Seawater conductivity refers to the ability of seawater to conduct electricity. The presence of ions in the seawater, such as salt, increases the electrical conducting ability of seawater. As such, conductivity can be used as a proxy for determining the quantity of salt in a sample of seawater. This is the unprocessed data that are output directly from the sensor which are then converted to salinity in S m-1.
Seawater conductivity refers to the ability of seawater to conduct electricity. The presence of ions in the seawater, such as salt, increases the electrical conducting ability of seawater. As such, conductivity can be used as a proxy for determining the quantity of salt in a sample of seawater.
coordinates :
time lat lon depth
data_product_identifier :
CONDWAT_L1
long_name :
Seawater Conductivity
precision :
6
standard_name :
sea_water_electrical_conductivity
units :
S m-1
Array
Chunk
Bytes
10.10 MB
644.29 kB
Shape
(1261965,)
(80536,)
Count
17 Tasks
16 Chunks
Type
float64
numpy.ndarray
ctdbp_seawater_conductivity_qartod_results
(time)
uint8
dask.array<chunksize=(80536,), meta=np.ndarray>
comment :
Summary QARTOD test flags. For each datum, the flag is set to the most significant result of all QARTOD tests run for that datum.
Seawater Pressure refers to the pressure exerted on a sensor in situ by the weight of the column of seawater above it. It is calculated by subtracting one standard atmosphere from the absolute pressure at the sensor to remove the weight of the atmosphere on top of the water column. The pressure at a sensor in situ provides a metric of the depth of that sensor.
coordinates :
time lat lon depth
data_product_identifier :
PRESWAT_L1
long_name :
Seawater Pressure
precision :
3
standard_name :
sea_water_pressure
units :
dbar
Array
Chunk
Bytes
10.10 MB
644.29 kB
Shape
(1261965,)
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Count
17 Tasks
16 Chunks
Type
float64
numpy.ndarray
ctdbp_seawater_pressure_qartod_results
(time)
uint8
dask.array<chunksize=(80536,), meta=np.ndarray>
comment :
Summary QARTOD test flags. For each datum, the flag is set to the most significant result of all QARTOD tests run for that datum.
Seawater Density is defined as mass per unit volume and is calculated from the conductivity, temperature and depth of a seawater sample using the TEOS-10 equation.
coordinates :
time lat lon depth
data_product_identifier :
DENSITY_L2
long_name :
Seawater Density
precision :
3
standard_name :
sea_water_density
units :
kg m-3
Array
Chunk
Bytes
10.10 MB
644.29 kB
Shape
(1261965,)
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17 Tasks
16 Chunks
Type
float64
numpy.ndarray
density_qc_executed
(time)
uint8
dask.array<chunksize=(80536,), meta=np.ndarray>
coordinates :
time lat lon depth
long_name :
QC Checks Executed
Array
Chunk
Bytes
1.26 MB
80.54 kB
Shape
(1261965,)
(80536,)
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17 Tasks
16 Chunks
Type
uint8
numpy.ndarray
density_qc_results
(time)
uint8
dask.array<chunksize=(80536,), meta=np.ndarray>
coordinates :
time lat lon depth
long_name :
QC Checks Results
Array
Chunk
Bytes
1.26 MB
80.54 kB
Shape
(1261965,)
(80536,)
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17 Tasks
16 Chunks
Type
uint8
numpy.ndarray
deployment
(time)
int32
dask.array<chunksize=(80536,), meta=np.ndarray>
long_name :
Deployment Number
name :
deployment
Array
Chunk
Bytes
5.05 MB
322.14 kB
Shape
(1261965,)
(80536,)
Count
17 Tasks
16 Chunks
Type
int32
numpy.ndarray
depth
(time)
float64
dask.array<chunksize=(80536,), meta=np.ndarray>
ancillary_variables :
ctdbp_seawater_pressure
axis :
Z
comment :
Depth (m) calculated from pressure (dbar) and latitude.
long_name :
Depth calculated from pressure
precision :
3
units :
m
Array
Chunk
Bytes
10.10 MB
644.29 kB
Shape
(1261965,)
(80536,)
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17 Tasks
16 Chunks
Type
float64
numpy.ndarray
driver_timestamp
(time)
float64
dask.array<chunksize=(80536,), meta=np.ndarray>
comment :
Driver timestamp, UTC
long_name :
Driver Timestamp, UTC
units :
seconds since 1900-01-01
Array
Chunk
Bytes
10.10 MB
644.29 kB
Shape
(1261965,)
(80536,)
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17 Tasks
16 Chunks
Type
float64
numpy.ndarray
ingestion_timestamp
(time)
float64
dask.array<chunksize=(80536,), meta=np.ndarray>
comment :
The NTP Timestamp for when the granule was ingested
long_name :
Ingestion Timestamp, UTC
units :
seconds since 1900-01-01
Array
Chunk
Bytes
10.10 MB
644.29 kB
Shape
(1261965,)
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Type
float64
numpy.ndarray
internal_timestamp
(time)
float64
dask.array<chunksize=(80536,), meta=np.ndarray>
comment :
Internal timestamp, UTC
long_name :
Internal Timestamp, UTC
units :
seconds since 1900-01-01
Array
Chunk
Bytes
10.10 MB
644.29 kB
Shape
(1261965,)
(80536,)
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17 Tasks
16 Chunks
Type
float64
numpy.ndarray
port_timestamp
(time)
float64
dask.array<chunksize=(80536,), meta=np.ndarray>
comment :
Port timestamp, UTC
long_name :
Port Timestamp, UTC
units :
seconds since 1900-01-01
Array
Chunk
Bytes
10.10 MB
644.29 kB
Shape
(1261965,)
(80536,)
Count
17 Tasks
16 Chunks
Type
float64
numpy.ndarray
practical_salinity
(time)
float64
dask.array<chunksize=(80536,), meta=np.ndarray>
ancillary_variables :
practical_salinity_qartod_results practical_salinity_qartod_executed pressure conductivity temperature
comment :
Salinity is generally defined as the concentration of dissolved salt in a parcel of seawater. Practical Salinity is a more specific unitless quantity calculated from the conductivity of seawater and adjusted for temperature and pressure. It is approximately equivalent to Absolute Salinity (the mass fraction of dissolved salt in seawater) but they are not interchangeable.
