50389862060547 | 22.81800079345703 | 0.6140000224113464 | 2.253000020980835 | 0.5099999904632568 | 0.0 | 1.718000054359436 | -9.0 |
13.600000381469727 | 13.5 | 20.180099487304688 | 20.177600860595703 | 4.506700038909912 | 32.53559875488281 | 22.83799934387207 | 0.35199999809265137 | 2.234999895095825 | 0.5099999904632568 | 0.0 | 1.722000002861023 | -9.0 |
14.100000381469727 | 14.0 | 20.2091007232666 | 20.206499099731445 | 4.513299942016602 | 32.56610107421875 | 22.853700637817383 | 0.527999997138977 | 2.1989998817443848 | 0.5099999904632568 | 0.0 | 1.7400000095367432 | -9.0 |
14.600000381469727 | 14.5 | 20.23110008239746 | 20.2283992767334 | 4.520199775695801 | 32.60490036010742 | 22.877500534057617 | 0.6850000023841858 | 2.0889999866485596 | 0.5099999904632568 | 0.0 | 1.753000020980835 | -9.0 |
15.100000381469727 | 15.0 | 20.24410057067871 | 20.24139976501465 | 4.524199962615967 | 32.62620162963867 | 22.890300750732422 | 0.6430000066757202 | 2.0190000534057617 | 0.5099999904632568 | 0.0 | 1.7400000095367432 | -9.0 |
15.600000381469727 | 15.5 | 20.262100219726562 | 20.259199142456055 | 4.528299808502197 | 32.64580154418945 | 22.90060043334961 | 0.36500000953674316 | 1.8890000581741333 | 0.5099999904632568 | 0.0 | 1.718000054359436 | -9.0 |
In total, there are 29 rows of distinct combinations of the variables listed above.
All of the rows are shown above.
To change the maximum number of rows displayed, change View : Distinct Data above.
To view the related data counts,
check View : Related Data Counts above and select a value for one of the variables above.
WARNING: This may involve lots of data.
For some datasets, this may be slow.
Consider using this only when you need it and
have selected a small subset of the data.
Related Data (Metadata) (Refine the data subset and/or download the data)
To view the related data, change View : Related Data above.
WARNING: This may involve lots of data. For some datasets, this may be slow. Consider using this only when you need it and have selected a small subset of the data.