In [1]:
import datetime as dt
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
%matplotlib inline
In [2]:
f=xr.open_dataset('https://salishsea.eos.ubc.ca/erddap/griddap/ubcSSg3DBiologyFields1hV19-05')
f
Out[2]:
<xarray.Dataset>
Dimensions: (depth: 40, gridX: 398, gridY: 898, time: 123528)
Coordinates:
* time (time) datetime64[ns] 2007-01-01T00:30:00 ....
* depth (depth) float32 0.5 1.5 2.5 ... 414.5 441.5
* gridY (gridY) int16 0 1 2 3 4 ... 894 895 896 897
* gridX (gridX) int16 0 1 2 3 4 ... 394 395 396 397
Data variables:
ammonium (time, depth, gridY, gridX) float32 ...
biogenic_silicon (time, depth, gridY, gridX) float32 ...
ciliates (time, depth, gridY, gridX) float32 ...
diatoms (time, depth, gridY, gridX) float32 ...
dissolved_organic_nitrogen (time, depth, gridY, gridX) float32 ...
flagellates (time, depth, gridY, gridX) float32 ...
mesozooplankton (time, depth, gridY, gridX) float32 ...
microzooplankton (time, depth, gridY, gridX) float32 ...
nitrate (time, depth, gridY, gridX) float32 ...
particulate_organic_nitrogen (time, depth, gridY, gridX) float32 ...
silicon (time, depth, gridY, gridX) float32 ...
Attributes:
acknowledgement: MEOPAR, ONC, Compute Canada
cdm_data_type: Grid
comment: If you use this dataset in your research,\nple...
Conventions: CF-1.6, COARDS, ACDD-1.3
creator_email: sallen@eoas.ubc.ca
creator_name: Salish Sea MEOPAR Project Contributors
creator_url: https://salishsea-meopar-docs.readthedocs.io/
description: biogeochemical variables
drawLandMask: over
history: 2021-02-03T23:09:29Z (local files)\n2021-02-03...
infoUrl: https://salishsea-meopar-docs.readthedocs.io/e...
institution: UBC EOAS
institution_fullname: Earth, Ocean & Atmospheric Sciences, Universit...
keywords: ammonia, ammonium, aquatic, biogenic, biogenic...
keywords_vocabulary: GCMD Science Keywords
license: The Salish Sea MEOPAR NEMO model results are c...
project: Salish Sea MEOPAR NEMO Model
sourceUrl: (local files)
standard_name_vocabulary: CF Standard Name Table v29
summary: Green, Salish Sea, 3d Biology Fields, Hourly, ...
testOutOfDate: now-16hours
time_coverage_end: 2021-02-02T23:30:00Z
time_coverage_start: 2007-01-01T00:30:00Z
timeStamp: 2021-Feb-02 17:14:23 GMT
title: Green, Salish Sea, 3d Biology Fields, Hourly, ...
uuid: 8be37b3d-0482-4af5-9949-87d2c6467b57xarray.Dataset
- depth: 40
- gridX: 398
- gridY: 898
- time: 123528
- time(time)datetime64[ns]2007-01-01T00:30:00 ... 2021-02-...
