Metocean: Historical collections
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Brought to you by NOAA NMFS SWFSC ERD |
Dataset Title: | "LATEX CTD - d92a074.nc - 28.97N, 90.51W - 1992-05-05" |
Institution: | Texas A&M University, Department of Oceanography (Dataset ID: latex_d92a074) |
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 | 24.332599639892578 | 24.332300186157227 | 4.320899963378906 | 28.222700119018555 | 18.42569923400879 | 0.1420000046491623 | 2.744999885559082 | 176.0 | 2.7309999465942383 | 2.9159998893737793 | -9.0 |
2.0 | 2.0 | 24.324199676513672 | 24.32379913330078 | 4.320300102233887 | 28.224000930786133 | 18.429100036621094 | 0.3630000054836273 | 2.7200000286102295 | 144.0 | 2.6429998874664307 | 2.7730000019073486 | -9.0 |
2.5 | 2.5 | 24.130599975585938 | 24.13010025024414 | 4.3078999519348145 | 28.25510025024414 | 18.508399963378906 | 0.39100000262260437 | 2.6110000610351562 | 113.0 | 2.5380001068115234 | 3.1630001068115234 | -9.0 |
3.0 | 3.0 | 23.845300674438477 | 23.84469985961914 | 4.291399955749512 | 28.31439971923828 | 18.635000228881836 | 0.47600001096725464 | 2.7070000171661377 | 94.9000015258789 | 2.4630000591278076 | 3.9739999771118164 | -9.0 |
3.5 | 3.5 | 23.654199600219727 | 23.653499603271484 | 4.27869987487793 | 28.342199325561523 | 18.710500717163086 | 0.46700000762939453 | 2.6710000038146973 | 76.0999984741211 | 2.367000102996826 | 4.070000171661377 | -9.0 |
4.0 | 4.0 | 23.49169921875 | 23.49090003967285 | 4.2652997970581055 | 28.346500396728516 | 18.759700775146484 | 0.4580000042915344 | 2.743000030517578 | 61.70000076293945 | 2.2760000228881836 | 4.098999977111816 | -9.0 |
4.5 | 4.5 | 23.405000686645508 | 23.40410041809082 | 4.256499767303467 | 28.336000442504883 | 18.77630043029785 | 0.46700000762939453 | 2.747999906539917 | 49.79999923706055 | 2.183000087738037 | 4.203999996185303 | -9.0 |
5.0 | 5.0 | 23.3439998626709 | 23.343000411987305 | 4.249300003051758 | 28.322099685668945 | 18.783000946044922 | 0.4399999976158142 | 2.7769999504089355 | 41.20000076293945 | 2.1010000705718994 | 4.236999988555908 | -9.0 |
5.5 | 5.5 | 23.252399444580078 | 23.251300811767578 | 4.239200115203857 | 28.305099487304688 | 18.79599952697754 | 0.40799999237060547 | 2.799999952316284 | 34.20000076293945 | 2.0199999809265137 | 4.190999984741211 | -9.0 |
6.0 | 6.0 | 23.193099975585938 | 23.1919002532959 | 4.242099761962891 | 28.364500045776367 | 18.857500076293945 | 0.4480000138282776 | 2.8610000610351562 | 28.5 | 1.940000057220459 | 4.480000019073486 | -9.0 |
6.5 | 6.5 | 22.97920036315918 | 22.9778995513916 | 4.258699893951416 | 28.626399993896484 | 19.11520004272461 | 0.48399999737739563 | 3.3010001182556152 | 23.899999618530273 | 1.8639999628067017 | 4.104000091552734 | -9.0 |
7.0 | 7.0 | 22.996700286865234 | 22.99530029296875 | 4.283100128173828 | 28.797399520874023 | 19.239599227905273 | 0.44699999690055847 | 3.50600004196167 | 19.899999618530273 | 1.784999966621399 | 3.190999984741211 | -9.0 |
7.599999904632568 | 7.5 | 22.9414005279541 | 22.939899444580078 | 4.343500137329102 | 29.287200927734375 | 19.625499725341797 | 0.24899999797344208 | 3.3489999771118164 | 17.0 | 1.715999960899353 | 2.759000062942505 | -9.0 |
8.100000381469727 | 8.0 | 22.88610076904297 | 22.88450050354004 | 4.403900146484375 | 29.779199600219727 | 20.01289939880371 | 0.2809999883174896 | 3.313999891281128 | 14.399999618530273 | 1.6430000066757202 | 2.4830000400543213 | -9.0 |
8.600000381469727 | 8.5 | 22.2726993560791 | 22.270999908447266 | 4.775400161743164 | 33.062400817871094 | 22.67180061340332 | 0.24799999594688416 | 3.9690001010894775 | 12.5 | 1.5839999914169312 | 1.399999976158142 | -9.0 |
9.100000381469727 | 9.0 | 22.20829963684082 | 22.206499099731445 | 4.8308000564575195 | 33.54159927368164 | 23.053499221801758 | 0.20000000298023224 | 4.144999980926514 | 10.800000190734863 | 1.5190000534057617 | 0.7960000038146973 | -9.0 |
9.600000381469727 | 9.5 | 22.14389991760254 | 22.142000198364258 | 4.886099815368652 | 34.02299880981445 | 23.436899185180664 | 0.20200000703334808 | 3.9860000610351562 | 9.380000114440918 | 1.4579999446868896 | 0.9649999737739563 | -9.0 |
10.100000381469727 | 10.0 | 22.076000213623047 | 22.073999404907227 | 4.89709997177124 | 34.16239929199219 | 23.56170082092285 | 0.24199999868869781 | 3.9019999504089355 | 8.380000114440918 | 1.409000039100647 | 0.7879999876022339 | -9.0 |
10.600000381469727 | 10.5 | 22.044099807739258 | 22.04199981689453 | 4.9095001220703125 | 34.284698486328125 | 23.66349983215332 | 0.21299999952316284 | 3.7750000953674316 | 7.840000152587891 | 1.3799999952316284 | 0.6629999876022339 | -9.0 |
In total, there are 19 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.