Metocean: Historical collections
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Dataset Title: | "LATEX CTD - d94j063.nc - 28.86N, 93.0W - 1994-11-06" |
Institution: | Texas A&M University, Department of Oceanography (Dataset ID: latex_d94j063) |
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 |
2.5 | 2.5 | 24.895999908447266 | 24.89550018310547 | 5.098899841308594 | 33.541099548339844 | 22.27239990234375 | 0.23600000143051147 | 4.2179999351501465 | 791.0 | 3.0369999408721924 | 1.3639999628067017 | 0.0430000014603138 |
3.0 | 3.0 | 24.89739990234375 | 24.896799087524414 | 5.0991997718811035 | 33.54240036010742 | 22.273000717163086 | 0.7789999842643738 | 4.2129998207092285 | 593.0 | 2.9119999408721924 | 1.3849999904632568 | 0.041999999433755875 |
3.5 | 3.5 | 24.897199630737305 | 24.896400451660156 | 5.0991997718811035 | 33.542701721191406 | 22.273300170898438 | 0.8410000205039978 | 4.218999862670898 | 596.0 | 2.9140000343322754 | 1.3869999647140503 | 0.0430000014603138 |
4.0 | 4.0 | 24.8966007232666 | 24.895700454711914 | 5.0991997718811035 | 33.542598724365234 | 22.273500442504883 | 0.6930000185966492 | 4.2179999351501465 | 490.0 | 2.8289999961853027 | 1.3919999599456787 | 0.04100000113248825 |
4.5 | 4.5 | 24.896499633789062 | 24.895599365234375 | 5.0991997718811035 | 33.54290008544922 | 22.273799896240234 | 0.5189999938011169 | 4.215000152587891 | 480.0 | 2.819999933242798 | 1.4270000457763672 | 0.04100000113248825 |
5.0 | 5.0 | 24.896499633789062 | 24.89539909362793 | 5.099299907684326 | 33.54309844970703 | 22.27400016784668 | 0.8220000267028809 | 4.215000152587891 | 432.0 | 2.7750000953674316 | 1.434000015258789 | 0.041999999433755875 |
5.5 | 5.5 | 24.89620018005371 | 24.895000457763672 | 5.099299907684326 | 33.54309844970703 | 22.274099349975586 | 0.9070000052452087 | 4.215000152587891 | 366.0 | 2.703000068664551 | 1.4259999990463257 | 0.0430000014603138 |
6.0 | 6.0 | 24.895999908447266 | 24.894699096679688 | 5.099299907684326 | 33.54290008544922 | 22.274099349975586 | 0.8420000076293945 | 4.210999965667725 | 301.0 | 2.617000102996826 | 1.4240000247955322 | 0.04500000178813934 |
6.5 | 6.5 | 24.89620018005371 | 24.894800186157227 | 5.099299907684326 | 33.54309844970703 | 22.274200439453125 | 0.5699999928474426 | 4.215000152587891 | 313.0 | 2.634000062942505 | 1.4179999828338623 | 0.0430000014603138 |
7.0 | 7.0 | 24.896099090576172 | 24.89459991455078 | 5.099400043487549 | 33.54330062866211 | 22.27440071105957 | 0.8759999871253967 | 4.2170000076293945 | 347.0 | 2.680000066757202 | 1.4119999408721924 | 0.04100000113248825 |
7.599999904632568 | 7.5 | 24.896099090576172 | 24.894399642944336 | 5.099299907684326 | 33.542999267578125 | 22.274200439453125 | 0.8989999890327454 | 4.216000080108643 | 245.0 | 2.5290000438690186 | 1.4170000553131104 | 0.041999999433755875 |
8.100000381469727 | 8.0 | 24.895000457763672 | 24.893199920654297 | 5.0991997718811035 | 33.542999267578125 | 22.274599075317383 | 0.7599999904632568 | 4.2170000076293945 | 224.0 | 2.489000082015991 | 1.4259999990463257 | 0.04600000008940697 |
8.600000381469727 | 8.5 | 24.895700454711914 | 24.89389991760254 | 5.099299907684326 | 33.54290008544922 | 22.27429962158203 | 0.5590000152587891 | 4.215000152587891 | 201.0 | 2.443000078201294 | 1.434999942779541 | 0.04100000113248825 |
9.100000381469727 | 9.0 | 24.895999908447266 | 24.893999099731445 | 5.099400043487549 | 33.54330062866211 | 22.274499893188477 | 0.8740000128746033 | 4.2170000076293945 | 171.0 | 2.371000051498413 | 1.4359999895095825 | 0.04100000113248825 |
