Metocean: Historical collections
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Dataset Title: | "LATEX CTD - d93e107.nc - 29.12N, 94.0W - 1993-05-02" |
Institution: | Texas A&M University, Department of Oceanography (Dataset ID: latex_d93e107) |
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 | 21.248899459838867 | 21.248600006103516 | 4.674200057983398 | 33.0546989440918 | 22.947900772094727 | 0.13600000739097595 | 3.447999954223633 | 776.0 | 3.9200000762939453 | 1.5820000171661377 | -9.0 |
2.5 | 2.5 | 21.254600524902344 | 21.254100799560547 | 4.674799919128418 | 33.054500579833984 | 22.946300506591797 | 0.17800000309944153 | 3.447000026702881 | 473.0 | 3.7049999237060547 | 1.5679999589920044 | -9.0 |
3.0 | 3.0 | 21.258699417114258 | 21.258100509643555 | 4.67519998550415 | 33.05459976196289 | 22.94529914855957 | 0.675000011920929 | 3.433000087738037 | 412.0 | 3.6449999809265137 | 1.5950000286102295 | -9.0 |
3.5 | 3.5 | 21.25819969177246 | 21.257600784301758 | 4.67519998550415 | 33.0546989440918 | 22.945499420166016 | 0.6949999928474426 | 3.441999912261963 | 339.0 | 3.559999942779541 | 1.6119999885559082 | -9.0 |
4.0 | 4.0 | 21.257699966430664 | 21.256999969482422 | 4.67519998550415 | 33.0547981262207 | 22.94569969177246 | 0.44699999690055847 | 3.4509999752044678 | 284.0 | 3.4830000400543213 | 1.6019999980926514 | -9.0 |
4.5 | 4.5 | 21.25550079345703 | 21.254600524902344 | 4.675000190734863 | 33.054901123046875 | 22.946500778198242 | 0.18299999833106995 | 3.4590001106262207 | 221.0 | 3.375 | 1.6119999885559082 | -9.0 |
5.0 | 5.0 | 21.256399154663086 | 21.255399703979492 | 4.675099849700928 | 33.05500030517578 | 22.946300506591797 | 0.6880000233650208 | 3.4579999446868896 | 179.0 | 3.2829999923706055 | 1.625 | -9.0 |
5.5 | 5.5 | 21.257400512695312 | 21.256399154663086 | 4.67519998550415 | 33.055198669433594 | 22.946199417114258 | 0.8220000267028809 | 3.4519999027252197 | 152.0 | 3.2130000591278076 | 1.6269999742507935 | -9.0 |
6.0 | 6.0 | 21.259899139404297 | 21.258699417114258 | 4.67549991607666 | 33.05500030517578 | 22.94540023803711 | 0.7770000100135803 | 3.4579999446868896 | 131.0 | 3.1470000743865967 | 1.628999948501587 | -9.0 |
6.5 | 6.5 | 21.259599685668945 | 21.25830078125 | 4.67549991607666 | 33.055301666259766 | 22.94569969177246 | 0.375 | 3.4679999351501465 | 117.0 | 3.0980000495910645 | 1.6369999647140503 | -9.0 |
7.0 | 7.0 | 21.260799407958984 | 21.2593994140625 | 4.6757001876831055 | 33.05569839477539 | 22.94580078125 | 0.3190000057220459 | 3.4719998836517334 | 80.9000015258789 | 2.937999963760376 | 1.6859999895095825 | -9.0 |
7.599999904632568 | 7.5 | 21.25279998779297 | 21.251300811767578 | 4.675000190734863 | 33.05580139160156 | 22.94809913635254 | 0.621999979019165 | 3.4760000705718994 | 70.5999984741211 | 2.878999948501587 | 1.6629999876022339 | -9.0 |
8.100000381469727 | 8.0 | 21.23780059814453 | 21.2362003326416 | 4.673600196838379 | 33.056400299072266 | 22.952499389648438 | 0.7310000061988831 | 3.4769999980926514 | 68.4000015258789 | 2.865000009536743 | 1.6540000438690186 | -9.0 |
8.600000381469727 | 8.5 | 21.236299514770508 | 21.234699249267578 | 4.673500061035156 | 33.056800842285156 | 22.95330047607422 | 0.7170000076293945 | 3.4769999980926514 | 62.5 | 2.8259999752044678 | 1.6610000133514404 | -9.0 |
