Metocean: Historical collections
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Brought to you by NOAA NMFS SWFSC ERD |
Dataset Title: | "NEGOM CTD - n9l03s00.nc - 30.15N, 88.09W - 2000-07-30" |
Institution: | OCEAN.TAMU (Dataset ID: negom_n9l03s00) |
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 | 29.18470001220703 | 29.184200286865234 | 5.646900177001953 | 34.301998138427734 | 21.478599548339844 | -0.12700000405311584 | 4.0320000648498535 | 737.2000122070312 | 2.4609999656677246 | 1.1410000324249268 | 0.27000001072883606 |
2.5 | 2.5 | 29.147300720214844 | 29.146699905395508 | 5.6468000411987305 | 34.32780075073242 | 21.510400772094727 | 0.4429999887943268 | 3.9509999752044678 | 331.79998779296875 | 2.132999897003174 | 1.1729999780654907 | 0.35499998927116394 |
3.0 | 3.0 | 29.165700912475586 | 29.165000915527344 | 5.647200107574463 | 34.31719970703125 | 21.496400833129883 | 0.2160000056028366 | 3.947999954223633 | 273.1000061035156 | 2.0450000762939453 | 1.2000000476837158 | 0.3799999952316284 |
3.5 | 3.5 | 29.14859962463379 | 29.1476993560791 | 5.64739990234375 | 34.33100128173828 | 21.512500762939453 | 0.32499998807907104 | 4.017000198364258 | 246.60000610351562 | 2.000999927520752 | 1.1890000104904175 | 0.34700000286102295 |
4.0 | 4.0 | 29.154199600219727 | 29.153200149536133 | 5.64769983291626 | 34.32899856567383 | 21.509199142456055 | 0.24899999797344208 | 4.0 | 221.6999969482422 | 1.9539999961853027 | 1.1610000133514404 | 0.2630000114440918 |
4.5 | 4.5 | 29.124500274658203 | 29.12339973449707 | 5.650899887084961 | 34.37189865112305 | 21.55139923095703 | 0.3100000023841858 | 3.9709999561309814 | 180.3000030517578 | 1.8630000352859497 | 1.1920000314712524 | 0.3709999918937683 |
5.0 | 5.0 | 28.89069938659668 | 28.88949966430664 | 5.6519999504089355 | 34.548301696777344 | 21.76180076599121 | 0.20999999344348907 | 3.934999942779541 | 153.8000030517578 | 1.7960000038146973 | 1.1929999589920044 | 0.4490000009536743 |
5.5 | 5.5 | 28.84160041809082 | 28.840299606323242 | 5.651400089263916 | 34.57939910888672 | 21.801599502563477 | 0.21899999678134918 | 3.8559999465942383 | 132.6999969482422 | 1.7309999465942383 | 1.25600004196167 | 0.48899999260902405 |
6.0 | 6.0 | 28.804500579833984 | 28.80299949645996 | 5.657299995422363 | 34.64670181274414 | 21.864500045776367 | 0.3529999852180481 | 3.8259999752044678 | 113.5 | 1.6640000343322754 | 1.2549999952316284 | 0.45500001311302185 |
6.5 | 6.5 | 28.804899215698242 | 28.803300857543945 | 5.6620001792907715 | 34.679100036621094 | 21.888700485229492 | 0.23999999463558197 | 3.7739999294281006 | 99.06999969482422 | 1.6050000190734863 | 1.2519999742507935 | 0.48100000619888306 |
7.0 | 7.0 | 28.815099716186523 | 28.813400268554688 | 5.667699813842773 | 34.71080017089844 | 21.909099578857422 | 0.27399998903274536 | 3.7869999408721924 | 92.08999633789062 | 1.5729999542236328 | 1.2549999952316284 | 0.5059999823570251 |
7.599999904632568 | 7.5 | 28.79990005493164 | 28.798099517822266 | 5.678599834442139 | 34.7963981628418 | 21.978500366210938 | 0.24400000274181366 | 3.7990000247955322 | 80.37000274658203 | 1.5140000581741333 | 1.2699999809265137 | 0.4490000009536743 |
8.100000381469727 | 8.0 | 28.636999130249023 | 28.635099411010742 | 5.665599822998047 | 34.82569885253906 | 22.05470085144043 | 0.1770000010728836 | 3.8389999866485596 | 69.33999633789062 | 1.4500000476837158 | 1.2619999647140503 | 0.3919999897480011 |
8.600000381469727 | 8.5 | 27.424400329589844 | 27.422399520874023 | 5.57420015335083 | 35.084999084472656 | 22.64620018005371 | 0.17499999701976776 | 3.690000057220459 | 63.439998626708984 | 1.4110000133514404 | 1.2669999599456787 | 0.4560000002384186 |
9.100000381469727 | 9.0 | 27.13960075378418 | 27.137500762939453 | 5.555500030517578 | 35.16640090942383 | 22.799100875854492 | 0.2720000147819519 | 3.321000099182129 | 58.36000061035156 | 1.375 | 1.4270000457763672 | 0.847000002861023 |
9.600000381469727 | 9.5 | 26.079599380493164 | 26.077499389648438 | 5.492700099945068 | 35.52470016479492 | 23.405099868774414 | 0.18299999833106995 | 2.7760000228881836 | 51.150001525878906 | 1.3179999589920044 | 1.593999981880188 | 1.2380000352859497 |
10.100000381469727 | 10.0 | 25.56800079345703 | 25.565799713134766 | 5.462699890136719 | 35.704200744628906 | 23.69969940185547 | 0.2800000011920929 | 2.2109999656677246 | 42.65999984741211 | 1.2380000352859497 | 1.656999945640564 | 1.5549999475479126 |
10.600000381469727 | 10.5 | 25.376300811767578 | 25.374000549316406 | 5.453100204467773 | 35.78369903564453 | 23.819000244140625 | 0.0 | 2.3889999389648438 | 31.719999313354492 | 1.1160000562667847 | 1.6519999504089355 | 2.0859999656677246 |
In total, there are 18 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.