Metocean: Historical collections
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Brought to you by NOAA NMFS SWFSC ERD |
Dataset Title: | "NEGOM CTD - n5l03s01.nc - 30.1N, 88.07W - 1999-05-26" |
Institution: | OCEAN.TAMU (Dataset ID: negom_n5l03s01) |
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 | 27.172100067138672 | 27.171499252319336 | 4.502099990844727 | 27.767799377441406 | 17.227800369262695 | -0.03400000184774399 | 3.984999895095825 | 356.8999938964844 | 2.1619999408721924 | 1.6169999837875366 | 0.7369999885559082 |
3.0 | 3.0 | 27.16950035095215 | 27.168800354003906 | 4.502099990844727 | 27.76930046081543 | 17.229799270629883 | 0.32499998807907104 | 3.989000082015991 | 311.8999938964844 | 2.1029999256134033 | 1.6169999837875366 | 0.7300000190734863 |
3.5 | 3.5 | 27.169099807739258 | 27.16830062866211 | 4.502200126647949 | 27.770200729370117 | 17.230600357055664 | 0.22699999809265137 | 3.994999885559082 | 249.10000610351562 | 2.00600004196167 | 1.63100004196167 | 0.7160000205039978 |
4.0 | 4.0 | 27.169200897216797 | 27.16830062866211 | 4.502299785614014 | 27.77090072631836 | 17.23110008239746 | 0.5239999890327454 | 3.993000030517578 | 208.39999389648438 | 1.9270000457763672 | 1.6549999713897705 | 0.7229999899864197 |
4.5 | 4.5 | 27.160999298095703 | 27.15999984741211 | 4.504300117492676 | 27.7893009185791 | 17.247499465942383 | 0.5509999990463257 | 3.9709999561309814 | 181.0 | 1.8669999837875366 | 1.652999997138977 | 0.7110000252723694 |
5.0 | 5.0 | 27.158100128173828 | 27.156999588012695 | 4.504899978637695 | 27.7947998046875 | 17.252599716186523 | 0.3149999976158142 | 3.9800000190734863 | 148.0 | 1.777999997138977 | 1.649999976158142 | 0.7120000123977661 |
5.5 | 5.5 | 27.149799346923828 | 27.14859962463379 | 4.507999897003174 | 27.821199417114258 | 17.274999618530273 | 0.31700000166893005 | 3.9649999141693115 | 125.80000305175781 | 1.7089999914169312 | 1.6790000200271606 | 0.7289999723434448 |
6.099999904632568 | 6.0 | 27.12529945373535 | 27.123899459838867 | 4.519800186157227 | 27.916500091552734 | 17.35420036315918 | 0.4259999990463257 | 3.9719998836517334 | 110.5 | 1.6510000228881836 | 1.7200000286102295 | 0.7379999756813049 |
6.599999904632568 | 6.5 | 27.087900161743164 | 27.08639907836914 | 4.540999889373779 | 28.0851993560791 | 17.492399215698242 | 0.36800000071525574 | 4.013999938964844 | 94.75 | 1.5850000381469727 | 1.7259999513626099 | 0.7089999914169312 |
7.099999904632568 | 7.0 | 26.966999053955078 | 26.96540069580078 | 4.601099967956543 | 28.57390022277832 | 17.896900177001953 | 0.3779999911785126 | 4.014999866485596 | 81.54000091552734 | 1.5199999809265137 | 1.6699999570846558 | 0.7239999771118164 |
7.599999904632568 | 7.5 | 26.78730010986328 | 26.785600662231445 | 4.68779993057251 | 29.288700103759766 | 18.489500045776367 | 0.34700000286102295 | 4.013000011444092 | 72.22000122070312 | 1.4670000076293945 | 1.6649999618530273 | 0.7210000157356262 |
8.100000381469727 | 8.0 | 26.077899932861328 | 26.076099395751953 | 4.867700099945068 | 31.020299911499023 | 20.010799407958984 | 0.2750000059604645 | 3.999000072479248 | 64.7300033569336 | 1.4199999570846558 | 1.5839999914169312 | 0.7289999723434448 |
8.600000381469727 | 8.5 | 25.43779945373535 | 25.43589973449707 | 4.956299781799316 | 32.098899841308594 | 21.018800735473633 | 0.3700000047683716 | 4.072999954223633 | 59.08000183105469 | 1.3799999952316284 | 1.5260000228881836 | 0.7300000190734863 |
9.100000381469727 | 9.0 | 25.225500106811523 | 25.223499298095703 | 5.000899791717529 | 32.574798583984375 | 21.442100524902344 | 0.42500001192092896 | 4.083000183105469 | 54.84000015258789 | 1.3480000495910645 | 1.5130000114440918 | 0.7260000109672546 |
9.600000381469727 | 9.5 | 25.016799926757812 | 25.014699935913086 | 5.018400192260742 | 32.85319900512695 | 21.715299606323242 | 0.3490000069141388 | 4.067999839782715 | 50.900001525878906 | 1.315000057220459 | 1.5299999713897705 | 0.7229999899864197 |
10.100000381469727 | 10.0 | 24.332300186157227 | 24.3302001953125 | 4.97760009765625 | 33.052101135253906 | 22.070100784301758 | 0.23399999737739563 | 4.051000118255615 | 48.83000183105469 | 1.2979999780654907 | 1.5740000009536743 | 0.7369999885559082 |
10.600000381469727 | 10.5 | 23.87339973449707 | 23.871200561523438 | 4.970799922943115 | 33.34199905395508 | 22.42449951171875 | 0.4300000071525574 | 4.078999996185303 | 46.130001068115234 | 1.2730000019073486 | 1.5800000429153442 | 0.7390000224113464 |
