diff --git a/aodn_cloud_optimised/bin/radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc.py b/aodn_cloud_optimised/bin/radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc.py index 1722275..3050eb8 100755 --- a/aodn_cloud_optimised/bin/radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc.py +++ b/aodn_cloud_optimised/bin/radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc.py @@ -9,8 +9,7 @@ def main(): - # for i, year in enumerate(range(2007, 2025)): - for i, year in enumerate(range(2017, 2025)): + for i, year in enumerate(range(2007, 2025)): command = [ "generic_cloud_optimised_creation", "--paths", @@ -24,8 +23,8 @@ def main(): ] # Add --clear-existing-data for the first iteration only - # if i == 0: - # command.append("--clear-existing-data") + if i == 0: + command.append("--clear-existing-data") # Run the command subprocess.run(command, check=True) diff --git a/aodn_cloud_optimised/config/dataset/radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc.json b/aodn_cloud_optimised/config/dataset/radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc.json index d07e5df..be96aa9 100644 --- a/aodn_cloud_optimised/config/dataset/radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc.json +++ b/aodn_cloud_optimised/config/dataset/radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc.json @@ -1,5 +1,5 @@ { - "dataset_name": "radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc", + "dataset_name": "radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc_test", "logger_name": "radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc", "parent_config": "radar_velocity_hourly_averaged_delayed_qc_no_I_J_version_main.json", "metadata_uuid": "cb2e22b5-ebb9-460b-8cff-b446fe14ea2f", diff --git a/notebooks/radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc.ipynb b/notebooks/radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc.ipynb index 0d52a01..79f4758 100644 --- a/notebooks/radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc.ipynb +++ b/notebooks/radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc.ipynb @@ -60,7 +60,7 @@ "Using CPython 3.12.6 interpreter at: \u001b[36m/home/lbesnard/miniforge3/envs/AodnCloudOptimised/bin/python\u001b[39m\n", "Creating virtual environment at: \u001b[36m.venv\u001b[39m\n", "Activate with: \u001b[32msource .venv/bin/activate\u001b[39m\n", - "\u001b[2mAudited \u001b[1m232 packages\u001b[0m \u001b[2min 29ms\u001b[0m\u001b[0m\n" + "\u001b[2mAudited \u001b[1m234 packages\u001b[0m \u001b[2min 27ms\u001b[0m\u001b[0m\n" ] } ], @@ -503,37 +503,37 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
<xarray.Dataset> Size: 14GB\n", - "Dimensions: (TIME: 25027, LATITUDE: 74, LONGITUDE: 102)\n", + "<xarray.Dataset> Size: 41GB\n", + "Dimensions: (TIME: 74523, LATITUDE: 74, LONGITUDE: 102)\n", "Coordinates:\n", " * LATITUDE (LATITUDE) float64 592B -37.46 -37.42 ... -34.82\n", " * LONGITUDE (LONGITUDE) float64 816B 133.0 133.0 ... 137.4 137.5\n", - " * TIME (TIME) datetime64[ns] 200kB 2009-11-23T17:29:59.999...\n", + " * TIME (TIME) datetime64[ns] 596kB 2009-11-23T17:29:59.999...\n", "Data variables:\n", - " GDOP (TIME, LATITUDE, LONGITUDE) float64 2GB dask.array<chunksize=(100, 74, 102), meta=np.ndarray>\n", - " NOBS1 (TIME, LATITUDE, LONGITUDE) float64 2GB dask.array<chunksize=(100, 74, 102), meta=np.ndarray>\n", - " NOBS2 (TIME, LATITUDE, LONGITUDE) float64 2GB dask.array<chunksize=(100, 74, 102), meta=np.ndarray>\n", - " UCUR (TIME, LATITUDE, LONGITUDE) float64 2GB dask.array<chunksize=(100, 74, 102), meta=np.ndarray>\n", - " UCUR_quality_control (TIME, LATITUDE, LONGITUDE) float64 2GB dask.array<chunksize=(100, 74, 102), meta=np.ndarray>\n", - " UCUR_sd (TIME, LATITUDE, LONGITUDE) float64 2GB dask.array<chunksize=(100, 74, 102), meta=np.ndarray>\n", - " VCUR (TIME, LATITUDE, LONGITUDE) float64 2GB dask.array<chunksize=(100, 74, 102), meta=np.ndarray>\n", - " VCUR_quality_control (TIME, LATITUDE, LONGITUDE) float64 2GB dask.array<chunksize=(100, 74, 102), meta=np.ndarray>\n", - " VCUR_sd (TIME, LATITUDE, LONGITUDE) float64 2GB dask.array<chunksize=(100, 74, 102), meta=np.ndarray>\n", - " filename (TIME) <U53 5MB dask.array<chunksize=(100,), meta=np.ndarray>\n", + " GDOP (TIME, LATITUDE, LONGITUDE) float64 4GB dask.array<chunksize=(100, 74, 102), meta=np.ndarray>\n", + " NOBS1 (TIME, LATITUDE, LONGITUDE) float64 4GB dask.array<chunksize=(100, 74, 102), meta=np.ndarray>\n", + " NOBS2 (TIME, LATITUDE, LONGITUDE) float64 4GB dask.array<chunksize=(100, 74, 