9.600000381469727 | 9.5 | 24.8976993560791 | 24.895599365234375 | 5.099599838256836 | 33.54290008544922 | 22.273799896240234 | 0.9470000267028809 | 4.2170000076293945 | 153.0 | 2.325000047683716 | 1.437000036239624 | 0.041999999433755875 |
10.100000381469727 | 10.0 | 24.897499084472656 | 24.89539909362793 | 5.099599838256836 | 33.54290008544922 | 22.27389907836914 | 0.8349999785423279 | 4.209000110626221 | 159.0 | 2.3399999141693115 | 1.437999963760376 | 0.041999999433755875 |
10.600000381469727 | 10.5 | 24.89739990234375 | 24.895200729370117 | 5.099599838256836 | 33.54290008544922 | 22.27389907836914 | 0.546999990940094 | 4.210999965667725 | 159.0 | 2.3399999141693115 | 1.437999963760376 | 0.04100000113248825 |
11.100000381469727 | 11.0 | 24.897300720214844 | 24.894899368286133 | 5.0995001792907715 | 33.54249954223633 | 22.273700714111328 | 0.6010000109672546 | 4.2170000076293945 | 146.0 | 2.302000045776367 | 1.440999984741211 | 0.04100000113248825 |
11.600000381469727 | 11.5 | 24.897199630737305 | 24.894699096679688 | 5.0995001792907715 | 33.54240036010742 | 22.273700714111328 | 0.3880000114440918 | 4.215000152587891 | 125.0 | 2.236999988555908 | 1.4570000171661377 | 0.0430000014603138 |
12.100000381469727 | 12.0 | 24.898300170898438 | 24.895700454711914 | 5.100100040435791 | 33.5458984375 | 22.275999069213867 | 0.39399999380111694 | 4.215000152587891 | 126.0 | 2.239000082015991 | 1.4470000267028809 | 0.04100000113248825 |
12.600000381469727 | 12.5 | 24.899099349975586 | 24.896400451660156 | 5.100599765777588 | 33.54840087890625 | 22.277700424194336 | 0.5090000033378601 | 4.216000080108643 | 120.0 | 2.2179999351501465 | 1.4459999799728394 | 0.041999999433755875 |
13.100000381469727 | 13.0 | 24.901599884033203 | 24.898799896240234 | 5.1016998291015625 | 33.5546989440918 | 22.281700134277344 | 0.36500000953674316 | 4.201000213623047 | 109.0 | 2.1760001182556152 | 1.4579999446868896 | 0.04600000008940697 |
13.600000381469727 | 13.5 | 24.90719985961914 | 24.904300689697266 | 5.104100227355957 | 33.56809997558594 | 22.290199279785156 | 0.32199999690055847 | 4.198999881744385 | 106.0 | 2.1649999618530273 | 1.472000002861023 | 0.04399999976158142 |
14.100000381469727 | 14.0 | 24.910600662231445 | 24.90760040283203 | 5.105400085449219 | 33.57500076293945 | 22.29439926147461 | 0.36800000071525574 | 4.195000171661377 | 96.80000305175781 | 2.125 | 1.4880000352859497 | 0.04600000008940697 |
14.600000381469727 | 14.5 | 24.913000106811523 | 24.909900665283203 | 5.106400012969971 | 33.580299377441406 | 22.297700881958008 | 0.4180000126361847 | 4.188000202178955 | 84.69999694824219 | 2.066999912261963 | 1.4809999465942383 | 0.04699999839067459 |
15.100000381469727 | 15.0 | 24.929000854492188 | 24.92569923400879 | 5.112599849700928 | 33.614200592041016 | 22.318500518798828 | 0.41999998688697815 | 4.171999931335449 | 81.80000305175781 | 2.052000045776367 | 1.4989999532699585 | 0.04699999839067459 |
15.600000381469727 | 15.5 | 24.92770004272461 | 24.924400329589844 | 5.111499786376953 | 33.606998443603516 | 22.313499450683594 | 0.27399998903274536 | 4.140999794006348 | 82.80000305175781 | 2.056999921798706 | 1.5169999599456787 | 0.04800000041723251 |
16.100000381469727 | 16.0 | 24.93939971923828 | 24.93600082397461 | 5.116300106048584 | 33.63359832763672 | 22.33009910583496 | 0.3059999942779541 | 4.124000072479248 | 72.80000305175781 | 2.000999927520752 | 1.5670000314712524 | 0.052000001072883606 |
16.600000381469727 | 16.5 | 24.946300506591797 | 24.942699432373047 | 5.118899822235107 | 33.64720153808594 | 22.338300704956055 | 0.3880000114440918 | 4.111000061035156 | 68.69999694824219 | 1.9759999513626099 | 1.5859999656677246 | 0.061000000685453415 |
17.100000381469727 | 17.0 | 24.966100692749023 | 24.962400436401367 | 5.125699996948242 | 33.682899475097656 | 22.359399795532227 | 0.4659999907016754 | 4.0929999351501465 | 63.20000076293945 | 1.940000057220459 | 1.6009999513626099 | 0.057999998331069946 |
17.600000381469727 | 17.5 | 24.981800079345703 | 24.97800064086914 | 5.131199836730957 | 33.71160125732422 | 22.376399993896484 | 0.5320000052452087 | 4.041999816894531 | 60.0 | 1.9170000553131104 | 1.6109999418258667 | 0.06700000166893005 |
18.100000381469727 | 18.0 | 24.987699508666992 | 24.983800888061523 | 5.1331000328063965 | 33.72090148925781 | 22.38159942626953 | 0.4059999883174896 | 4.000999927520752 | 55.5 | 1.8830000162124634 | 1.6410000324249268 | 0.07100000232458115 |
18.600000381469727 | 18.5 | 25.012500762939453 | 25.008399963378906 | 5.142199993133545 | 33.76890182495117 | 22.410400390625 | 0.5989999771118164 | 3.9590001106262207 | 51.400001525878906 | 1.850000023841858 | 1.684000015258789 | 0.08100000023841858 |
19.100000381469727 | 19.0 | 25.047199249267578 | 25.042999267578125 | 5.155300140380859 | 33.839500427246094 | 22.45330047607422 | 0.5989999771118164 | 3.884000062942505 | 47.20000076293945 | 1.812999963760376 | 1.6929999589920044 | 0.08799999952316284 |
19.600000381469727 | 19.5 | 25.04129981994629 | 25.037099838256836 | 5.155300140380859 | 33.84379959106445 | 22.45829963684082 | 0.33799999952316284 | 3.6419999599456787 | 43.79999923706055 | 1.781000018119812 | 1.7380000352859497 | 0.10100000351667404 |
20.100000381469727 | 20.0 | 25.102399826049805 | 25.097999572753906 | 5.183499813079834 | 34.00590133666992 | 22.562299728393555 | 0.382999986410141 | 3.486999988555908 | 42.5 | 1.7669999599456787 | 1.8250000476837158 | 0.1459999978542328 |
20.600000381469727 | 20.5 | 25.103599548339844 | 25.09910011291504 | 5.183800220489502 | 34.00699996948242 | 22.562700271606445 | 0.4320000112056732 | 3.4679999351501465 | 38.20000076293945 | 1.7209999561309814 | 1.8279999494552612 | 0.18199999630451202 |
21.100000381469727 | 21.0 | 25.10219955444336 | 25.09760093688965 | 5.188000202178955 | 34.039100646972656 | 22.587499618530273 | 0.40400001406669617 | 3.2920000553131104 | 35.400001525878906 | 1.687999963760376 | 1.8489999771118164 | 0.2029999941587448 |
21.600000381469727 | 21.5 | 25.089000701904297 | 25.084299087524414 | 5.18209981918335 | 34.005001068115234 | 22.56570053100586 | 0.44999998807907104 | 3.190000057220459 | 32.099998474121094 | 1.6449999809265137 | 1.8619999885559082 | 0.2370000034570694 |
22.100000381469727 | 22.0 | 25.118799209594727 | 25.11400032043457 | 5.200900077819824 | 34.12139892578125 | 22.64459991455078 | 0.35899999737739563 | 3.121999979019165 | 28.600000381469727 | 1.5950000286102295 | 1.8830000162124634 | 0.24799999594688416 |
22.700000762939453 | 22.5 | 25.12380027770996 | 25.118799209594727 | 5.201200008392334 | 34.11949920654297 | 22.641700744628906 | 0.29499998688697815 | 3.1019999980926514 | 26.700000762939453 | 1.565999984741211 | 1.8880000114440918 | 0.2770000100135803 |
23.200000762939453 | 23.0 | 25.15329933166504 | 25.148300170898438 | 5.207799911499023 | 34.14590072631836 | 22.652700424194336 | 0.30799999833106995 | 3.1689999103546143 | 23.200000762939453 | 1.5049999952316284 | 1.871999979019165 | 0.2809999883174896 |
23.700000762939453 | 23.5 | 25.138900756835938 | 25.133800506591797 | 5.201600074768066 | 34.11050033569336 | 22.630399703979492 | 0.24899999797344208 | 3.2179999351501465 | 20.600000381469727 | 1.4529999494552612 | 1.8619999885559082 | 0.27799999713897705 |
In total, there are 43 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.