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A public forum to talk about Quality Control of Argo measurements
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Is there an exhaustive list of situations where N_PROF > 1? #16

Open cgrdn opened 3 years ago

cgrdn commented 3 years ago

Is your QC expertise request related to a problem? Please describe. In a given cycle, the netCDF dimension N_PROF is typically 1 (1 cycle, 1 profile), but is sometimes greater than 1 (1 cycle, multiple profiles). In my experience, this is usually when there are observations recorded on the descent, and so there are two profiles for that cycle (files R[wmo]_[cycle].nc R[wmo]_[cycle]D.nc to give an example. There are some situations where there are even more than 2 profiles, and from the file I can't seem to discern from the file why that is (see next section for data). I also have not found an exhaustive lists in any Argo handbook, but hoping I just haven't been looking in the right place and someone could point me to a list or table.

Describe the Argo data you'd like to validate An example of such a file is here: https://data-argo.ifremer.fr/dac/coriolis/6901494/profiles/BD6901494_352.nc

Describe QC methods you've already considered Not so much a QC method, but this is a more general question related to this issue in the argoFloats R package: https://github.com/ArgoCanada/argoFloats/issues/413

Additional context Some python code looking at the netCDF file:

Click to unroll code

```python from netCDF4 import Dataset # get the file - not showing the local path on my machine, just assuming same directory nc = Dataset('BD6901494_352.nc') # show the dimension print(nc.dimensions['N_PROF']) # : name = 'N_PROF', size = 4 # variables in this file print(nc.variables.keys()) # dict_keys(['DATA_TYPE', 'FORMAT_VERSION', 'HANDBOOK_VERSION', # 'REFERENCE_DATE_TIME', 'DATE_CREATION', 'DATE_UPDATE', 'PLATFORM_NUMBER', # 'PROJECT_NAME', 'PI_NAME', 'STATION_PARAMETERS', 'CYCLE_NUMBER', 'DIRECTION', # 'DATA_CENTRE', 'DC_REFERENCE', 'DATA_STATE_INDICATOR', 'DATA_MODE', # 'PARAMETER_DATA_MODE', 'PLATFORM_TYPE', 'FLOAT_SERIAL_NO', 'FIRMWARE_VERSION', # 'WMO_INST_TYPE', 'JULD', 'JULD_QC', 'JULD_LOCATION', 'LATITUDE', 'LONGITUDE', # 'POSITION_QC', 'POSITIONING_SYSTEM', 'PROFILE_RAW_DOWNWELLING_IRRADIANCE380_QC', # 'PROFILE_RAW_DOWNWELLING_IRRADIANCE412_QC', # 'PROFILE_RAW_DOWNWELLING_IRRADIANCE490_QC', 'PROFILE_RAW_DOWNWELLING_PAR_QC', # 'PROFILE_DOWN_IRRADIANCE380_QC', 'PROFILE_DOWN_IRRADIANCE412_QC', # 'PROFILE_DOWN_IRRADIANCE490_QC', 'PROFILE_DOWNWELLING_PAR_QC', # 'PROFILE_FLUORESCENCE_CHLA_QC', 'PROFILE_BETA_BACKSCATTERING700_QC', # 'PROFILE_FLUORESCENCE_CDOM_QC', 'PROFILE_CHLA_QC', 'PROFILE_BBP700_QC', # 'PROFILE_CDOM_QC', 'VERTICAL_SAMPLING_SCHEME', 'CONFIG_MISSION_NUMBER', 'PRES', # 