102), meta=np.ndarray>\n", + " UCUR (TIME, LATITUDE, LONGITUDE) float64 4GB dask.array<chunksize=(100, 74, 102), meta=np.ndarray>\n", + " UCUR_quality_control (TIME, LATITUDE, LONGITUDE) float64 4GB dask.array<chunksize=(100, 74, 102), meta=np.ndarray>\n", + " UCUR_sd (TIME, LATITUDE, LONGITUDE) float64 4GB dask.array<chunksize=(100, 74, 102), meta=np.ndarray>\n", + " VCUR (TIME, LATITUDE, LONGITUDE) float64 4GB dask.array<chunksize=(100, 74, 102), meta=np.ndarray>\n", + " VCUR_quality_control (TIME, LATITUDE, LONGITUDE) float64 4GB dask.array<chunksize=(100, 74, 102), meta=np.ndarray>\n", + " VCUR_sd (TIME, LATITUDE, LONGITUDE) float64 4GB dask.array<chunksize=(100, 74, 102), meta=np.ndarray>\n", + " filename (TIME) <U53 16MB dask.array<chunksize=(100,), meta=np.ndarray>\n", "Attributes: (12/40)\n", " Conventions: CF-1.6,IMOS-1.4\n", - " abstract: The ACORN facility is producing NetCDF fil...\n", + " abstract: The IMOS Ocean Radar Facility (previously ...\n", " acknowledgement: Any users (including re-packagers) of IMOS...\n", - " author: Cosoli, Simone\n", - " author_email: simone.cosoli@uwa.edu.au\n", + " author: Cosoli, Simone; Hetzel, Yasha\n", + " author_email: simone.cosoli@uwa.edu.au; yasha.hetzel@uwa...\n", " citation: The citation in a list of references is: I...\n", " ... ...\n", " source: Terrestrial HF radar\n", " ssr_Stations: Cape Wiles (CWI), Cape Spencer (CSP)\n", " standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metadata ...\n", - " time_coverage_end: 2013-11-09T00:30:00Z\n", - " time_coverage_start: 2013-11-09T00:30:00Z\n", - " title: IMOS ACORN South Australian Gulf (SAG), on...xarray.Dataset
- TIME: 25027
- LATITUDE: 74
- LONGITUDE: 102
LATITUDE(LATITUDE)float64-37.46 -37.42 ... -34.86 -34.82
- axis :
- Y
- long_name :
- latitude
- reference_datum :
- geographical coordinates, WGS84 datum
- standard_name :
- latitude
- units :
- degrees_north
- valid_max :
- 90.0
- valid_min :
- -90.0
array([-37.455159, -37.419108, -37.383057, -37.347004, -37.310955, -37.274899,\n", + " time_coverage_end: 2024-01-01T00:30:00Z\n", + " time_coverage_start: 2024-01-01T00:30:00Z\n", + " title: IMOS Ocean Radar Facility South Australian...xarray.Dataset
- TIME: 74523
- LATITUDE: 74
- LONGITUDE: 102
LATITUDE(LATITUDE)float64-37.46 -37.42 ... -34.86 -34.82
- axis :
- Y
- long_name :
- latitude
- reference_datum :
- geographical coordinates, WGS84 datum
- standard_name :
- latitude
- units :
- degrees_north
- valid_max :
- 90.0
- valid_min :
- -90.0
array([-37.455159, -37.419108, -37.383057, -37.347004, -37.310955, -37.274899,\n", " -37.23885 , -37.202801, -37.166748, -37.130699, -37.094646, -37.058594,\n", " -37.022545, -36.986492, -36.950443, -36.914391, -36.878338, -36.842285,\n", " -36.806236, -36.770187, -36.734135, -36.698086, -36.662029, -36.62598 ,\n", @@ -545,7 +545,7 @@ " -35.5084 , -35.472343, -35.436295, -35.400242, -35.364193, -35.328144,\n", " -35.292088, -35.256039, -35.219986, -35.183937, -35.147888, -35.111835,\n", " -35.075783, -35.03973 , -35.003681, -34.967632, -34.93158 , -34.895527,\n", - " -34.859474, -34.823423]) LONGITUDE(LONGITUDE)float64133.0 133.0 133.0 ... 137.4 137.5
- axis :
- X
- long_name :
- longitude
- reference_datum :
- geographical coordinates, WGS84 datum
- standard_name :
- longitude
- units :
- degrees_east
- valid_max :
- 180.0
- valid_min :
- -180.0
array([132.953971, 132.998611, 133.043243, 133.087891, 133.132538, 133.177155,\n", + " -34.859474, -34.823423]) LONGITUDE(LONGITUDE)float64133.0 133.0 133.0 ... 137.4 137.5
- axis :
- X
- long_name :
- longitude
- reference_datum :
- geographical coordinates, WGS84 datum
- standard_name :
- longitude
- units :
- degrees_east
- valid_max :
- 180.0
- valid_min :
- -180.0
array([132.953971, 132.998611, 133.043243, 133.087891, 133.132538, 133.177155,\n", " 133.221802, 133.266449, 133.311081, 133.355728, 133.400375, 133.445007,\n", " 133.489655, 133.534302, 133.578918, 133.623566, 133.668213, 133.712845,\n", " 133.757492, 133.802139, 133.846771, 133.891418, 133.936066, 133.980698,\n", @@ -561,10 +561,10 @@ " 136.435928, 136.48056 , 136.525208, 136.569855, 136.614487, 136.659134,\n", " 136.703781, 136.748413, 136.79306 , 136.837692, 136.882339, 136.926971,\n", " 136.971619, 137.016266, 137.060898, 137.105545, 137.150192, 137.194824,\n", - " 137.239456, 137.284103, 137.328735, 137.373383, 137.418023, 137.462663]) TIME(TIME)datetime64[ns]2009-11-23T17:29:59.999996928 .....
- axis :
- T
- comment :
- Given time lays at the middle of the averaging time period.
