Metocean: Historical collections
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Dataset Title: | "LATEX CTD - d93e108.nc - 29.17N, 94.0W - 1993-05-02" |
Institution: | Texas A&M University, Department of Oceanography (Dataset ID: latex_d93e108) |
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 | 21.33060073852539 | 21.330299377441406 | 4.500199794769287 | 31.620399475097656 | 21.83609962463379 | -0.061000000685453415 | 3.6010000705718994 | 353.0 | 3.578000068664551 | 2.049999952316284 | -9.0 |
2.0 | 2.0 | 21.334400177001953 | 21.333999633789062 | 4.500199794769287 | 31.618200302124023 | 21.83329963684082 | 0.335999995470047 | 3.5969998836517334 | 275.0 | 3.4700000286102295 | 2.0799999237060547 | -9.0 |
2.5 | 2.5 | 21.31559944152832 | 21.315099716186523 | 4.498499870300293 | 31.617900848388672 | 21.83810043334961 | 0.49799999594688416 | 3.5880000591278076 | 243.0 | 3.4159998893737793 | 2.1110000610351562 | -9.0 |
3.0 | 3.0 | 21.29680061340332 | 21.296199798583984 | 4.496699810028076 | 31.617700576782227 | 21.843000411987305 | 0.4950000047683716 | 3.5869998931884766 | 197.0 | 3.3239998817443848 | 2.140000104904175 | -9.0 |
3.5 | 3.5 | 21.310800552368164 | 21.310100555419922 | 4.497000217437744 | 31.60930061340332 | 21.83300018310547 | 0.27799999713897705 | 3.5940001010894775 | 168.0 | 3.25600004196167 | 2.131999969482422 | -9.0 |
4.0 | 4.0 | 21.30620002746582 | 21.305400848388672 | 4.500699996948242 | 31.642000198364258 | 21.858800888061523 | 0.4970000088214874 | 3.6019999980926514 | 150.0 | 3.2060000896453857 | 2.135999917984009 | -9.0 |
4.5 | 4.5 | 21.273799896240234 | 21.272899627685547 | 4.510700225830078 | 31.744400024414062 | 21.94540023803711 | 0.609000027179718 | 3.5989999771118164 | 123.0 | 3.121000051498413 | 2.132999897003174 | -9.0 |
5.0 | 5.0 | 21.268999099731445 | 21.26799964904785 | 4.516600131988525 | 31.794200897216797 | 21.984600067138672 | 0.43299999833106995 | 3.6050000190734863 | 104.0 | 3.0490000247955322 | 2.114000082015991 | -9.0 |
5.5 | 5.5 | 21.26420021057129 | 21.263200759887695 | 4.522500038146973 | 31.844100952148438 | 22.023799896240234 | 0.2669999897480011 | 3.615999937057495 | 91.4000015258789 | 2.990999937057495 | 2.122999906539917 | -9.0 |
6.0 | 6.0 | 21.2593994140625 | 21.25830078125 | 4.52839994430542 | 31.89389991760254 | 22.06290054321289 | 0.5360000133514404 | 3.6080000400543213 | 77.5999984741211 | 2.9200000762939453 | 2.0969998836517334 | -9.0 |
6.5 | 6.5 | 21.22879981994629 | 21.22760009765625 | 4.537899971008301 | 31.99139976501465 | 22.145200729370117 | 0.5019999742507935 | 3.624000072479248 | 65.30000305175781 | 2.8450000286102295 | 2.071000099182129 | -9.0 |
7.0 | 7.0 | 21.195100784301758 | 21.19379997253418 | 4.552999973297119 | 32.135799407958984 | 22.26409912109375 | 0.33399999141693115 | 3.6059999465942383 | 57.900001525878906 | 2.7929999828338623 | 2.056999921798706 | -9.0 |
7.599999904632568 | 7.5 | 21.168699264526367 | 21.167200088500977 | 4.562699794769287 | 32.23210144042969 | 22.34429931640625 | 0.4050000011920929 | 3.5460000038146973 | 50.5 | 2.7330000400543213 | 2.068000078201294 | -9.0 |
8.100000381469727 | 8.0 | 21.142200469970703 | 21.14069938659668 | 4.572400093078613 | 32.32849884033203 | 22.424699783325195 | 0.5059999823570251 | 3.502000093460083 | 42.900001525878906 | 2.6630001068115234 | 2.0769999027252197 | -9.0 |