- _ChunkSizes :
- 1
- _CoordinateAxisType :
- Time
- actual_range :
- [1.1676114e+09 1.6123086e+09]
- axis :
- T
- comment :
- time values are UTC at the centre of the intervals over which the calculated model results are averaged
- coverage_content_type :
- modelResult
- ioos_category :
- Time
- long_name :
- Time axis
- standard_name :
- time
- time_origin :
- 01-JAN-1970 00:00:00
array(['2007-01-01T00:30:00.000000000', '2007-01-01T01:30:00.000000000', '2007-01-01T02:30:00.000000000', ..., '2021-02-02T21:30:00.000000000', '2021-02-02T22:30:00.000000000', '2021-02-02T23:30:00.000000000'], dtype='datetime64[ns]') - depth(depth)float320.5 1.5 2.5 ... 387.6 414.5 441.5
- _ChunkSizes :
- 40
- _CoordinateAxisType :
- Height
- _CoordinateZisPositive :
- down
- actual_range :
- [ 0.5000003 441.4661 ]
- axis :
- Z
- coverage_content_type :
- modelResult
- ioos_category :
- Location
- long_name :
- Depth
- name :
- deptht
- positive :
- down
- standard_name :
- depth
- units :
- m
array([ 0.5 , 1.500003, 2.500011, 3.500031, 4.500071, 5.500151, 6.50031 , 7.500623, 8.501236, 9.502433, 10.504766, 11.509312, 12.518167, 13.535412, 14.568982, 15.634288, 16.761173, 18.007135, 19.481785, 21.389978, 24.100256, 28.229916, 34.685757, 44.517723, 58.484333, 76.58559 , 98.06296 , 121.866516, 147.08946 , 173.11449 , 199.57304 , 226.2603 , 253.06664 , 279.93454 , 306.8342 , 333.75018 , 360.67453 , 387.6032 , 414.5341 , 441.4661 ], dtype=float32) - gridY(gridY)int160 1 2 3 4 5 ... 893 894 895 896 897
- actual_range :
- [ 0 897]
- coverage_content_type :
- modelResult
- ioos_category :
- location
- long_name :
- Y
- units :
- count
array([ 0, 1, 2, ..., 895, 896, 897], dtype=int16)
- gridX(gridX)int160 1 2 3 4 5 ... 393 394 395 396 397
- actual_range :
- [ 0 397]
- coverage_content_type :
- modelResult
- ioos_category :
- location
- long_name :
- X
- units :
- count
array([ 0, 1, 2, ..., 395, 396, 397], dtype=int16)
- ammonium(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- colorBarMaximum :
- 5.0
- colorBarMinimum :
- 0.0
- coverage_content_type :
- modelResult
- ioos_category :
- dissolved_nutrients
- long_name :
- Ammonium Concentration
- standard_name :
- mole_concentration_of_ammonium_in_sea_water
- units :
- mmol m-3
[1765976052480 values with dtype=float32]
- biogenic_silicon(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- colorBarMaximum :
- 70.0
- colorBarMinimum :
- 0.0
- coverage_content_type :
- modelResult
- ioos_category :
- dissolved_nutrients
- long_name :
- Biogenic Silicon Concentration
- standard_name :
- mole_concentration_of_organic_detritus_expressed_as_silicon_in_sea_water
- units :
- mmol m-3
[1765976052480 values with dtype=float32]
- ciliates(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- coverage_content_type :
- modelResult
- ioos_category :
- biology
- long_name :
- Mesodinium rubrum Concentration
- standard_name :
- mole_concentration_of_mesodinium_rubrum_expressed_as_nitrogen_in_sea_water
- units :
- mmol m-3
[1765976052480 values with dtype=float32]
- diatoms(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- colorBarMaximum :
- 20.0
- colorBarMinimum :
- 0.0
- coverage_content_type :
- modelResult
- ioos_category :
- biology
- long_name :
- Diatoms Concentration
- standard_name :
- mole_concentration_of_diatoms_expressed_as_nitrogen_in_sea_water
- units :
- mmol m-3
[1765976052480 values with dtype=float32]
- dissolved_organic_nitrogen(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- coverage_content_type :
- modelResult
- ioos_category :
- dissolved_nutrients
- long_name :
- Dissolved Organic N Concentration
- standard_name :
- mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_sea_water
- units :
- mmol m-3
[1765976052480 values with dtype=float32]
- flagellates(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- colorBarMaximum :
- 128.0
- colorBarMinimum :
- 0.0