coordinates :
time lat lon depth
data_product_identifier :
PRACSAL_L2
long_name :
Practical Salinity
precision :
4
standard_name :
sea_water_practical_salinity
units :
1
Array
Chunk
Bytes
10.10 MB
644.29 kB
Shape
(1261965,)
(80536,)
Count
17 Tasks
16 Chunks
Type
float64
numpy.ndarray
practical_salinity_qartod_results
(time)
uint8
dask.array<chunksize=(80536,), meta=np.ndarray>
comment :
Summary QARTOD test flags. For each datum, the flag is set to the most significant result of all QARTOD tests run for that datum.
Seawater Pressure refers to the pressure exerted on a sensor in situ by the weight of the column of seawater above it. It is calculated by subtracting one standard atmosphere from the absolute pressure at the sensor to remove the weight of the atmosphere on top of the water column. The pressure at a sensor in situ provides a metric of the depth of that sensor. This is the unprocessed data that are output directly from the sensor which are then converted to pressure in dbar.
coordinates :
time lat lon depth
data_product_identifier :
PRESWAT_L0
long_name :
Seawater Pressure Measurement
precision :
0
units :
counts
Array
Chunk
Bytes
10.10 MB
644.29 kB
Shape
(1261965,)
(80536,)
Count
17 Tasks
16 Chunks
Type
float64
numpy.ndarray
pressure_qc_executed
(time)
uint8
dask.array<chunksize=(80536,), meta=np.ndarray>
coordinates :
time lat lon depth
long_name :
QC Checks Executed
Array
Chunk
Bytes
1.26 MB
80.54 kB
Shape
(1261965,)
(80536,)
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17 Tasks
16 Chunks
Type
uint8
numpy.ndarray
pressure_qc_results
(time)
uint8
dask.array<chunksize=(80536,), meta=np.ndarray>
coordinates :
time lat lon depth
long_name :
QC Checks Results
Array
Chunk
Bytes
1.26 MB
80.54 kB
Shape
(1261965,)
(80536,)
Count
17 Tasks
16 Chunks
Type
uint8
numpy.ndarray
pressure_temp
(time)
float64
dask.array<chunksize=(80536,), meta=np.ndarray>
comment :
Seawater Pressure refers to the pressure exerted on a sensor in situ by the weight of the column of seawater above it. It is calculated by subtracting one standard atmosphere from the absolute pressure at the sensor to remove the weight of the atmosphere on top of the water column. The pressure at a sensor in situ provides a metric of the depth of that sensor. This is the unprocessed data that are output directly from the sensor which are then converted to pressure in dbar.
coordinates :
time lat lon depth
long_name :
Seawater Pressure Measurement
precision :
0
units :
counts
Array
Chunk
Bytes
10.10 MB
644.29 kB
Shape
(1261965,)
(80536,)
Count
17 Tasks
16 Chunks
Type
float64
numpy.ndarray
temperature
(time)
float64
dask.array<chunksize=(80536,), meta=np.ndarray>
comment :
Seawater temperature unprocessed measurement near the sensor.
coordinates :
time lat lon depth
data_product_identifier :
TEMPWAT_L0
long_name :
Seawater Temperature Measurement
precision :
0
units :
counts
Array
Chunk
Bytes
10.10 MB
644.29 kB
Shape
(1261965,)
(80536,)
Count
17 Tasks
16 Chunks
Type
float64
numpy.ndarray
Conventions :
CF-1.6
Metadata_Conventions :
Unidata Dataset Discovery v1.0
Notes :
This netCDF product is a copy of the data on the University of Washington AWS Cloud Infrastructure.
Owner :
University of Washington Cabled Array Value Add Team.
cdm_data_type :
Point
collection_method :
recovered_inst
comment :
Some of the metadata of this dataset has been modified to be CF-1.6 compliant.
creator_name :
Ocean Observatories Initiative
creator_url :
http://oceanobservatories.org/
date_created :
2020-11-24T06:46:02.872456
date_downloaded :
2020-11-24T06:45:49.426527
date_modified :
2020-11-24T06:46:02.872463
date_processed :
2020-11-24T06:50:56.160304
featureType :
point
geospatial_lat_max :
46.85385
geospatial_lat_min :
46.85385
geospatial_lat_resolution :
0.1
geospatial_lat_units :
degrees_north
geospatial_lon_max :
-124.95838
geospatial_lon_min :
-124.95838
geospatial_lon_resolution :
0.1
geospatial_lon_units :
degrees_east
geospatial_vertical_positive :
down
geospatial_vertical_resolution :
0.1
geospatial_vertical_units :
meters
history :
2020-11-24T06:46:02.872368 generated from Stream Engine