'TEMP_STD', 'PSAL_STD', 'PRES_MED', 'TEMP_MED', 'PSAL_MED', # 'RAW_DOWNWELLING_IRRADIANCE380', 'RAW_DOWNWELLING_IRRADIANCE380_QC', # 'RAW_DOWNWELLING_IRRADIANCE412', 'RAW_DOWNWELLING_IRRADIANCE412_QC', # 'RAW_DOWNWELLING_IRRADIANCE490', 'RAW_DOWNWELLING_IRRADIANCE490_QC', # 'RAW_DOWNWELLING_PAR', 'RAW_DOWNWELLING_PAR_QC', # 'RAW_DOWNWELLING_IRRADIANCE380_STD', 'RAW_DOWNWELLING_IRRADIANCE412_STD', # 'RAW_DOWNWELLING_IRRADIANCE490_STD', 'RAW_DOWNWELLING_PAR_STD', # 'RAW_DOWNWELLING_IRRADIANCE380_MED', 'RAW_DOWNWELLING_IRRADIANCE412_MED', # 'RAW_DOWNWELLING_IRRADIANCE490_MED', 'RAW_DOWNWELLING_PAR_MED', # 'DOWN_IRRADIANCE380', 'DOWN_IRRADIANCE380_QC', 'DOWN_IRRADIANCE380_ADJUSTED', # 'DOWN_IRRADIANCE380_ADJUSTED_QC', 'DOWN_IRRADIANCE380_ADJUSTED_ERROR', # 'DOWN_IRRADIANCE412', 'DOWN_IRRADIANCE412_QC', 'DOWN_IRRADIANCE412_ADJUSTED', # 'DOWN_IRRADIANCE412_ADJUSTED_QC', 'DOWN_IRRADIANCE412_ADJUSTED_ERROR', # 'DOWN_IRRADIANCE490', 'DOWN_IRRADIANCE490_QC', 'DOWN_IRRADIANCE490_ADJUSTED', # 'DOWN_IRRADIANCE490_ADJUSTED_QC', 'DOWN_IRRADIANCE490_ADJUSTED_ERROR', # 'DOWNWELLING_PAR', 'DOWNWELLING_PAR_QC', 'DOWNWELLING_PAR_ADJUSTED', # 'DOWNWELLING_PAR_ADJUSTED_QC', 'DOWNWELLING_PAR_ADJUSTED_ERROR', # 'FLUORESCENCE_CHLA', 'FLUORESCENCE_CHLA_QC', 'BETA_BACKSCATTERING700', # 'BETA_BACKSCATTERING700_QC', 'FLUORESCENCE_CDOM', 'FLUORESCENCE_CDOM_QC', # 'FLUORESCENCE_CHLA_STD', 'BETA_BACKSCATTERING700_STD', 'FLUORESCENCE_CDOM_STD', # 'FLUORESCENCE_CHLA_MED', 'BETA_BACKSCATTERING700_MED', 'FLUORESCENCE_CDOM_MED', # 'CHLA', 'CHLA_QC', 'CHLA_ADJUSTED', 'CHLA_ADJUSTED_QC', 'CHLA_ADJUSTED_ERROR', # 'BBP700', 'BBP700_QC', 'BBP700_ADJUSTED', 'BBP700_ADJUSTED_QC', # 'BBP700_ADJUSTED_ERROR', 'CDOM', 'CDOM_QC', 'CDOM_ADJUSTED', 'CDOM_ADJUSTED_QC', # 'CDOM_ADJUSTED_ERROR', 'HISTORY_INSTITUTION', 'HISTORY_STEP', 'HISTORY_SOFTWARE', # 'HISTORY_SOFTWARE_RELEASE', 'HISTORY_REFERENCE', 'HISTORY_DATE', 'HISTORY_ACTION', # 'HISTORY_PARAMETER', 'HISTORY_START_PRES', 'HISTORY_STOP_PRES', # 'HISTORY_PREVIOUS_VALUE', 'HISTORY_QCTEST', 'PARAMETER', # 'SCIENTIFIC_CALIB_EQUATION', 'SCIENTIFIC_CALIB_COEFFICIENT', # 'SCIENTIFIC_CALIB_COMMENT', 'SCIENTIFIC_CALIB_DATE']) # pressures for each profile for i in range(nc.dimensions['N_PROF'].size): print(nc['PRES'][:].data[i,:]) # [2.1000e+00 2.6000e+00 3.5000e+00 4.6000e+00 5.4000e+00 6.5000e+00 # 7.5000e+00 8.6000e+00 9.7000e+00 1.0700e+01 1.1500e+01 1.2400e+01 # 1.3500e+01 1.4600e+01 1.5600e+01 1.6700e+01 1.7500e+01 1.8300e+01 # 1.9400e+01 2.0600e+01 2.1600e+01 2.2600e+01 2.3400e+01 2.4400e+01 # 2.5700e+01 2.6400e+01 2.7300e+01 2.8300e+01 2.9400e+01 3.0600e+01 # 3.1700e+01 3.2600e+01 3.3300e+01 3.4300e+01 3.5600e+01 3.6700e+01 # 3.7700e+01 3.8400e+01 3.9400e+01 4.0700e+01 4.1700e+01 4.2600e+01 # 4.3400e+01 4.4500e+01 4.5600e+01 4.6600e+01 4.7300e+01 4.8300e+01 # 4.9600e+01 5.0600e+01 5.1600e+01 5.2600e+01 5.3600e+01 5.4300e+01 # 5.5700e+01 5.6500e+01 5.7500e+01 5.8700e+01 5.9600e+01 6.0300e+01 # 6.1400e+01 6.2700e+01 6.3600e+01 6.4300e+01 6.5500e+01 6.6600e+01 # 6.7300e+01 6.8300e+01 6.9500e+01 7.0500e+01 7.1400e+01 7.2500e+01 # 7.3500e+01 7.4600e+01 7.5700e+01 7.6500e+01 7.7300e+01 7.8300e+01 # 7.9500e+01 8.0500e+01 8.1700e+01 8.2700e+01 8.3700e+01 8.4700e+01 # 8.5700e+01 8.6500e+01 8.7300e+01 8.8400e+01 8.9500e+01 9.0700e+01 # 9.1500e+01 9.2300e+01 9.3500e+01 9.4600e+01 9.5500e+01 9.6300e+01 # 9.7400e+01 9.8500e+01 9.9400e+01 1.0030e+02 1.0130e+02 1.0250e+02 # 1.0340e+02 1.0430e+02 1.0560e+02 1.0650e+02 1.0740e+02 1.0860e+02 # 1.0940e+02 1.1040e+02 1.1160e+02 1.1240e+02 1.1330e+02 1.1460e+02 # 1.1570e+02 1.1660e+02 1.1740e+02 1.1840e+02 1.1950e+02 1.2060e+02 # 1.2160e+02 1.2270e+02 1.2350e+02 1.2440e+02 1.2530e+02 1.2650e+02 # 1.2760e+02 1.2830e+02 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04] # [6.0000e-01 1.5000e+00 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04] # [5.0000e-01 1.5000e+00 2.2000e+00 2.9000e+00 3.7000e+00 4.4000e+00 # 5.1000e+00 6.0000e+00 6.8000e+00 7.6000e+00 8.2000e+00 8.9000e+00 # 9.6000e+00 1.0300e+01 1.1000e+01 1.1800e+01 1.2600e+01 1.3600e+01 # 1.4500e+01 1.5400e+01 1.6300e+01 1.7100e+01 1.8000e+01 1.9000e+01 # 2.0000e+01 2.1100e+01 2.2300e+01 2.3400e+01 2.4500e+01 2.5700e+01 # 2.6900e+01 2.8000e+01 2.9200e+01 3.0300e+01 3.1500e+01 3.2600e+01 # 3.3800e+01 3.4900e+01 3.6100e+01 3.7200e+01 3.8400e+01 3.9500e+01 # 4.0700e+01 4.1900e+01 4.3100e+01 4.4200e+01 4.5400e+01 4.6600e+01 # 4.7700e+01 4.8900e+01 5.0100e+01 5.1200e+01 5.2300e+01 5.3400e+01 # 5.4500e+01 5.5600e+01 5.6700e+01 5.7800e+01 5.8900e+01 6.0000e+01 # 6.1100e+01 6.2200e+01 6.3200e+01 6.4300e+01 6.5300e+01 6.6500e+01 # 6.7600e+01 6.8700e+01 6.9900e+01 7.0900e+01 7.1800e+01 7.2500e+01 # 7.3100e+01 7.3800e+01 7.4400e+01 7.5100e+01 7.5800e+01 7.6500e+01 # 7.7200e+01 7.7900e+01 7.8600e+01 7.9300e+01 8.0100e+01 8.0800e+01 # 8.1600e+01 8.2500e+01 8.3300e+01 8.4100e+01 8.5000e+01 8.5800e+01 # 8.6700e+01 8.7600e+01 8.8500e+01 8.9400e+01 9.0200e+01 9.1100e+01 # 9.2100e+01 9.3000e+01 9.3900e+01 9.4900e+01 9.5900e+01 9.6900e+01 # 9.7900e+01 9.8800e+01 9.9800e+01 1.0080e+02 1.0180e+02 1.0280e+02 # 1.0380e+02 1.0490e+02 1.0590e+02 1.0690e+02 1.0790e+02 1.0900e+02 # 1.1000e+02 1.1110e+02 1.1220e+02 1.1320e+02 1.1440e+02 1.1540e+02 # 1.1660e+02 1.1770e+02 1.1890e+02 1.1990e+02 1.2110e+02 1.2220e+02 # 1.2330e+02 1.2440e+02 1.2540e+02 1.2630e+02 1.2700e+02 1.2740e+02 # 1.2750e+02 1.2760e+02 1.2770e+02 1.2780e+02 1.2790e+02 1.2790e+02 # 1.2800e+02 1.2810e+02 1.2820e+02 1.2820e+02 1.2820e+02 1.2830e+02 # 1.2830e+02 1.2830e+02 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 9.9999e+04 # 9.9999e+04 9.9999e+04 9.9999e+04] # [ 0.5 1.1 1.2 1.3 1.5 1.6 1.9 2.1 2.4 2.6 2.8 3.1 # 3.2 3.5 3.7 3.8 4. 