- coverage_content_type :
- modelResult
- ioos_category :
- biology
- long_name :
- Flagellates Concentration
- standard_name :
- mole_concentration_of_flagellates_expressed_as_nitrogen_in_sea_water
- units :
- mmol m-3
[1765976052480 values with dtype=float32]
- mesozooplankton(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- coverage_content_type :
- modelResult
- ioos_category :
- biology
- long_name :
- Mesozooplankton Concentration
- standard_name :
- mole_concentration_of_mesozooplankton_expressed_as_nitrogen_in_sea_water
- units :
- mmol m-3
[1765976052480 values with dtype=float32]
- microzooplankton(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- coverage_content_type :
- modelResult
- ioos_category :
- biology
- long_name :
- Microzooplankton Concentration
- standard_name :
- mole_concentration_of_microzooplankton_expressed_as_nitrogen_in_sea_water
- units :
- mmol m-3
[1765976052480 values with dtype=float32]
- nitrate(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- colorBarMaximum :
- 40.0
- colorBarMinimum :
- 0.0
- coverage_content_type :
- modelResult
- ioos_category :
- dissolved_nutrients
- long_name :
- Nitrate Concentration
- standard_name :
- mole_concentration_of_nitrate_in_sea_water
- units :
- mmol m-3
[1765976052480 values with dtype=float32]
- particulate_organic_nitrogen(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- coverage_content_type :
- modelResult
- ioos_category :
- dissolved_nutrients
- long_name :
- Particulate Organic N Concentration
- standard_name :
- mole_concentration_of_particulate_organic_matter_expressed_as_nitrogen_in_sea_water
- units :
- mmol m-3
[1765976052480 values with dtype=float32]
- silicon(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- colorBarMaximum :
- 70.0
- colorBarMinimum :
- 0.0
- coverage_content_type :
- modelResult
- ioos_category :
- dissolved_nutrients
- long_name :
- Silicon Concentration
- standard_name :
- mole_concentration_of_silicate_in_sea_water
- units :
- mmol m-3
[1765976052480 values with dtype=float32]
- acknowledgement :
- MEOPAR, ONC, Compute Canada
- cdm_data_type :
- Grid
- comment :
- If you use this dataset in your research, please reference it with wording similar to the example below, and include citations of the publications below. Inclusion of the date(s) when you downloaded the dataset, and the dataset id help to ensure reproducibility of your work. Reference wording: 3d SMELT biological model field values from the SalishSeaCast model (Soontiens et al, 2016; Moore-Maley et al, 2016; Soontiens and Allen, 2017, Olson et al, 2020) were downloaded from their ERDDAP server (https://salishsea.eos.ubc.ca/erddap/) on DATE from dataset ubcSSg3DBiologyFields1hV19-05. Citations: Soontiens, N., Allen, S., Latornell, D., Le Souef, K., Machuca, I., Paquin, J.-P., Lu, Y., Thompson, K., Korabel, V., 2016. Storm surges in the Strait of Georgia simulated with a regional model. Atmosphere-Ocean 54 1-21. https://dx.doi.org/10.1080/07055900.2015.1108899 Moore-Maley, B. L., Allen, S. E., and IansonD., 2016. Locally-driven interannual variability of near-surface pH and ΩA in the Strait of Georgia. J. Geophys. Res. Oceans, 121(3), 1600–1625. https://dx.doi.org/10.1002/2015JC011118 Soontiens, N. and Allen, S., 2017. Modelling sensitivities to mixing and advection in a sill-basin estuarine system. Ocean Modelling, 112, 17-32. https://dx.doi.org/10.1016/j.ocemod.2017.02.008 Olson, E. M., Allen, S. E., Do, Vy, Dunphy, M., and Ianson, D., 2020. Assessment of Nutrient Supply by a Tidal Jet in the Northern Strait of Georgia Based on a Biogeochemical Model. J. Geophys. Res. Oceans, 125(8). https://doi.org/10.1029/2019JC015766
- Conventions :
- CF-1.6, COARDS, ACDD-1.3
- creator_email :
- sallen@eoas.ubc.ca
- creator_name :
- Salish Sea MEOPAR Project Contributors
- creator_url :
- https://salishsea-meopar-docs.readthedocs.io/
