Metocean: Historical collections
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Brought to you by NOAA NMFS SWFSC ERD |
Dataset Title: | "NEGOM CTD - n2l06s01.nc - 30.18N, 85.89W - 1998-05-11" |
Institution: | OCEAN.TAMU (Dataset ID: negom_n2l06s01) |
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 |
2.5 | 2.5 | 20.79360008239746 | 20.793100357055664 | 4.912700176239014 | 35.32270050048828 | 24.795799255371094 | 0.17499999701976776 | 3.9760000705718994 | 74.68000030517578 | 1.965000033378601 | 1.371999979019165 | 0.28700000047683716 |
3.0 | 3.0 | 20.78969955444336 | 20.789100646972656 | 4.912499904632568 | 35.32400131225586 | 24.79789924621582 | 0.33399999141693115 | 3.9790000915527344 | 40.91999816894531 | 1.7589999437332153 | 1.3830000162124634 | 0.24300000071525574 |
3.5 | 3.5 | 20.7893009185791 | 20.78860092163086 | 4.912499904632568 | 35.32429885864258 | 24.798200607299805 | 0.22300000488758087 | 3.9749999046325684 | 35.04999923706055 | 1.6979999542236328 | 1.378999948501587 | 0.24199999868869781 |
4.0 | 4.0 | 20.780899047851562 | 20.780099868774414 | 4.9120001792907715 | 35.326698303222656 | 24.802400588989258 | 0.3490000069141388 | 3.9800000190734863 | 30.5 | 1.6380000114440918 | 1.4010000228881836 | 0.23499999940395355 |
4.5 | 4.5 | 20.743200302124023 | 20.742300033569336 | 4.909800052642822 | 35.340301513671875 | 24.822999954223633 | 0.4009999930858612 | 3.9809999465942383 | 27.600000381469727 | 1.593999981880188 | 1.3990000486373901 | 0.23899999260902405 |
5.0 | 5.0 | 20.748199462890625 | 20.74720001220703 | 4.910099983215332 | 35.33869934082031 | 24.82040023803711 | 0.27900001406669617 | 3.9760000705718994 | 24.520000457763672 | 1.5420000553131104 | 1.3960000276565552 | 0.25099998712539673 |
5.5 | 5.5 | 20.742000579833984 | 20.740999221801758 | 4.909800052642822 | 35.34120178222656 | 24.823999404907227 | 0.33500000834465027 | 3.9760000705718994 | 19.81999969482422 | 1.4509999752044678 | 1.3839999437332153 | 0.2540000081062317 |
6.0 | 6.0 | 20.717300415039062 | 20.71619987487793 | 4.908299922943115 | 35.349300384521484 | 24.83690071105957 | 0.3070000112056732 | 3.9769999980926514 | 18.510000228881836 | 1.4199999570846558 | 1.3799999952316284 | 0.24400000274181366 |
6.5 | 6.5 | 20.71489906311035 | 20.713699340820312 | 4.908100128173828 | 35.34939956665039 | 24.837600708007812 | 0.27000001072883606 | 3.9760000705718994 | 16.8799991607666 | 1.38100004196167 | 1.3839999437332153 | 0.24699999392032623 |
7.0 | 7.0 | 20.707000732421875 | 20.705699920654297 | 4.907700061798096 | 35.352298736572266 | 24.841999053955078 | 0.4339999854564667 | 3.9730000495910645 | 14.90999984741211 | 1.3270000219345093 | 1.3849999904632568 | 0.23999999463558197 |
7.599999904632568 | 7.5 | 20.464599609375 | 20.463199615478516 | 4.892899990081787 | 35.43470001220703 | 24.97010040283203 | 0.34299999475479126 | 3.9590001106262207 | 13.109999656677246 | 1.2699999809265137 | 1.409999966621399 | 0.2409999966621399 |
8.100000381469727 | 8.0 | 20.17970085144043 | 20.178199768066406 | 4.875400066375732 | 35.53129959106445 | 25.119800567626953 | 0.23999999463558197 | 3.950000047683716 | 11.619999885559082 | 1.218999981880188 | 1.4630000591278076 | 0.25999999046325684 |
8.600000381469727 | 8.5 | 20.02720069885254 | 20.02560043334961 | 4.865699768066406 | 35.580101013183594 | 25.197500228881836 | 0.2669999897480011 | 3.884000062942505 | 10.84000015258789 | 1.187999963760376 | 1.5410000085830688 | 0.2809999883174896 |
9.100000381469727 | 9.0 | 19.863399505615234 | 19.8617000579834 | 4.855500221252441 | 35.63520050048828 | 25.282800674438477 | 0.4449999928474426 | 3.75600004196167 | 9.579999923706055 | 1.1349999904632568 | 1.6109999418258667 | 0.3050000071525574 |
9.600000381469727 | 9.5 | 19.71139907836914 | 19.7096004486084 | 4.846099853515625 | 35.686798095703125 | 25.362199783325195 | 0.4650000035762787 | 3.683000087738037 | 8.729999542236328 | 1.093999981880188 | 1.6920000314712524 | 0.36399999260902405 |
10.100000381469727 | 10.0 | 19.70359992980957 | 19.701799392700195 | 4.845200061798096 | 35.686100006103516 | 25.363800048828125 | 0.23999999463558197 | 3.4660000801086426 | 7.949999809265137 | 1.0540000200271606 | 1.99399995803833 | 0.45100000500679016 |
10.600000381469727 | 10.5 | 19.571199417114258 | 19.569299697875977 | 4.84119987487793 | 35.76559829711914 | 25.4591007232666 | 0.37400001287460327 | 3.364000082015991 | 7.349999904632568 | 1.0190000534057617 | 2.132999897003174 | 0.5389999747276306 |