4. 4.2 4.4 4.9 5.3 5.4 5.7 # 5.9 6.2 6.3 6.6 6.8 7. 7.1 7.3 7.7 8. 8. 8.2 # 8.3 8.5 8.8 8.9 9. 9.2 9.3 9.4 10.1 10.8 11.6 12.5 # 13.4 14.4 15.2 16.1 16.9 17.8 18.8 19.8 20.9 22. 23.2 24.3 # 25.5 26.6 27.7 28.9 30. 31.2 32.4 33.5 34.7 35.8 37. 38.2 # 39.3 40.5 41.7 42.8 44. 45.2 46.3 47.5 48.7 49.8 51. 52. # 53.2 54.2 55.3 56.5 57.6 58.7 59.8 60.9 61.9 63. 64. 65.1 # 66.2 67.3 68.5 69.6 70.7 71.6 72.4 73. 73.7 74.3 75. 75.7 # 76.3 77.1 77.8 78.4 79.2 79.9 80.7 81.5 82.3 83.1 84. 84.8 # 85.6 86.5 87.4 88.3 89.2 90.1 90.9 91.9 92.8 93.8 94.7 95.7 # 96.7 97.7 98.6 99.7 100.6 101.7 102.6 103.6 104.7 105.7 106.7 107.7 # 108.8 109.8 110.9 111.9 113. 114.1 115.2 116.4 117.5 118.6 119.7 120.8 # 122. 123.1 124.2 125.2 126.1 126.9 127.3 127.5 127.6 127.7 127.8 127.8 # 127.9 128. 128.1 128.1 128.2 128.2 128.2 128.3 128.3] # for all N_PROF except the last one, biological variables (CHLA, BBP, CDOM) are all fillvalues for i in range(nc.dimensions['N_PROF'].size): print(nc['CHLA'][:].data[i,:]) # Out: # [99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999.] # [99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999.] # [99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. 99999. # 99999. 99999. 99999. 99999. 99999. 99999. 99999.] # [-0.1533 0.073 0.365 0.1898 0.4745 0.5256 0.2555 0.1679 0.2117 # -0.2555 -0.0657 -0.0146 0.3723 0.0876 0.0219 0.5183 0.2701 -0.0438 # 0.2774 0.2847 -0.1825 -0.0657 0.0584 -0.0949 0.2044 0.0584 -0.2409 # 0.1898 -0.0511 0.1752 0.4088 0.146 0.6132 0.3431 0.5548 0.0438 # 0.2701 -0.1606 0.5183 0.292 0.6497 0.0584 0.9052 -0.3504 0.1679 # 0.1679 0.1971 0.1971 0.1971 0.219 0.1898 0.219 0.2263 0.2482 # 0.2555 0.2263 0.1971 0.1825 0.1752 0.1898 0.2117 0.1971 0.2117 # 0.219 0.2263 0.2336 0.2628 0.2701 0.2774 0.2774 0.2628 0.2701 # 0.292 0.3066 0.3066 0.3139 0.3358 0.3504 0.3431 0.4161 0.4307 # 0.4818 0.4818 0.4891 0.5037 0.5256 0.5913 0.6862 0.6716 0.7008 # 0.7665 0.6862 0.6643 0.6351 0.5986 0.511 0.5402 0.4891 0.4818 # 0.438 0.5037 0.4453 0.4453 0.4307 0.4672 0.4161 0.4088 0.4599 # 0.4088 0.4015 0.4088 0.4234 0.4307 0.4234 0.4088 0.4453 0.4453 # 0.4599 0.438 0.4891 0.4234 0.4453 0.4307 0.4161 0.4307 0.3942 # 0.3869 0.3796 0.4161 0.4015 0.3796 0.3577 0.3139 0.3139 0.3285 # 0.3066 0.3066 0.3139 0.3066 0.3139 0.3066 0.3139 0.2993 0.2774 # 0.2774 0.2482 0.2482 0.2409 0.219 0.219 0.2044 0.1825 0.1752 # 0.2336 0.2117 0.1898 0.1606 0.1679 0.1606 0.1533 0.146 0.1533 # 0.1533 0.146 0.1679 0.1606 0.1606 0.1387 0.1679 0.1533 0.1533 # 0.146 0.1752 0.1606 0.1825 0.146 0.1606]

cgrdn commented 3 years ago

The more specific part of this question related to why N_PROF = 4 in this particular file, I can find in 'VERTICAL_SAMPLING_SCHEME', however the core question looking for a list of situations where this occurs is what I am looking for.