- local_time_zone :
- 9.5
- long_name :
- time
- standard_name :
- time
- valid_max :
- 999999.0
- valid_min :
- 0.0
array(['2009-11-23T17:29:59.999996928', '2009-11-23T18:29:59.999993088',\n", - " '2009-11-23T19:30:00.000000000', ..., '2013-12-31T21:29:59.999993088',\n", - " '2013-12-31T22:30:00.000000000', '2013-12-31T23:29:59.999996928'],\n", - " dtype='datetime64[ns]')
GDOP(TIME, LATITUDE, LONGITUDE)float64dask.array<chunksize=(100, 74, 102), meta=np.ndarray>
- comment :
- This angle is used to assess the impact of Geometric Dilution of Precision. If angle >= 150 or <= 30, then QC flag will not be lower than 4 (see abstract).
- long_name :
- radar beam intersection angle
- units :
- Degrees
- valid_max :
- 180.0
- valid_min :
- 0.0
\n", + " 137.239456, 137.284103, 137.328735, 137.373383, 137.418023, 137.462663])
TIME(TIME)datetime64[ns]2009-11-23T17:29:59.999996928 .....
- axis :
- T
- comment :
- Given time lays at the middle of the averaging time period.
- local_time_zone :
- 9.5
- long_name :
- time
- standard_name :
- time
- valid_max :
- 999999.0
- valid_min :
- 0.0
array(['2009-11-23T17:29:59.999996928', '2009-11-23T18:29:59.999993088',\n", + " '2009-11-23T19:30:00.000000000', ..., '2024-02-20T04:30:00.000000000',\n", + " '2024-02-20T05:30:00.000007168', '2024-02-20T06:30:00.000003072'],\n", + " dtype='datetime64[ns]')
GDOP(TIME, LATITUDE, LONGITUDE)float64dask.array<chunksize=(100, 74, 102), meta=np.ndarray>
- comment :
- This angle is used to assess the impact of Geometric Dilution of Precision. If angle >= 150 or <= 30, then QC flag will not be lower than 4 (see abstract).
- long_name :
- radar beam intersection angle
- units :
- Degrees
- valid_max :
- 180.0
- valid_min :
- 0.0
\n", "
\n", " \n", " \n", @@ -579,18 +579,18 @@ " \n", "
\n", " \n", " \n", "Bytes \n", - "1.41 GiB \n", + "4.19 GiB \n", "5.76 MiB \n", "\n", " \n", "Shape \n", - "(25027, 74, 102) \n", + "(74523, 74, 102) \n", "(100, 74, 102) \n", "\n", " \n", "Dask graph \n", - "251 chunks in 2 graph layers \n", + "746 chunks in 2 graph layers \n", "\n", " \n", - "Data type \n", @@ -610,18 +610,18 @@ "\n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -635,18 +635,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -674,11 +674,11 @@ " \n", " 102 \n", "74 \n", - "25027 \n", + "74523 \n", "\n", " \n", " NOBS1(TIME, LATITUDE, LONGITUDE)float64dask.array<chunksize=(100, 74, 102), meta=np.ndarray>
- long_name :
- Number of observations of sea water velocity in 1 hour from station 1, after rejection of obvious bad data (see abstract).
- units :
- 1
\n", + "
NOBS1(TIME, LATITUDE, LONGITUDE)float64dask.array<chunksize=(100, 74, 102), meta=np.ndarray>
- long_name :
- Number of observations of sea water velocity in 1 hour from station 1, after rejection of obvious bad data (see abstract).
- units :
- 1
\n", "
\n", " \n", " \n", @@ -693,18 +693,18 @@ " \n", "
\n", " \n", " \n", "Bytes \n", - "1.41 GiB \n", + "4.19 GiB \n", "5.76 MiB \n", "\n", " \n", "Shape \n", - "(25027, 74, 102) \n", + "(74523, 74, 102) \n", "(100, 74, 102) \n", "\n", " \n", "Dask graph \n", - "251 chunks in 2 graph layers \n", + "746 chunks in 2 graph layers \n", "\n", " \n", - "Data type \n", @@ -724,18 +724,18 @@ "\n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -749,18 +749,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -788,11 +788,11 @@ " \n", " 102 \n", "74 \n", - "25027 \n", + "74523 \n", "\n", " \n", " NOBS2(TIME, LATITUDE, LONGITUDE)float64dask.array<chunksize=(100, 74, 102), meta=np.ndarray>
- long_name :
- Number of observations of sea water velocity in 1 hour from station 2, after rejection of obvious bad data (see abstract).
- units :
- 1
\n", + "
NOBS2(TIME, LATITUDE, LONGITUDE)float64dask.array<chunksize=(100, 74, 102), meta=np.ndarray>
- long_name :
- Number of observations of sea water velocity in 1 hour from station 2, after rejection of obvious bad data (see abstract).
- units :
- 1
\n", "
\n", " \n", " \n", @@ -807,18 +807,18 @@ " \n", "
\n", " \n", " \n", "Bytes \n", - "1.41 GiB \n", + "4.19 GiB \n", "5.76 MiB \n", "\n", " \n", "Shape \n", - "(25027, 74, 102) \n", + "(74523, 74, 102) \n", "(100, 74, 102) \n", "\n", " \n", "Dask graph \n", - "251 chunks in 2 graph layers \n", + "746 chunks in 2 graph layers \n", "\n", " \n", - "Data type \n", @@ -838,18 +838,18 @@ "\n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -863,18 +863,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -902,11 +902,11 @@ " \n", " 102 \n", "74 \n", - "25027 \n", + "74523 \n", "\n", " \n", " UCUR(TIME, LATITUDE, LONGITUDE)float64dask.array<chunksize=(100, 74, 102), meta=np.ndarray>
- ancillary_variables :
- UCUR_quality_control
- cell_methods :
- TIME: mean
- long_name :
- Mean of sea water velocity U component values in 1 hour, after rejection of obvious bad data (see abstract).
