Metocean: Historical collections
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Dataset Title: | "LATEX CTD - d94i107.nc - 28.54N, 95.44W - 1994-08-03" |
Institution: | Texas A&M University, Department of Oceanography (Dataset ID: latex_d94i107) |
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 | 28.09709930419922 | 28.096599578857422 | 5.7667999267578125 | 35.942901611328125 | 23.074399948120117 | -0.05000000074505806 | 3.697999954223633 | 32.20000076293945 | 1.6469999551773071 | 1.531000018119812 | 0.16599999368190765 |
2.5 | 2.5 | 28.088899612426758 | 28.088300704956055 | 5.7667999267578125 | 35.94860076904297 | 23.08139991760254 | -0.4560000002384186 | 3.7019999027252197 | 28.600000381469727 | 1.5950000286102295 | 1.5379999876022339 | 0.16200000047683716 |
3.0 | 3.0 | 28.077800750732422 | 28.07699966430664 | 5.7667999267578125 | 35.95750045776367 | 23.091800689697266 | -2.0869998931884766 | 3.7100000381469727 | 24.899999618530273 | 1.534999966621399 | 1.5230000019073486 | 0.16300000250339508 |
3.5 | 3.5 | 28.085100173950195 | 28.084299087524414 | 5.7667999267578125 | 35.95159912109375 | 23.084999084472656 | 0.11800000071525574 | 3.7079999446868896 | 21.399999618530273 | 1.468999981880188 | 1.5429999828338623 | 0.16699999570846558 |
4.0 | 4.0 | 28.095199584960938 | 28.09429931640625 | 5.766600131988525 | 35.94219970703125 | 23.07469940185547 | 0.032999999821186066 | 3.7109999656677246 | 18.600000381469727 | 1.409000039100647 | 1.5470000505447388 | 0.16599999368190765 |
4.5 | 4.5 | 28.099199295043945 | 28.098100662231445 | 5.76639986038208 | 35.93730163574219 | 23.069700241088867 | -0.05900000035762787 | 3.7060000896453857 | 15.899999618530273 | 1.340999960899353 | 1.5490000247955322 | 0.17000000178813934 |
5.0 | 5.0 | 28.101299285888672 | 28.100099563598633 | 5.76639986038208 | 35.936100006103516 | 23.068199157714844 | 0.25600001215934753 | 3.7019999027252197 | 13.300000190734863 | 1.2630000114440918 | 1.5449999570846558 | 0.20800000429153442 |
5.5 | 5.5 | 28.101699829101562 | 28.100400924682617 | 5.76669979095459 | 35.93730163574219 | 23.069000244140625 | 0.5559999942779541 | 3.697000026702881 | 11.199999809265137 | 1.187999963760376 | 1.5770000219345093 | 0.19599999487400055 |
6.0 | 6.0 | 28.101999282836914 | 28.10059928894043 | 5.767000198364258 | 35.939300537109375 | 23.07040023803711 | -0.0430000014603138 | 3.687999963760376 | 9.880000114440918 | 1.1339999437332153 | 1.5859999656677246 | 0.16899999976158142 |
6.5 | 6.5 | 28.101900100708008 | 28.100400924682617 | 5.767300128936768 | 35.941200256347656 | 23.0718994140625 | -0.4729999899864197 | 3.703000068664551 | 8.850000381469727 | 1.0859999656677246 | 1.562999963760376 | 0.16599999368190765 |
7.0 | 7.0 | 28.101499557495117 | 28.09980010986328 | 5.767499923706055 | 35.94260025024414 | 23.073200225830078 | 2.319000005722046 | 3.703000068664551 | 7.960000038146973 | 1.0399999618530273 | 1.5420000553131104 | 0.164000004529953 |
7.599999904632568 | 7.5 | 28.101299285888672 | 28.09950065612793 | 5.767600059509277 | 35.943199157714844 | 23.073699951171875 | 0.07100000232458115 | 3.687999963760376 | 7.159999847412109 | 0.9940000176429749 | 1.5379999876022339 | 0.1899999976158142 |
8.100000381469727 | 8.0 | 28.100900650024414 | 28.099000930786133 | 5.767600059509277 | 35.94390106201172 | 23.074399948120117 | 2.134000062942505 | 3.684000015258789 | 6.440000057220459 | 0.9480000138282776 | 1.5360000133514404 | 0.17000000178813934 |
8.600000381469727 | 8.5 | 28.101200103759766 | 28.09910011291504 | 5.7677001953125 | 35.94430160522461 | 23.074600219726562 | -0.11699999868869781 | 3.694999933242798 | 5.78000020980835 | 0.9010000228881836 | 1.5529999732971191 | 0.17299999296665192 |