For reference, output of vertical sampling scheme variable:

for i in range(nc.dimensions['N_PROF'].size):
    # read_ncstr is my own function that makes netcdf string arrays more readable
    print(read_ncstr(nc['VERTICAL_SAMPLING_SCHEME'][:].data[i,:]))
# Out:
# Primary sampling: averaged [2s sampling,1dbar average in [100-10]dbar;2s samp.,1dbar avg in [10-2.1]dbar]
# Near-surface sampling: averaged, unpumped [2s samp.,1dbar avg in [2.1-1]dbar;2s samp.,1dbar avg in [1dbar-surface]]
# Secondary sampling: mixed [10s sampling in [100-10]dbar;2s samp. in [10-1]dbar;10s samp.,1dbar avg in [1dbar-surface]]
# Secondary sampling: mixed [10s sampling in [100-10]dbar;2s samp. in [10-1]dbar;10s samp.,1dbar avg in [1dbar-surface]]
cabanesc commented 3 years ago

N_PROF>1 will occur when: 1) multiple sensors operate at the same time or/and a single sensor uses different sampling scheme (e.g Near-surface sampling, with CTD unpumped) 2) additional profiles are collected each cycle (e.g Bouncing profiles: a series of shallow profiles performed during one cycle from some specialty floats)

If a descending and ascending profiles are collected during the same cycle, these profiles are in two separate files ( R[wmo][cycle].nc and R[wmo][cycle]D.nc , with 'D' for the descending profile), In this case, in each file, N_PROF=1, of course if there are no multiple sensors/sampling schemes or other "bouncing" profiles.

I don't know if this answers your question... Here is an example (a float carring multiple sensors) that you can find in the Argo user manual (section 2.6.1):

"Suppose a hypothetical float carries a high-resolution CTD sensor and a low-resolution nitrate sensor. In each single-cycle, this hypothetical float is configured to return a 2-dbar bin-averaged CTD profile to 1000 dbar with no corresponding nitrate measurements, and a discrete 250-dbar interval nitrate profile to 1000 dbar with no corresponding temperature and salinity measurements. The parameters in the resulting core-Argo and b-Argo profile files are formatted as follows for the two profiles in each cycle: In the core-Argo profile file, N_PROF = 2, N_LEVELS = 500. PRES= [2, 4, 6, .......................................1000]; = [250, 500, 750, 1000, FillValue, ............]. TEMP= [T2, T4, T6, ............................T1000]; = [FillValue, ......................................]. PSAL= [S2, S4, S6, .............................S1000]; = [FillValue, ......................................]. In the b-Argo profile file, N_PROF = 2, N_LEVELS = 500. PRES= [2, 4, 6, .......................................1000]; = [250, 500, 750, 1000, FillValue, .............]. NITRATE= [FillValue, ......................................]; = [N250, N500, N750, N1000, FillValue, ...]."