- standard_name :
- eastward_sea_water_velocity
- units :
- m s-1
- valid_max :
- 10.0
- valid_min :
- -10.0
\n", + "
UCUR(TIME, LATITUDE, LONGITUDE)float64dask.array<chunksize=(100, 74, 102), meta=np.ndarray>
- ancillary_variables :
- UCUR_quality_control
- cell_methods :
- TIME: mean
- long_name :
- Mean of sea water velocity U component values in 1 hour, after rejection of obvious bad data (see abstract).
- standard_name :
- eastward_sea_water_velocity
- units :
- m s-1
- valid_max :
- 10.0
- valid_min :
- -10.0
\n", "
\n", " \n", " \n", @@ -921,18 +921,18 @@ " \n", "
\n", " \n", " \n", "Bytes \n", - "1.41 GiB \n", + "4.19 GiB \n", "5.76 MiB \n", "\n", " \n", "Shape \n", - "(25027, 74, 102) \n", + "(74523, 74, 102) \n", "(100, 74, 102) \n", "\n", " \n", "Dask graph \n", - "251 chunks in 2 graph layers \n", + "746 chunks in 2 graph layers \n", "\n", " \n", - "Data type \n", @@ -952,18 +952,18 @@ "\n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -977,18 +977,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1016,11 +1016,11 @@ " \n", " 102 \n", "74 \n", - "25027 \n", + "74523 \n", "\n", " \n", " UCUR_quality_control(TIME, LATITUDE, LONGITUDE)float64dask.array<chunksize=(100, 74, 102), meta=np.ndarray>
- comment :
- This value is set on the basis of the offline quality controls applied in the time domain (see abstract).
- flag_meanings :
- no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed not_used not_used interpolated_values missing_values
- flag_values :
- [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
- long_name :
- quality flag for eastward_sea_water_velocity
- quality_control_conventions :
- IMOS standard flags
- quality_control_set :
- 1.0
- standard_name :
- eastward_sea_water_velocity status_flag
- valid_max :
- 9
- valid_min :
- 0
\n", + "
UCUR_quality_control(TIME, LATITUDE, LONGITUDE)float64dask.array<chunksize=(100, 74, 102), meta=np.ndarray>
- comment :
- This value is set on the basis of the offline quality controls applied in the time domain (see abstract).
- flag_meanings :
- no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed not_used not_used interpolated_values missing_values
- flag_values :
- [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
- long_name :
- quality flag for eastward_sea_water_velocity
- quality_control_conventions :
- IMOS standard flags
- quality_control_set :
- 1.0
- standard_name :
- eastward_sea_water_velocity status_flag
- valid_max :
- 9
- valid_min :
- 0
\n", "
\n", " \n", " \n", @@ -1035,18 +1035,18 @@ " \n", "
\n", " \n", " \n", "Bytes \n", - "1.41 GiB \n", + "4.19 GiB \n", "5.76 MiB \n", "\n", " \n", "Shape \n", - "(25027, 74, 102) \n", + "(74523, 74, 102) \n", "(100, 74, 102) \n", "\n", " \n", "Dask graph \n", - "251 chunks in 2 graph layers \n", + "746 chunks in 2 graph layers \n", "\n", " \n", - "Data type \n", @@ -1066,18 +1066,18 @@ "\n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1091,18 +1091,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1130,11 +1130,11 @@ " \n", " 102 \n", "74 \n", - "25027 \n", + "74523 \n", "\n", " \n", " UCUR_sd(TIME, LATITUDE, LONGITUDE)float64dask.array<chunksize=(100, 74, 102), meta=np.ndarray>
- ancillary_variables :
- UCUR_quality_control
- cell_methods :
- TIME: standard_deviation
- long_name :
- Standard deviation of sea water velocity U component values in 1 hour, after rejection of obvious bad data (see abstract).
- units :
- m s-1
- valid_max :
- 10.0
- valid_min :
- -10.0
\n", + "
UCUR_sd(TIME, LATITUDE, LONGITUDE)float64dask.array<chunksize=(100, 74, 102), meta=np.ndarray>
- ancillary_variables :
- UCUR_quality_control
- cell_methods :
- TIME: standard_deviation
- long_name :
- Standard deviation of sea water velocity U component values in 1 hour, after rejection of obvious bad data (see abstract).
- units :
- m s-1
- valid_max :
- 10.0
- valid_min :
- -10.0
\n", "
\n", " \n", " \n", @@ -1149,18 +1149,18 @@ " \n", "
\n", " \n", " \n", "Bytes \n", - "1.41 GiB \n", + "4.19 GiB \n", "5.76 MiB \n", "\n", " \n", "Shape \n", - "(25027, 74, 102) \n", + "(74523, 74, 102) \n", "(100, 74, 102) \n", "\n", " \n", "Dask graph \n", - "251 chunks in 2 graph layers \n", + "746 chunks in 2 graph layers \n", "\n", " \n", - "Data type \n", @@ -1180,18 +1180,18 @@ "\n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1205,18 +1205,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1244,11 +1244,11 @@ " \n", " 102 \n", "74 \n", - "25027 \n", + "74523 \n", "\n", " \n", " VCUR(TIME, LATITUDE, LONGITUDE)float64dask.array<chunksize=(100, 74, 102), meta=np.ndarray>
- ancillary_variables :
- VCUR_quality_control
- cell_methods :
- TIME: mean
- long_name :
- Mean of sea water velocity V component values in 1 hour, after rejection of obvious bad data (see abstract).