9.100000381469727 | 9.0 | 21.233200073242188 | 21.231399536132812 | 4.673099994659424 | 33.05590057373047 | 22.953500747680664 | 0.40799999237060547 | 3.4749999046325684 | 52.599998474121094 | 2.750999927520752 | 1.6729999780654907 | -9.0 |
9.600000381469727 | 9.5 | 21.155899047851562 | 21.15410041809082 | 4.665500164031982 | 33.0547981262207 | 22.973600387573242 | 0.6129999756813049 | 3.447999954223633 | 46.599998474121094 | 2.697999954223633 | 1.7510000467300415 | -9.0 |
10.100000381469727 | 10.0 | 21.132099151611328 | 21.130199432373047 | 4.663300037384033 | 33.055999755859375 | 22.980899810791016 | 0.8799999952316284 | 3.4509999752044678 | 41.099998474121094 | 2.6440000534057617 | 1.7339999675750732 | -9.0 |
10.600000381469727 | 10.5 | 21.128999710083008 | 21.12700080871582 | 4.663099765777588 | 33.056400299072266 | 22.982099533081055 | 0.9089999794960022 | 3.447999954223633 | 35.29999923706055 | 2.578000068664551 | 1.7430000305175781 | -9.0 |
11.100000381469727 | 11.0 | 21.12929916381836 | 21.127199172973633 | 4.6631999015808105 | 33.05630111694336 | 22.98200035095215 | 0.6890000104904175 | 3.4539999961853027 | 29.700000762939453 | 2.503000020980835 | 1.7259999513626099 | -9.0 |
11.600000381469727 | 11.5 | 21.129100799560547 | 21.12689971923828 | 4.663300037384033 | 33.05730056762695 | 22.982799530029297 | 0.41999998688697815 | 3.453000068664551 | 25.200000762939453 | 2.430999994277954 | 1.7259999513626099 | -9.0 |
12.100000381469727 | 12.0 | 21.116899490356445 | 21.114599227905273 | 4.662399768829346 | 33.059898376464844 | 22.98819923400879 | 0.8059999942779541 | 3.4489998817443848 | 22.200000762939453 | 2.375999927520752 | 1.7450000047683716 | -9.0 |
12.600000381469727 | 12.5 | 21.11639976501465 | 21.11400032043457 | 4.662399768829346 | 33.05979919433594 | 22.98819923400879 | 0.8109999895095825 | 3.4509999752044678 | 19.299999237060547 | 2.315999984741211 | 1.7419999837875366 | -9.0 |
13.100000381469727 | 13.0 | 21.117000579833984 | 21.114500045776367 | 4.662399768829346 | 33.05950164794922 | 22.98780059814453 | 0.5400000214576721 | 3.4489998817443848 | 16.899999618530273 | 2.257999897003174 | 1.7589999437332153 | -9.0 |
13.600000381469727 | 13.5 | 21.117300033569336 | 21.114700317382812 | 4.662399768829346 | 33.059200286865234 | 22.987499237060547 | 0.21799999475479126 | 3.453000068664551 | 14.399999618530273 | 2.187999963760376 | 1.753000020980835 | -9.0 |
14.100000381469727 | 14.0 | 21.111900329589844 | 21.10930061340332 | 4.661900043487549 | 33.05889892578125 | 22.988800048828125 | 0.5519999861717224 | 3.4200000762939453 | 13.100000381469727 | 2.1480000019073486 | 1.7480000257492065 | -9.0 |
14.600000381469727 | 14.5 | 21.111400604248047 | 21.108699798583984 | 4.661900043487549 | 33.058799743652344 | 22.98889923095703 | 0.32499998807907104 | 3.257999897003174 | 12.199999809265137 | 2.118000030517578 | 1.7649999856948853 | -9.0 |
15.100000381469727 | 15.0 | 21.094100952148438 | 21.09119987487793 | 4.660099983215332 | 33.05830001831055 | 22.993200302124023 | 0.20200000703334808 | 3.052999973297119 | 9.550000190734863 | 2.009999990463257 | 1.8389999866485596 | -9.0 |
15.600000381469727 | 15.5 | 21.090200424194336 | 21.087299346923828 | 4.659900188446045 | 33.05889892578125 | 22.994800567626953 | 0.24899999797344208 | 2.9040000438690186 | 7.130000114440918 | 1.8830000162124634 | 1.8519999980926514 | -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.