8.600000381469727 | 8.5 | 21.137100219726562 | 21.135499954223633 | 4.573200225830078 | 32.33789825439453 | 22.433300018310547 | 0.42500001192092896 | 3.367000102996826 | 36.70000076293945 | 2.5950000286102295 | 2.075000047683716 | -9.0 |
9.100000381469727 | 9.0 | 21.136600494384766 | 21.134899139404297 | 4.57390022277832 | 32.34379959106445 | 22.43790054321289 | 0.3149999976158142 | 3.2850000858306885 | 32.29999923706055 | 2.5390000343322754 | 2.0799999237060547 | -9.0 |
9.600000381469727 | 9.5 | 21.138999938964844 | 21.13719940185547 | 4.5802001953125 | 32.3921012878418 | 22.473899841308594 | 0.4180000126361847 | 3.127000093460083 | 27.299999237060547 | 2.4660000801086426 | 2.1010000705718994 | -9.0 |
10.100000381469727 | 10.0 | 21.14080047607422 | 21.138900756835938 | 4.588900089263916 | 32.459598541259766 | 22.524700164794922 | 0.5180000066757202 | 3.0989999771118164 | 23.399999618530273 | 2.4000000953674316 | 2.0820000171661377 | -9.0 |
10.600000381469727 | 10.5 | 21.136699676513672 | 21.134700775146484 | 4.602399826049805 | 32.56949996948242 | 22.609399795532227 | 0.4580000042915344 | 3.0950000286102295 | 20.5 | 2.3410000801086426 | 2.0350000858306885 | -9.0 |
11.100000381469727 | 11.0 | 21.13129997253418 | 21.129199981689453 | 4.617199897766113 | 32.690101623535156 | 22.702499389648438 | 0.3490000069141388 | 3.0450000762939453 | 17.200000762939453 | 2.2660000324249268 | 2.015000104904175 | -9.0 |
11.600000381469727 | 11.5 | 21.126300811767578 | 21.124099731445312 | 4.632999897003174 | 32.81930160522461 | 22.802099227905273 | 0.38100001215934753 | 2.9070000648498535 | 13.800000190734863 | 2.1689999103546143 | 2.007999897003174 | -9.0 |
12.100000381469727 | 12.0 | 21.123300552368164 | 21.121000289916992 | 4.642600059509277 | 32.89759826660156 | 22.862300872802734 | 0.38600000739097595 | 2.7829999923706055 | 11.5 | 2.0889999866485596 | 1.9809999465942383 | -9.0 |
12.600000381469727 | 12.5 | 21.122800827026367 | 21.120399475097656 | 4.644199848175049 | 32.91080093383789 | 22.872600555419922 | 0.46399998664855957 | 2.755000114440918 | 9.770000457763672 | 2.0199999809265137 | 1.9429999589920044 | -9.0 |
13.100000381469727 | 13.0 | 21.122800827026367 | 21.12030029296875 | 4.644700050354004 | 32.91389846801758 | 22.874900817871094 | 0.5009999871253967 | 2.7209999561309814 | 8.09000015258789 | 1.937999963760376 | 1.937999963760376 | -9.0 |
13.600000381469727 | 13.5 | 21.122699737548828 | 21.120100021362305 | 4.645199775695801 | 32.918399810791016 | 22.878400802612305 | 0.41499999165534973 | 2.739000082015991 | 6.5 | 1.843000054359436 | 1.937999963760376 | -9.0 |
14.100000381469727 | 14.0 | 21.122400283813477 | 21.119699478149414 | 4.646299839019775 | 32.92720031738281 | 22.885099411010742 | 0.3619999885559082 | 2.755000114440918 | 5.150000095367432 | 1.7419999837875366 | 1.9270000457763672 | -9.0 |
14.600000381469727 | 14.5 | 21.12220001220703 | 21.119400024414062 | 4.647500038146973 | 32.936500549316406 | 22.89229965209961 | 0.3959999978542328 | 2.7079999446868896 | 4.110000133514404 | 1.6440000534057617 | 1.9110000133514404 | -9.0 |
15.100000381469727 | 15.0 | 21.121299743652344 | 21.11840057373047 | 4.649700164794922 | 32.9541015625 | 22.905899047851562 | 0.21699999272823334 | 2.6700000762939453 | 3.2100000381469727 | 1.5369999408721924 | 1.9049999713897705 | -9.0 |
In total, there are 28 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.