- standard_name :
- northward_sea_water_velocity
- units :
- m s-1
- valid_max :
- 10.0
- valid_min :
- -10.0
\n", + "
VCUR(TIME, LATITUDE, LONGITUDE)float64dask.array<chunksize=(100, 74, 102), meta=np.ndarray>
- ancillary_variables :
- VCUR_quality_control
- cell_methods :
- TIME: mean
- long_name :
- Mean of sea water velocity V component values in 1 hour, after rejection of obvious bad data (see abstract).
- standard_name :
- northward_sea_water_velocity
- units :
- m s-1
- valid_max :
- 10.0
- valid_min :
- -10.0
\n", "
\n", " \n", " \n", @@ -1263,18 +1263,18 @@ " \n", "
\n", " \n", " \n", "Bytes \n", - "1.41 GiB \n", + "4.19 GiB \n", "5.76 MiB \n", "\n", " \n", "Shape \n", - "(25027, 74, 102) \n", + "(74523, 74, 102) \n", "(100, 74, 102) \n", "\n", " \n", "Dask graph \n", - "251 chunks in 2 graph layers \n", + "746 chunks in 2 graph layers \n", "\n", " \n", - "Data type \n", @@ -1294,18 +1294,18 @@ "\n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1319,18 +1319,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1358,11 +1358,11 @@ " \n", " 102 \n", "74 \n", - "25027 \n", + "74523 \n", "\n", " \n", " VCUR_quality_control(TIME, LATITUDE, LONGITUDE)float64dask.array<chunksize=(100, 74, 102), meta=np.ndarray>
- comment :
- This value is set on the basis of the offline quality controls applied in the time domain (see abstract).
- flag_meanings :
- no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed not_used not_used interpolated_values missing_values
- flag_values :
- [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
- long_name :
- quality flag for northward_sea_water_velocity
- quality_control_conventions :
- IMOS standard flags
- quality_control_set :
- 1.0
- standard_name :
- northward_sea_water_velocity status_flag
- valid_max :
- 9
- valid_min :
- 0
\n", + "
VCUR_quality_control(TIME, LATITUDE, LONGITUDE)float64dask.array<chunksize=(100, 74, 102), meta=np.ndarray>
- comment :
- This value is set on the basis of the offline quality controls applied in the time domain (see abstract).
- flag_meanings :
- no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed not_used not_used interpolated_values missing_values
- flag_values :
- [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
- long_name :
- quality flag for northward_sea_water_velocity
- quality_control_conventions :
- IMOS standard flags
- quality_control_set :
- 1.0
- standard_name :
- northward_sea_water_velocity status_flag
- valid_max :
- 9
- valid_min :
- 0
\n", "
\n", " \n", " \n", @@ -1377,18 +1377,18 @@ " \n", "
\n", " \n", " \n", "Bytes \n", - "1.41 GiB \n", + "4.19 GiB \n", "5.76 MiB \n", "\n", " \n", "Shape \n", - "(25027, 74, 102) \n", + "(74523, 74, 102) \n", "(100, 74, 102) \n", "\n", " \n", "Dask graph \n", - "251 chunks in 2 graph layers \n", + "746 chunks in 2 graph layers \n", "\n", " \n", - "Data type \n", @@ -1408,18 +1408,18 @@ "\n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1433,18 +1433,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1472,11 +1472,11 @@ " \n", " 102 \n", "74 \n", - "25027 \n", + "74523 \n", "\n", " \n", " VCUR_sd(TIME, LATITUDE, LONGITUDE)float64dask.array<chunksize=(100, 74, 102), meta=np.ndarray>
- ancillary_variables :
- VCUR_quality_control
- cell_methods :
- TIME: standard_deviation
- long_name :
- Standard deviation of sea water velocity V component values in 1 hour, after rejection of obvious bad data (see abstract).
- units :
- m s-1
- valid_max :
- 10.0
- valid_min :
- -10.0
\n", + "
VCUR_sd(TIME, LATITUDE, LONGITUDE)float64dask.array<chunksize=(100, 74, 102), meta=np.ndarray>
- ancillary_variables :
- VCUR_quality_control
- cell_methods :
- TIME: standard_deviation
- long_name :
- Standard deviation of sea water velocity V component values in 1 hour, after rejection of obvious bad data (see abstract).