11.100000381469727 | 11.0 | 23.69849967956543 | 23.696199417114258 | 5.002900123596191 | 33.71649932861328 | 22.75909996032715 | 0.4580000042915344 | 4.10099983215332 | 42.939998626708984 | 1.2419999837875366 | 1.5839999914169312 | 0.7429999709129333 |
11.600000381469727 | 11.5 | 23.512699127197266 | 23.510299682617188 | 5.011600017547607 | 33.92399978637695 | 22.970300674438477 | 0.33799999952316284 | 4.086999893188477 | 39.31999969482422 | 1.2029999494552612 | 1.6130000352859497 | 0.7400000095367432 |
12.100000381469727 | 12.0 | 23.179399490356445 | 23.17690086364746 | 5.014599800109863 | 34.20389938354492 | 23.27899932861328 | 0.3050000071525574 | 4.086999893188477 | 36.119998931884766 | 1.1670000553131104 | 1.6670000553131104 | 0.7379999756813049 |
12.600000381469727 | 12.5 | 22.93000030517578 | 22.927400588989258 | 5.017099857330322 | 34.41749954223633 | 23.512699127197266 | 0.31200000643730164 | 4.079999923706055 | 34.72999954223633 | 1.149999976158142 | 1.7070000171661377 | 0.7279999852180481 |
13.100000381469727 | 13.0 | 22.71739959716797 | 22.714799880981445 | 5.029399871826172 | 34.68000030517578 | 23.7726993560791 | 0.3370000123977661 | 4.031000137329102 | 32.45000076293945 | 1.1200000047683716 | 1.7380000352859497 | 0.718999981880188 |
13.600000381469727 | 13.5 | 22.423599243164062 | 22.420900344848633 | 5.048099994659424 | 35.06050109863281 | 24.145200729370117 | 0.3319999873638153 | 4.0229997634887695 | 30.420000076293945 | 1.0920000076293945 | 1.815000057220459 | 0.7379999756813049 |
14.100000381469727 | 14.0 | 22.113000869750977 | 22.110200881958008 | 5.055200099945068 | 35.367801666259766 | 24.466400146484375 | 0.18000000715255737 | 4.039999961853027 | 29.90999984741211 | 1.0850000381469727 | 1.7660000324249268 | 0.7620000243186951 |
14.600000381469727 | 14.5 | 21.876399993896484 | 21.87350082397461 | 5.061200141906738 | 35.60850143432617 | 24.715700149536133 | 0.32199999690055847 | 3.999000072479248 | 28.809999465942383 | 1.0679999589920044 | 1.7450000047683716 | 0.7749999761581421 |
15.100000381469727 | 15.0 | 21.659500122070312 | 21.6564998626709 | 5.060299873352051 | 35.78070068359375 | 24.90730094909668 | 0.24500000476837158 | 4.043000221252441 | 26.530000686645508 | 1.0329999923706055 | 1.725000023841858 | 0.8069999814033508 |
15.600000381469727 | 15.5 | 21.5132999420166 | 21.510299682617188 | 5.059199810028076 | 35.89390182495117 | 25.034099578857422 | 0.1979999989271164 | 4.078999996185303 | 26.360000610351562 | 1.0299999713897705 | 1.684999942779541 | 0.8130000233650208 |
16.100000381469727 | 16.0 | 21.489299774169922 | 21.4862003326416 | 5.058700084686279 | 35.91019821166992 | 25.053199768066406 | 0.24400000274181366 | 4.065000057220459 | 24.0 | 0.9890000224113464 | 1.6690000295639038 | 0.7990000247955322 |
16.600000381469727 | 16.5 | 21.459999084472656 | 21.45680046081543 | 5.057600021362305 | 35.92559814453125 | 25.072999954223633 | 0.16099999845027924 | 4.019999980926514 | 23.18000030517578 | 0.9739999771118164 | 1.659000039100647 | 0.8109999895095825 |
17.100000381469727 | 17.0 | 21.42889976501465 | 21.425600051879883 | 5.056399822235107 | 35.941898345947266 | 25.094100952148438 | 0.1599999964237213 | 3.9590001106262207 | 22.09000015258789 | 0.953000009059906 | 1.6619999408721924 | 0.8349999785423279 |
17.600000381469727 | 17.5 | 21.422199249267578 | 21.418800354003906 | 5.056099891662598 | 35.945098876953125 | 25.098499298095703 | 0.1340000033378601 | 3.9200000762939453 | 20.600000381469727 | 0.9229999780654907 | 1.6679999828338623 | 0.8809999823570251 |
18.100000381469727 | 18.0 | 21.420799255371094 | 21.417299270629883 | 5.056099891662598 | 35.945899963378906 | 25.09939956665039 | 0.15600000321865082 | 3.9079999923706055 | 19.110000610351562 | 0.8899999856948853 | 1.6799999475479126 | 0.9269999861717224 |
18.600000381469727 | 18.5 | 21.416799545288086 | 21.41320037841797 | 5.0559000968933105 | 35.947200775146484 | 25.101600646972656 | 0.1809999942779541 | 3.872999906539917 | 17.68000030517578 | 0.8569999933242798 | 1.6399999856948853 | 0.9570000171661377 |
19.100000381469727 | 19.0 | 21.4153995513916 | 21.411699295043945 | 5.055500030517578 | 35.94540023803711 | 25.10059928894043 | 0.00800000037997961 | 3.864000082015991 | 18.139999389648438 | 0.8679999709129333 | 1.6540000438690186 | 0.9729999899864197 |
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.