11.100000381469727 | 11.0 | 19.409700393676758 | 19.407699584960938 | 4.8404998779296875 | 35.89830017089844 | 25.60260009765625 | 0.5019999742507935 | 3.2079999446868896 | 6.289999961853027 | 0.9520000219345093 | 2.0789999961853027 | 0.5350000262260437 |
11.600000381469727 | 11.5 | 19.344900131225586 | 19.34280014038086 | 4.8379998207092285 | 35.933101654052734 | 25.646099090576172 | 0.47600001096725464 | 3.296999931335449 | 5.25 | 0.8730000257492065 | 2.071000099182129 | 0.531000018119812 |
12.100000381469727 | 12.0 | 19.34670066833496 | 19.344499588012695 | 4.837800025939941 | 35.930198669433594 | 25.64349937438965 | 0.27799999713897705 | 3.3959999084472656 | 4.550000190734863 | 0.8109999895095825 | 2.0390000343322754 | 0.46799999475479126 |
12.600000381469727 | 12.5 | 19.20170021057129 | 19.199399948120117 | 4.831900119781494 | 36.00579833984375 | 25.738800048828125 | 0.4690000116825104 | 3.5220000743865967 | 3.799999952316284 | 0.7329999804496765 | 1.996999979019165 | 0.4050000011920929 |
13.100000381469727 | 13.0 | 19.150999069213867 | 19.14859962463379 | 4.82919979095459 | 36.02750015258789 | 25.76849937438965 | 0.5170000195503235 | 3.630000114440918 | 3.2200000286102295 | 0.6610000133514404 | 1.9529999494552612 | 0.4050000011920929 |
13.600000381469727 | 13.5 | 19.15489959716797 | 19.15250015258789 | 4.829400062561035 | 36.0255012512207 | 25.766000747680664 | 0.29600000381469727 | 3.6989998817443848 | 2.809999942779541 | 0.6019999980926514 | 1.9299999475479126 | 0.4339999854564667 |
14.100000381469727 | 14.0 | 19.135000228881836 | 19.13249969482422 | 4.828700065612793 | 36.03630065917969 | 25.779499053955078 | 0.23800000548362732 | 3.734999895095825 | 2.390000104904175 | 0.5320000052452087 | 1.8919999599456787 | 0.3790000081062317 |
14.600000381469727 | 14.5 | 19.103599548339844 | 19.10099983215332 | 4.827099800109863 | 36.05030059814453 | 25.798200607299805 | 0.2329999953508377 | 3.7720000743865967 | 2.1700000762939453 | 0.48899999260902405 | 1.8919999599456787 | 0.3240000009536743 |
15.100000381469727 | 15.0 | 19.09670066833496 | 19.0939998626709 | 4.8267998695373535 | 36.053199768066406 | 25.802200317382812 | 0.20200000703334808 | 3.7860000133514404 | 1.8600000143051147 | 0.421999990940094 | 1.8639999628067017 | 0.34700000286102295 |
15.600000381469727 | 15.5 | 19.08530044555664 | 19.082500457763672 | 4.826200008392334 | 36.05799865722656 | 25.808900833129883 | 0.21400000154972076 | 3.808000087738037 | 1.6799999475479126 | 0.3779999911785126 | 1.8819999694824219 | 0.3659999966621399 |
16.100000381469727 | 16.0 | 19.087200164794922 | 19.084299087524414 | 4.826499938964844 | 36.05889892578125 | 25.809099197387695 | 0.20999999344348907 | 3.819999933242798 | 1.4600000381469727 | 0.31700000166893005 | 1.8660000562667847 | 0.38100001215934753 |
16.600000381469727 | 16.5 | 19.08340072631836 | 19.080400466918945 | 4.826200008392334 | 36.05979919433594 | 25.810800552368164 | 0.20000000298023224 | 3.813999891281128 | 1.2899999618530273 | 0.26600000262260437 | 1.815999984741211 | 0.38100001215934753 |
17.100000381469727 | 17.0 | 19.08049964904785 | 19.07740020751953 | 4.826000213623047 | 36.059898376464844 | 25.811599731445312 | 0.33000001311302185 | 3.815000057220459 | 1.1699999570846558 | 0.22200000286102295 | 1.8229999542236328 | 0.3659999966621399 |
17.600000381469727 | 17.5 | 19.08180046081543 | 19.07859992980957 | 4.826099872589111 | 36.05970001220703 | 25.811199188232422 | 0.20900000631809235 | 3.812000036239624 | 1.0499999523162842 | 0.17299999296665192 | 1.8650000095367432 | 0.34299999475479126 |
18.100000381469727 | 18.0 | 19.08099937438965 | 19.077699661254883 | 4.826000213623047 | 36.059600830078125 | 25.81130027770996 | 0.3050000071525574 | 3.819000005722046 | 0.949999988079071 | 0.12999999523162842 | 1.819000005722046 | 0.36899998784065247 |
18.600000381469727 | 18.5 | 19.08099937438965 | 19.077699661254883 | 4.826099872589111 | 36.05979919433594 | 25.811500549316406 | 0.30300000309944153 | 3.812999963760376 | 0.8500000238418579 | 0.08299999684095383 | 1.840000033378601 | 0.3919999897480011 |
19.100000381469727 | 19.0 | 19.083099365234375 | 19.079700469970703 | 4.826300144195557 | 36.05929946899414 | 25.81060028076172 | 0.10899999737739563 | 3.821000099182129 | 0.75 | 0.028999999165534973 | 1.8700000047683716 | 0.38100001215934753 |
In total, there are 34 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.