- units :
- m s-1
- valid_max :
- 10.0
- valid_min :
- -10.0
\n", "
\n", " \n", " \n", @@ -1491,18 +1491,18 @@ " \n", "
\n", " \n", " \n", "Bytes \n", - "1.41 GiB \n", + "4.19 GiB \n", "5.76 MiB \n", "\n", " \n", "Shape \n", - "(25027, 74, 102) \n", + "(74523, 74, 102) \n", "(100, 74, 102) \n", "\n", " \n", "Dask graph \n", - "251 chunks in 2 graph layers \n", + "746 chunks in 2 graph layers \n", "\n", " \n", - "Data type \n", @@ -1522,18 +1522,18 @@ "\n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1547,18 +1547,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1586,11 +1586,11 @@ " \n", " 102 \n", "74 \n", - "25027 \n", + "74523 \n", "\n", " \n", " filename(TIME)<U53dask.array<chunksize=(100,), meta=np.ndarray>\n", + "
filename(TIME)<U53dask.array<chunksize=(100,), meta=np.ndarray>\n", "
\n", " \n", " \n", @@ -1605,18 +1605,18 @@ " \n", "
\n", " \n", " \n", "Bytes \n", - "5.06 MiB \n", + "15.07 MiB \n", "20.70 kiB \n", "\n", " \n", "Shape \n", - "(25027,) \n", + "(74523,) \n", "(100,) \n", "\n", " \n", "Dask graph \n", - "251 chunks in 2 graph layers \n", + "746 chunks in 2 graph layers \n", "\n", " \n", - "Data type \n", @@ -1637,7 +1637,7 @@ "\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1646,7 +1646,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1658,12 +1658,12 @@ " \n", "\n", " \n", - " 25027 \n", + "74523 \n", "1 \n", "\n", " \n", "
LATITUDEPandasIndexPandasIndex(Index([-37.4551594, -37.4191082, -37.383057, -37.347004, -37.310955,\n", + "
LATITUDEPandasIndexPandasIndex(Index([-37.4551594, -37.4191082, -37.383057, -37.347004, -37.310955,\n", " -37.274899, -37.23885, -37.202801, -37.166748, -37.130699,\n", " -37.094646, -37.058594, -37.022545, -36.986492, -36.950443,\n", " -36.914391, -36.878338, -36.842285, -36.806236, -36.770187,\n", @@ -1678,12 +1678,12 @@ " -35.292088, -35.256039, -35.219986, -35.183937, -35.147888,\n", " -35.111835, -35.075783, -35.03973, -35.003681, -34.967632,\n", " -34.93158, -34.895527, -34.859474, -34.8234228],\n", - " dtype='float64', name='LATITUDE')) LONGITUDEPandasIndexPandasIndex(Index([132.953971, 132.998611, 133.043243, 133.087891, 133.132538, 133.177155,\n", + " dtype='float64', name='LATITUDE')) LONGITUDEPandasIndexPandasIndex(Index([132.953971, 132.998611, 133.043243, 133.087891, 133.132538, 133.177155,\n", " 133.221802, 133.266449, 133.311081, 133.355728,\n", " ...\n", " 137.060898, 137.105545, 137.150192, 137.194824, 137.239456, 137.284103,\n", " 137.328735, 137.373383, 137.418023, 137.462663],\n", - " dtype='float64', name='LONGITUDE', length=102)) TIMEPandasIndexPandasIndex(DatetimeIndex(['2009-11-23 17:29:59.999996928',\n", + " dtype='float64', name='LONGITUDE', length=102)) TIMEPandasIndexPandasIndex(DatetimeIndex(['2009-11-23 17:29:59.999996928',\n", " '2009-11-23 18:29:59.999993088',\n", " '2009-11-23 19:30:00',\n", " '2009-11-23 20:29:59.999996928',\n", @@ -1694,50 +1694,50 @@ " '2009-11-24 01:30:00',\n", " '2009-11-24 02:29:59.999996928',\n", " ...\n", - " '2013-12-31 14:29:59.999996928',\n", - " '2013-12-31 15:29:59.999993088',\n", - " '2013-12-31 16:30:00',\n", - " '2013-12-31 17:29:59.999996928',\n", - " '2013-12-31 18:29:59.999993088',\n", - " '2013-12-31 19:30:00',\n", - " '2013-12-31 20:29:59.999996928',\n", - " '2013-12-31 21:29:59.999993088',\n", - " '2013-12-31 22:30:00',\n", - " '2013-12-31 23:29:59.999996928'],\n", - " dtype='datetime64[ns]', name='TIME', length=25027, freq=None))- " + " '2024-02-19 19:30:00',\n", + " '2024-02-19 22:30:00',\n", + " '2024-02-19 23:30:00.000007168',\n", + " '2024-02-20 00:30:00.000003072',\n", + " '2024-02-20 01:30:00',\n", + " '2024-02-20 02:30:00.000007168',\n", + " '2024-02-20 03:30:00.000003072',\n", + " '2024-02-20 04:30:00',\n", + " '2024-02-20 05:30:00.000007168',\n", + " '2024-02-20 06:30:00.000003072'],\n", + " dtype='datetime64[ns]', name='TIME', length=74523, freq=None))
- Conventions :
- CF-1.6,IMOS-1.4
- abstract :
- The ACORN facility is producing NetCDF files containing vector current maps at 1 hour time intervals. They are produced from radial currents, which represent the surface sea water current component along the radial direction from a receiver antenna. Radials are calculated from the shift of the Bragg peaks in a power spectrum. Radials are extracted from the 5-minutes Doppler spectra at each grid point and then averaged over 1 hour period. A minimum first order signal-to-noise value of 8 dB is set for the radials. A set of Matlab tools to read reprocessed data files, perform additional quality-controls on radial and vector current components, and convert the files into netcdf format. Each current value computed in the selected grid points has a quality control flag. A set of 4 different statistical methods are used to define them: absolute deviation from the median (MAD); statistics of the velocity distributions; statistics of the distributions of the 1st derivative; statistics of the distributions of the high-frequency components. Data are flagged based on the results of the statistical tests: 4, if three or more tests fail; 3, if two tests fail; 2, if one test fails; 1, no test fails. Additional thresholds are applied on maximum current velocity and the radial beam intersecting angles (GDOP). The final product is mapped on a regular geographic grid. More information on the data processing is available through the document: Quality Control procedures for ACORN radars Manual Version 2.0. Integrated Marine Observing System. DOI: 10.26198/5c89b59a931cb (http://dx.doi.org/10.26198/5c89b59a931cb).
- acknowledgement :
- Any users (including re-packagers) of IMOS data are required to clearly acknowledge the source of the material in this format: "Data was sourced from the Integrated Marine Observing System (IMOS) - IMOS is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy and the Super Science Initiative."
- author :
- Cosoli, Simone
- author_email :
- simone.cosoli@uwa.edu.au
- citation :
- The citation in a list of references is: IMOS, [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access]
- comment :
- This NetCDF file has been created using the IMOS NetCDF Conventions v1.4.
- data_centre :
- Australian Ocean Data Network (AODN)
- data_centre_email :
- info@aodn.org.au
- date_created :
- 2019-06-19T05:02:03Z
- disclaimer :
- Data, products and services from IMOS are provided "as is" without any warranty as to fitness for a particular purpose.
- distribution_statement :
- Data may be re-used, provided that related metadata explaining the data has been reviewed by the user, and the data is appropriately acknowledged. Data, products and services from IMOS are provided "as is" without any warranty as to fitness for a particular purpose.
- file_version :
- Level 1 - Quality Controlled data
- file_version_quality_control :
- Data in this file has been through the quality control procedure as described in the document: Offline QC procedures for ACORN radars. Every data point in this file has an associated quality flag.
- geospatial_lat_max :
- -34.8234228
- geospatial_lat_min :
- -37.4551594
- geospatial_lat_units :
- degrees_north
- geospatial_lon_max :
- 137.462663
- geospatial_lon_min :
- 132.953971
- geospatial_lon_units :
- degrees_east
- geospatial_vertical_max :
- 0.0
- geospatial_vertical_min :
- 0.0
- geospatial_vertical_units :
- m
- institution :
- Australian Coastal Ocean Radar Network (ACORN)
- institution_references :
- http://www.imos.org.au/acorn.html
- instrument :
- WERA Oceanographic HF Radar/Helzel Messtechnik, GmbH
- keywords :
- Oceans
- license :
- http://creativecommons.org/licenses/by/4.0/
- local_time_zone :
- 9.5
- naming_authority :
- IMOS
- netcdf_version :
- 4.3.3.1
- principal_investigator :
- Cosoli, Simone
- project :
- Integrated Marine Observing System (IMOS)
- site_code :
- SAG, South Australian Gulf
- source :
- Terrestrial HF radar
- ssr_Stations :
- Cape Wiles (CWI), Cape Spencer (CSP)
- standard_name_vocabulary :
- NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table 27
- time_coverage_end :
- 2013-11-09T00:30:00Z
- time_coverage_start :
- 2013-11-09T00:30:00Z
- title :
- IMOS ACORN South Australian Gulf (SAG), one hour averaged current QC data, 2013-11-09T00:30:00Z
- " ], "text/plain": [ - "
- Conventions :
- CF-1.6,IMOS-1.4
- abstract :
- The IMOS Ocean Radar Facility (previously ACORN) is producing NetCDF files containing vector current maps at 1 hour time intervals. They are produced from radial currents, which represent the surface sea water current component along the radial direction from a receiver antenna. Radials are calculated from the shift of the Bragg peaks in a power spectrum. Radials are extracted from the 5-minutes Doppler spectra at each grid point and then averaged over 1 hour period. A minimum first order signal-to-noise value of 8 dB is set for the radials. A set of Matlab tools to read reprocessed data files, perform additional quality-controls on radial and vector current components, and convert the files into netcdf format. Each current value computed in the selected grid points has a quality control flag. A set of 4 different statistical methods are used to define them: absolute deviation from the median (MAD); statistics of the velocity distributions; statistics of the distributions of the 1st derivative; statistics of the distributions of the high-frequency components. Data are flagged based on the results of the statistical tests: 4, if three or more tests fail; 3, if two tests fail; 2, if one test fails; 1, no test fails. Additional thresholds are applied on maximum current velocity and the radial beam intersecting angles (GDOP). The final product is mapped on a regular geographic grid. More information on the data processing is available through the document: Quality Control procedures for ACORN radars Manual Version 2.1 Integrated Marine Observing System. DOI:10.26198/5c89b59a931cb (http://dx.doi.org/10.26198/5c89b59a931cb).
- acknowledgement :
- Any users (including re-packagers) of IMOS data are required to clearly acknowledge the source of the material in this format: "Australia\\'s Integrated Marine Observing System (IMOS) is enabled by the National Collaborative Research Infrastructure Strategy (NCRIS). It is operated by a consortium of institutions as an unincorporated joint venture, with the University of Tasmania as Lead Agent."
- author :
- Cosoli, Simone; Hetzel, Yasha
- author_email :
- simone.cosoli@uwa.edu.au; yasha.hetzel@uwa.edu.au
- citation :
- The citation in a list of references is: IMOS, [year-of-data-download], [Title], [data-access-URL], accessed [date-of-access]
- comment :
- This NetCDF file has been created using the IMOS NetCDF Conventions v1.4.
- data_centre :
- Australian Ocean Data Network (AODN)
- data_centre_email :
- info@aodn.org.au
- date_created :
- 2024-04-23T14:07:26Z
- disclaimer :
- Data, products and services from IMOS are provided "as is" without any warranty as to fitness for a particular purpose.
- distribution_statement :
- Data may be re-used, provided that related metadata explaining the data has been reviewed by the user, and the data is appropriately acknowledged. Data, products and services from IMOS are provided "as is" without any warranty as to fitness for a particular purpose.
- file_version :
- Level 1 - Quality Controlled data
- file_version_quality_control :
- Data in this file has been through the quality control procedure as described in the document: Quality Control procedures for ACORN radars Manual Version 2.1 Integrated Marine Observing System. DOI: 10.26198/5c89b59a931cb (http://dx.doi.org/10.26198/5c89b59a931cb). Every data point in this file has an associated quality flag.