- description :
- biogeochemical variables
- drawLandMask :
- over
- history :
- 2021-02-03T23:09:29Z (local files) 2021-02-03T23:09:29Z https://salishsea.eos.ubc.ca/erddap/griddap/ubcSSg3DBiologyFields1hV19-05.das
- infoUrl :
- https://salishsea-meopar-docs.readthedocs.io/en/latest/results_server/index.html#salish-sea-model-results
- institution :
- UBC EOAS
- institution_fullname :
- Earth, Ocean & Atmospheric Sciences, University of British Columbia
- keywords :
- ammonia, ammonium, aquatic, biogenic, biogenic_silicon, biological, biosphere, chemistry, ciliates, concentration, deptht, detritus, diatoms, dissolved, dissolved_organic_nitrogen, Earth Science > Biological Classification > Protists > Diatoms, Earth Science > Biosphere > Aquatic Ecosystems > Plankton > Zooplankton, Earth Science > Oceans > Ocean Chemistry > Ammonia, Earth Science > Oceans > Ocean Chemistry > Nitrate, Earth Science > Oceans > Ocean Chemistry > Nitrogen, Earth Science > Oceans > Ocean Chemistry > Organic Matter, Earth Science > Oceans > Ocean Chemistry > Silicate, ecosystems, flagellates, marine, mesodinium rubrum, mesozooplankton, microzooplankton, mole_concentration_of_ammonium_in_sea_water, mole_concentration_of_diatoms_expressed_as_nitrogen_in_sea_water, mole_concentration_of_flagellates_expressed_as_nitrogen_in_sea_water, mole_concentration_of_mesodinium_rubrum_expressed_as_nitrogen_in_sea_water, mole_concentration_of_mesozooplankton_expressed_as_nitrogen_in_sea_water, mole_concentration_of_microzooplankton_expressed_as_nitrogen_in_sea_water, mole_concentration_of_nitrate_in_sea_water, mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_sea_water, mole_concentration_of_organic_detritus_expressed_as_silicon_in_sea_water, mole_concentration_of_particulate_organic_matter_expressed_as_nitrogen_in_sea_water, mole_concentration_of_silicate_in_sea_water, n02, nh4, nitrate, nitrogen, no3, ocean, oceans, organic, particulate, particulate_organic_nitrogen, plankton, protists, river, sea, seawater, silicate, silicon, time_counter, tracer, water, zooplankton
- keywords_vocabulary :
- GCMD Science Keywords
- license :
- The Salish Sea MEOPAR NEMO model results are copyright by the Salish Sea MEOPAR Project Contributors and The University of British Columbia. They are licensed under the Apache License, Version 2.0. https://www.apache.org/licenses/LICENSE-2.0
- project :
- Salish Sea MEOPAR NEMO Model
- sourceUrl :
- (local files)
- standard_name_vocabulary :
- CF Standard Name Table v29
- summary :
- Green, Salish Sea, 3d Biology Fields, Hourly, v19-05 3d SMELT biological model field values averaged over 1 hour intervals from Salish Sea NEMO model runs with physics, biology and chemistry. The values are calculated for the entire model grid that includes the Juan de Fuca Strait, the Strait of Georgia, Puget Sound, and Johnstone Strait on the coasts of Washington State and British Columbia. The time values are UTC. They are the centre of the intervals over which the calculated model results are averaged. Geo-location and depth data for the Salish Sea NEMO model grid are available in the ubcSSnBathymetry2V17-02 dataset. v17-02: Micromolar concentrations of ammonium, biogenic silicon, ciliates (mesodinium rubrum), diatoms, dissolved organic nitrogen, flagellates (nanophytoplankton), mesozooplankton, microzooplankton, nitrate, particulate organic nitrogen, silicon. Fraser River water turbidity tracer. NEMO-3.6; ubcSSnBathymetryV17-02 bathymetry; see infoUrl link for full details. v18-06: NEMO-3.6 SalishSeaCast-201806 configuration; ubcSSnBathymetryV17-02 bathymetry; see infoUrl link for full details. v18-12: NEMO-3.6 SalishSeaCast-201812 configuration; ubcSSnBathymetryV17-02 bathymetry; see infoUrl link for full details. v19-05: NEMO-3.6 SalishSeaCast-201905 configuration; ubcSSnBathymetryV17-02 bathymetry; see infoUrl link for full details.