9.100000381469727 | 9.0 | 28.101900100708008 | 28.099700927734375 | 5.768099784851074 | 35.945899963378906 | 23.075599670410156 | 0.3779999911785126 | 3.690000057220459 | 5.190000057220459 | 0.8539999723434448 | 1.5410000085830688 | 0.17000000178813934 |
9.600000381469727 | 9.5 | 28.100900650024414 | 28.09869956970215 | 5.76800012588501 | 35.945899963378906 | 23.076000213623047 | -0.6850000023841858 | 3.687999963760376 | 4.619999885559082 | 0.8040000200271606 | 1.5369999408721924 | 0.17299999296665192 |
10.100000381469727 | 10.0 | 28.100900650024414 | 28.098499298095703 | 5.76800012588501 | 35.945899963378906 | 23.076000213623047 | 0.06800000369548798 | 3.684999942779541 | 4.110000133514404 | 0.753000020980835 | 1.5640000104904175 | 0.17499999701976776 |
10.600000381469727 | 10.5 | 28.10110092163086 | 28.098600387573242 | 5.768099784851074 | 35.94609832763672 | 23.076200485229492 | -0.11599999666213989 | 3.684000015258789 | 3.6600000858306885 | 0.703000009059906 | 1.5740000009536743 | 0.17100000381469727 |
11.100000381469727 | 11.0 | 28.10099983215332 | 28.098400115966797 | 5.768099784851074 | 35.94599914550781 | 23.076200485229492 | 0.5830000042915344 | 3.694000005722046 | 3.2799999713897705 | 0.6549999713897705 | 1.5880000591278076 | 0.1679999977350235 |
11.600000381469727 | 11.5 | 28.101200103759766 | 28.098499298095703 | 5.768099784851074 | 35.94609832763672 | 23.076200485229492 | -0.41200000047683716 | 3.690999984741211 | 2.9600000381469727 | 0.6100000143051147 | 1.569000005722046 | 0.17100000381469727 |
12.100000381469727 | 12.0 | 28.101299285888672 | 28.098400115966797 | 5.768199920654297 | 35.94620132446289 | 23.0762996673584 | 0.921999990940094 | 3.691999912261963 | 2.6700000762939453 | 0.5649999976158142 | 1.5529999732971191 | 0.1850000023841858 |
12.600000381469727 | 12.5 | 28.1018009185791 | 28.098800659179688 | 5.768199920654297 | 35.945899963378906 | 23.076000213623047 | -0.25200000405311584 | 3.686000108718872 | 2.430000066757202 | 0.5239999890327454 | 1.5529999732971191 | 0.17399999499320984 |
13.100000381469727 | 13.0 | 28.101699829101562 | 28.098600387573242 | 5.768199920654297 | 35.94599914550781 | 23.076099395751953 | 0.6079999804496765 | 3.694000005722046 | 2.2100000381469727 | 0.48399999737739563 | 1.5329999923706055 | 0.1679999977350235 |
13.600000381469727 | 13.5 | 28.1018009185791 | 28.098600387573242 | 5.7683000564575195 | 35.94620132446289 | 23.076200485229492 | 1.7580000162124634 | 3.686000108718872 | 2.0299999713897705 | 0.44699999690055847 | 1.600000023841858 | 0.1679999977350235 |
14.100000381469727 | 14.0 | 28.101900100708008 | 28.098499298095703 | 5.7683000564575195 | 35.94609832763672 | 23.076200485229492 | 0.1589999943971634 | 3.686000108718872 | 1.8700000047683716 | 0.41100001335144043 | 1.597000002861023 | 0.2750000059604645 |
14.600000381469727 | 14.5 | 28.10110092163086 | 28.09760093688965 | 5.7683000564575195 | 35.94620132446289 | 23.07659912109375 | -3.056999921798706 | 3.691999912261963 | 1.7300000190734863 | 0.37599998712539673 | 1.5759999752044678 | 0.19200000166893005 |
15.100000381469727 | 15.0 | 28.10059928894043 | 28.097000122070312 | 5.768199920654297 | 35.94609832763672 | 23.07670021057129 | 2.071000099182129 | 3.694999933242798 | 1.590000033378601 | 0.3400000035762787 | 1.5509999990463257 | 0.19300000369548798 |
15.600000381469727 | 15.5 | 28.10070037841797 | 28.097000122070312 | 5.7683000564575195 | 35.94620132446289 | 23.07670021057129 | 0.7080000042915344 | 3.694000005722046 | 1.4500000476837158 | 0.3009999990463257 | 1.5360000133514404 | 0.17599999904632568 |
16.100000381469727 | 16.0 | 28.100299835205078 | 28.096500396728516 | 5.768199920654297 | 35.94609832763672 | 23.076900482177734 | 1.468999981880188 | 3.684999942779541 | 1.3200000524520874 | 0.2590000033378601 | 1.531999945640564 | 0.17800000309944153 |
16.600000381469727 | 16.5 | 28.100500106811523 | 28.096500396728516 | 5.7683000564575195 | 35.94599914550781 | 23.076799392700195 | -0.2160000056028366 | 3.683000087738037 | 1.190000057220459 | 0.2160000056028366 | 1.527999997138977 | 0.17499999701976776 |
17.100000381469727 | 17.0 | 28.100400924682617 | 28.09630012512207 | 5.7683000564575195 | 35.94609832763672 | 23.076900482177734 | 0.21299999952316284 | 3.678999900817871 | 1.090000033378601 | 0.17599999904632568 | 1.5160000324249268 | 0.18700000643730164 |
17.600000381469727 | 17.5 | 28.100900650024414 | 28.09670066833496 | 5.768400192260742 | 35.94620132446289 | 23.076900482177734 | -0.06199999898672104 | 3.678999900817871 | 1.0 | 0.1379999965429306 | 1.5160000324249268 | 0.2590000033378601 |
18.100000381469727 | 18.0 | 28.100900650024414 | 28.096599578857422 | 5.768400192260742 | 35.94620132446289 | 23.076900482177734 | 0.1550000011920929 | 3.677999973297119 | 0.9200000166893005 | 0.10400000214576721 | 1.5190000534057617 | 0.19200000166893005 |
18.600000381469727 | 18.5 | 28.10140037536621 | 28.097000122070312 | 5.768499851226807 | 35.94599914550781 | 23.07659912109375 | 1.2339999675750732 | 3.6480000019073486 | 0.8500000238418579 | 0.07000000029802322 | 1.5240000486373901 | 0.1770000010728836 |
19.100000381469727 | 19.0 | 28.101699829101562 | 28.097200393676758 | 5.768499851226807 | 35.94599914550781 | 23.076499938964844 | -1.1490000486373901 | 3.6480000019073486 | 0.7900000214576721 | 0.039000000804662704 | 1.5410000085830688 | 0.1770000010728836 |
19.600000381469727 | 19.5 | 28.102100372314453 | 28.097400665283203 | 5.768599987030029 | 35.94609832763672 | 23.07659912109375 | 0.7350000143051147 | 3.6619999408721924 | 0.7300000190734863 | 0.004000000189989805 | 1.5329999923706055 | 0.17900000512599945 |
20.100000381469727 | 20.0 | 28.10219955444336 | 28.097400665283203 | 5.768599987030029 | 35.94609832763672 | 23.076499938964844 | -0.027000000700354576 | 3.6649999618530273 | 0.7300000190734863 | 0.0010000000474974513 | 1.5520000457763672 | 0.20800000429153442 |
20.600000381469727 | 20.5 | 28.102399826049805 | 28.09760093688965 | 5.768700122833252 | 35.94599914550781 | 23.076499938964844 | 0.3089999854564667 | 3.6549999713897705 | 0.7300000190734863 | 0.0 | 1.5110000371932983 | 0.21799999475479126 |
21.100000381469727 | 21.0 | 28.101699829101562 | 28.09670066833496 | 5.768599987030029 | 35.94620132446289 | 23.076799392700195 | -0.6230000257492065 | 3.6600000858306885 | 0.7300000190734863 | 0.0 | 1.5260000228881836 | 0.2150000035762787 |
21.600000381469727 | 21.5 | 28.101600646972656 | 28.096500396728516 | 5.768599987030029 | 35.94609832763672 | 23.076799392700195 | 1.0729999542236328 | 3.6110000610351562 | 0.7300000190734863 | 0.0 | 1.5420000553131104 | 0.20999999344348907 |
22.100000381469727 | 22.0 | 28.10110092163086 | 28.09589958190918 | 5.768599987030029 | 35.94620132446289 | 23.07710075378418 | 0.19200000166893005 | 3.625999927520752 | 0.7300000190734863 | 0.0 | 1.5509999990463257 | 0.19200000166893005 |
22.700000762939453 | 22.5 | 28.101499557495117 | 28.096200942993164 | 5.768700122833252 | 35.9463005065918 | 23.07710075378418 | -0.12399999797344208 | 3.6589999198913574 | 0.7300000190734863 | 0.0 | 1.5379999876022339 | 0.19300000369548798 |
In total, there are 42 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.