- geospatial_lat_max :
- -34.8234228
- geospatial_lat_min :
- -37.4551594
- geospatial_lat_units :
- degrees_north
- geospatial_lon_max :
- 137.462663
- geospatial_lon_min :
- 132.953971
- geospatial_lon_units :
- degrees_east
- geospatial_vertical_max :
- 0.0
- geospatial_vertical_min :
- 0.0
- geospatial_vertical_units :
- m
- institution :
- IMOS Ocean Radar Facility
- institution_references :
- http://imos.org.au/facilities/oceanradar/
- instrument :
- WERA Oceanographic HF Radar/Helzel Messtechnik, GmbH
- keywords :
- Oceans
- license :
- http://creativecommons.org/licenses/by/4.0/
- local_time_zone :
- 9.5
- naming_authority :
- IMOS
- netcdf_version :
- 4.8.1
- principal_investigator :
- Cosoli, Simone
- project :
- Integrated Marine Observing System (IMOS)
- site_code :
- SAG, South Australian Gulf
- source :
- Terrestrial HF radar
- ssr_Stations :
- Cape Wiles (CWI), Cape Spencer (CSP)
- standard_name_vocabulary :
- NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table 27
- time_coverage_end :
- 2024-01-01T00:30:00Z
- time_coverage_start :
- 2024-01-01T00:30:00Z
- title :
- IMOS Ocean Radar Facility South Australian Gulf (SAG), one hour averaged current QC data, 2024-01-01T00:30:00Z
Size: 14GB\n", - "Dimensions: (TIME: 25027, LATITUDE: 74, LONGITUDE: 102)\n", + " Size: 41GB\n", + "Dimensions: (TIME: 74523, LATITUDE: 74, LONGITUDE: 102)\n", "Coordinates:\n", " * LATITUDE (LATITUDE) float64 592B -37.46 -37.42 ... -34.82\n", " * LONGITUDE (LONGITUDE) float64 816B 133.0 133.0 ... 137.4 137.5\n", - " * TIME (TIME) datetime64[ns] 200kB 2009-11-23T17:29:59.999...\n", + " * TIME (TIME) datetime64[ns] 596kB 2009-11-23T17:29:59.999...\n", "Data variables:\n", - " GDOP (TIME, LATITUDE, LONGITUDE) float64 2GB dask.array \n", - " NOBS1 (TIME, LATITUDE, LONGITUDE) float64 2GB dask.array \n", - " NOBS2 (TIME, LATITUDE, LONGITUDE) float64 2GB dask.array \n", - " UCUR (TIME, LATITUDE, LONGITUDE) float64 2GB dask.array \n", - " UCUR_quality_control (TIME, LATITUDE, LONGITUDE) float64 2GB dask.array \n", - " UCUR_sd (TIME, LATITUDE, LONGITUDE) float64 2GB dask.array \n", - " VCUR (TIME, LATITUDE, LONGITUDE) float64 2GB dask.array \n", - " VCUR_quality_control (TIME, LATITUDE, LONGITUDE) float64 2GB dask.array \n", - " VCUR_sd (TIME, LATITUDE, LONGITUDE) float64 2GB dask.array \n", - " filename (TIME) \n", + " GDOP (TIME, LATITUDE, LONGITUDE) float64 4GB dask.array \n", + " NOBS1 (TIME, LATITUDE, LONGITUDE) float64 4GB dask.array \n", + " NOBS2 (TIME, LATITUDE, LONGITUDE) float64 4GB dask.array \n", + " UCUR (TIME, LATITUDE, LONGITUDE) float64 4GB dask.array \n", + " UCUR_quality_control (TIME, LATITUDE, LONGITUDE) float64 4GB dask.array \n", + " UCUR_sd (TIME, LATITUDE, LONGITUDE) float64 4GB dask.array \n", + " VCUR (TIME, LATITUDE, LONGITUDE) float64 4GB dask.array \n", + " VCUR_quality_control (TIME, LATITUDE, LONGITUDE) float64 4GB dask.array \n", + " VCUR_sd (TIME, LATITUDE, LONGITUDE) float64 4GB dask.array \n", + " filename (TIME) \n", "Attributes: (12/40)\n", " Conventions: CF-1.6,IMOS-1.4\n", - " abstract: The ACORN facility is producing NetCDF fil...\n", + " abstract: The IMOS Ocean Radar Facility (previously ...\n", " acknowledgement: Any users (including re-packagers) of IMOS...\n", - " author: Cosoli, Simone\n", - " author_email: simone.cosoli@uwa.edu.au\n", + " author: Cosoli, Simone; Hetzel, Yasha\n", + " author_email: simone.cosoli@uwa.edu.au; yasha.hetzel@uwa...\n", " citation: The citation in a list of references is: I...\n", " ... ...\n", " source: Terrestrial HF radar\n", " ssr_Stations: Cape Wiles (CWI), Cape Spencer (CSP)\n", " standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metadata ...\n", - " time_coverage_end: 2013-11-09T00:30:00Z\n", - " time_coverage_start: 2013-11-09T00:30:00Z\n", - " title: IMOS ACORN South Australian Gulf (SAG), on..." + " time_coverage_end: 2024-01-01T00:30:00Z\n", + " time_coverage_start: 2024-01-01T00:30:00Z\n", + " title: IMOS Ocean Radar Facility South Australian..." ] }, "execution_count": 6, @@ -1768,7 +1768,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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