Metocean: Historical collections
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Dataset Title: | "LATEX CTD - d94i067.nc - 29.27N, 93.0W - 1994-08-01" |
Institution: | Texas A&M University, Department of Oceanography (Dataset ID: latex_d94i067) |
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 |
2.0 | 2.0 | 28.89940071105957 | 28.89900016784668 | 5.344200134277344 | 32.43870162963867 | 20.1781005859375 | -0.054999999701976776 | 4.414000034332275 | 219.0 | 2.4790000915527344 | 1.465000033378601 | 0.026000000536441803 |
2.5 | 2.5 | 28.898799896240234 | 28.898300170898438 | 5.344200134277344 | 32.43939971923828 | 20.178800582885742 | 0.16200000047683716 | 4.414999961853027 | 166.0 | 2.359999895095825 | 1.4630000591278076 | 0.02500000037252903 |
3.0 | 3.0 | 28.898300170898438 | 28.897499084472656 | 5.344299793243408 | 32.439998626708984 | 20.179500579833984 | 0.3779999911785126 | 4.415999889373779 | 126.0 | 2.240999937057495 | 1.4609999656677246 | 0.02500000037252903 |
3.5 | 3.5 | 28.901899337768555 | 28.900999069213867 | 5.344699859619141 | 32.440101623535156 | 20.178499221801758 | 0.04600000008940697 | 4.415999889373779 | 95.30000305175781 | 2.118000030517578 | 1.468000054359436 | 0.027000000700354576 |
4.0 | 4.0 | 28.905000686645508 | 28.90410041809082 | 5.345399856567383 | 32.44240188598633 | 20.17919921875 | 0.0560000017285347 | 4.415999889373779 | 90.0999984741211 | 2.0940001010894775 | 1.4550000429153442 | 0.02500000037252903 |
4.5 | 4.5 | 28.903600692749023 | 28.90250015258789 | 5.345099925994873 | 32.44160079956055 | 20.179100036621094 | 0.0560000017285347 | 4.415999889373779 | 83.19999694824219 | 2.059000015258789 | 1.4589999914169312 | 0.02500000037252903 |
5.0 | 5.0 | 28.901399612426758 | 28.90019989013672 | 5.344900131225586 | 32.44110107421875 | 20.179500579833984 | -0.17599999904632568 | 4.415999889373779 | 75.30000305175781 | 2.0160000324249268 | 1.4700000286102295 | 0.024000000208616257 |
5.5 | 5.5 | 28.905799865722656 | 28.904499053955078 | 5.3454999923706055 | 32.44200134277344 | 20.178699493408203 | 0.04899999871850014 | 4.418000221252441 | 67.80000305175781 | 1.9700000286102295 | 1.4819999933242798 | 0.02500000037252903 |
6.0 | 6.0 | 28.912599563598633 | 28.91119956970215 | 5.346399784088135 | 32.44390106201172 | 20.17799949645996 | 0.17499999701976776 | 4.415999889373779 | 62.5 | 1.934999942779541 | 1.4809999465942383 | 0.027000000700354576 |
6.5 | 6.5 | 28.92300033569336 | 28.92140007019043 | 5.348499774932861 | 32.450599670410156 | 20.17959976196289 | -0.41499999165534973 | 4.418000221252441 | 51.79999923706055 | 1.8530000448226929 | 1.49399995803833 | 0.039000000804662704 |
7.0 | 7.0 | 28.92840003967285 | 28.926700592041016 | 5.349599838256836 | 32.45429992675781 | 20.180599212646484 | -0.17499999701976776 | 4.396999835968018 | 50.0 | 1.8380000591278076 | 1.4850000143051147 | 0.027000000700354576 |
7.599999904632568 | 7.5 | 28.941699981689453 | 28.939899444580078 | 5.352099895477295 | 32.46260070800781 | 20.1825008392334 | 1.5329999923706055 | 4.414999961853027 | 45.099998474121094 | 1.7929999828338623 | 1.4589999914169312 | 0.035999998450279236 |
8.100000381469727 | 8.0 | 28.947500228881836 | 28.945600509643555 | 5.353300094604492 | 32.46670150756836 | 20.1835994720459 | 0.03799999877810478 | 4.413000106811523 | 41.400001525878906 | 1.75600004196167 | 1.4579999446868896 | 0.02500000037252903 |
8.600000381469727 | 8.5 | 28.95009994506836 | 28.94809913635254 | 5.353799819946289 | 32.468101501464844 | 20.183900833129883 | 1.1720000505447388 | 4.4079999923706055 | 37.599998474121094 | 1.7139999866485596 | 1.4609999656677246 | 0.024000000208616257 |
9.100000381469727 | 9.0 | 28.951099395751953 | 28.94890022277832 | 5.353899955749512 | 32.467899322509766 | 20.183500289916992 | -0.09300000220537186 | 4.414000034332275 | 33.900001525878906 | 1.6690000295639038 | 1.468000054359436 | 0.024000000208616257 |
9.600000381469727 | 9.5 | 28.963699340820312 | 28.96139907836914 | 5.356500148773193 | 32.476898193359375 | 20.186100006103516 | -0.12700000405311584 | 4.414000034332275 | 32.29999923706055 | 1.6480000019073486 | 1.4730000495910645 | 0.02500000037252903 |
10.100000381469727 | 10.0 | 28.97170066833496 | 28.96929931640625 | 5.35830020904541 | 32.483699798583984 | 20.188600540161133 | -0.04399999976158142 | 4.419000148773193 | 30.200000762939453 | 1.61899995803833 | 1.4709999561309814 | 0.027000000700354576 |
10.600000381469727 | 10.5 | 28.980100631713867 | 28.977500915527344 | 5.360099792480469 | 32.489898681640625 | 20.190500259399414 | -0.19599999487400055 | 4.415999889373779 | 26.299999237060547 | 1.559000015258789 | 1.4709999561309814 | 0.02500000037252903 |
11.100000381469727 | 11.0 | 28.990100860595703 | 28.987499237060547 | 5.3632001876831055 | 32.50389862060547 | 20.19770050048828 | 0.15299999713897705 | 4.408999919891357 | 25.5 | 1.5460000038146973 | 1.496000051498413 | 0.02500000037252903 |
11.600000381469727 | 11.5 | 28.98740005493164 | 28.984600067138672 | 5.372499942779541 | 32.56919860839844 | 20.247600555419922 | -0.08699999749660492 | 4.396999835968018 | 23.899999618530273 | 1.5180000066757202 | 1.4819999933242798 | 0.027000000700354576 |
12.100000381469727 | 12.0 | 28.878000259399414 | 28.875099182128906 | 5.386300086975098 | 32.73830032348633 | 20.410600662231445 | 0.2619999945163727 | 4.3979997634887695 | 21.700000762939453 | 1.475000023841858 | 1.5570000410079956 | 0.027000000700354576 |
12.699999809265137 | 12.5 | 28.768600463867188 | 28.765600204467773 | 5.400100231170654 | 32.90739822387695 | 20.57360076904297 | 0.6110000014305115 | 4.39900016784668 | 19.700000762939453 | 1.4329999685287476 | 1.6319999694824219 | 0.027000000700354576 |
13.100000381469727 | 13.0 | 28.70709991455078 | 28.703899383544922 | 5.396900177001953 | 32.928199768066406 | 20.609600067138672 | 0.2669999897480011 | 4.390999794006348 | 18.200000762939453 | 1.3980000019073486 | 1.7079999446868896 | 0.029999999329447746 |
13.600000381469727 | 13.5 | 28.476499557495117 | 28.47319984436035 | 5.412099838256836 | 33.19369888305664 | 20.884700775146484 | 0.05400000140070915 | 4.386000156402588 | 15.899999618530273 | 1.340999960899353 | 1.6970000267028809 | 0.027000000700354576 |
14.100000381469727 | 14.0 | 28.123199462890625 | 28.11989974975586 | 5.4421000480651855 | 33.64849853515625 | 21.341899871826172 | 0.2280000001192093 | 4.396999835968018 | 14.0 | 1.2860000133514404 | 1.6339999437332153 | 0.026000000536441803 |
14.600000381469727 | 14.5 | 27.991600036621094 | 27.98819923400879 | 5.440400123596191 | 33.7317008972168 | 21.447200775146484 | -0.013000000268220901 | 4.410999774932861 | 12.699999809265137 | 1.24399995803833 | 1.5679999589920044 | 0.027000000700354576 |
In total, there are 26 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.