- testOutOfDate :
- now-16hours
- time_coverage_end :
- 2021-02-02T23:30:00Z
- time_coverage_start :
- 2007-01-01T00:30:00Z
- timeStamp :
- 2021-Feb-02 17:14:23 GMT
- title :
- Green, Salish Sea, 3d Biology Fields, Hourly, v19-05
- uuid :
- 8be37b3d-0482-4af5-9949-87d2c6467b57
In [3]:
i0=130;i1=350
j0=330;j1=750
fig,ax=plt.subplots(1,1,figsize=(3,5))
ax.pcolormesh(f.variables['nitrate'][0,0,:,:])
ax.plot((i0,i1,i1,i0,i0),(j0,j0,j1,j1,j0),'r-')
f.close()
In [4]:
fig,ax=plt.subplots(10,2,figsize=(14,30))
fig.subplots_adjust(hspace=.4,wspace=.3)
ax=ax.flatten()
time=dt.datetime(2017,7,1,0,30)
with xr.open_dataset('https://salishsea.eos.ubc.ca/erddap/griddap/ubcSSg3DBiologyFields1hV19-05') as f:
with xr.open_dataset('https://salishsea.eos.ubc.ca/erddap/griddap/ubcSSg3DTracerFields1hV19-05') as fT:
for k in range(0,20):
sal=np.array(fT.salinity.sel(time=dt.datetime(2017,7,1,0,30)).\
isel(depth=k,gridY=slice(j0,j1),gridX=slice(i0,i1)).values).flatten()
no3=np.array(f.nitrate.sel(time=time).\
isel(depth=k,gridY=slice(j0,j1),gridX=slice(i0,i1)).values).flatten()
counts, xedges, yedges, m1=ax[k].hist2d(sal[~np.isnan(sal)&~np.isnan(no3)],
no3[~np.isnan(sal)&~np.isnan(no3)],
bins=36*3,norm=LogNorm())
cb=fig.colorbar(m1,ax=ax[k])
cb.set_label('Count')
ax[k].set_title('level k='+str(k))
ax[k].set_xlabel('S (g/kg)')
ax[k].set_ylabel('NO3 ($\mu$M)')
print('July 1')
July 1
In [5]:
fig,ax=plt.subplots(10,2,figsize=(14,30))
fig.subplots_adjust(hspace=.4,wspace=.3)
ax=ax.flatten()
time=dt.datetime(2017,3,1,0,30)
with xr.open_dataset('https://salishsea.eos.ubc.ca/erddap/griddap/ubcSSg3DBiologyFields1hV19-05') as f:
with xr.open_dataset('https://salishsea.eos.ubc.ca/erddap/griddap/ubcSSg3DTracerFields1hV19-05') as fT:
for k in range(0,20):
sal=np.array(fT.salinity.sel(time=dt.datetime(2017,7,1,0,30)).\
isel(depth=k,gridY=slice(j0,j1),gridX=slice(i0,i1)).values).flatten()
no3=np.array(f.nitrate.sel(time=time).\
isel(depth=k,gridY=slice(j0,j1),gridX=slice(i0,i1)).values).flatten()
counts, xedges, yedges, m1=ax[k].hist2d(sal[~np.isnan(sal)&~np.isnan(no3)],
no3[~np.isnan(sal)&~np.isnan(no3)],
bins=36*3,norm=LogNorm())
cb=fig.colorbar(m1,ax=ax[k])
cb.set_label('Count')
ax[k].set_title('level k='+str(k))
ax[k].set_xlabel('S (g/kg)')
ax[k].set_ylabel('NO3 ($\mu$M)')
print('March 1')
March 1
In [ ]: