S-MODE Workshop: Science Case Study In Situ

imported on: 2024-10-30

This notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop

The original source for this document is https://github.com/podaac/2022-SMODE-Open-Data-Workshop/blob/main/notebooks/insitu_dataviz_demo.ipynb

import glob
from netCDF4 import Dataset
import xarray as xr
import numpy as np
import matplotlib.pyplot as plt

import gsw

S-MODE Data Workshop: in-situ data demo

This is a simple demo of a workflow for downloading and working with S-MODE in-situ data. We will work with underway data collected by saildrones and R/V Oceanus during the S-MODE Pilot Campaign.

If you have any questions, do not hesitate in contacting me (cesar.rocha@uconn.edu).

Road map

Download saildrone data
Load and plot single dataset (one saildrone)
Load and merge multiple datasets (five saildrones)
Compare saildrone ADCP data with R/V Oceanus ADCP data

First, download the saildrone data

The saildrone data are organized into two different sets of files: ADCP (velocity measurements) and NON-ADCP (everything else). The ADCP data are available in 5-minute averages. The NON-ADCP data are available in 1-minute averages.

# constants for podaac-data-downloader
start_time = '2021-10-20T00:00:00Z'
end_time = '2021-11-08T00:00:00Z'
short_name = 'SMODE_LX_SAILDRONES_V1'
data_path = 'data/'

!podaac-data-downloader -c $short_name -d $data_path$short_name --start-date $start_time --end-date $end_time

/Users/crocha/miniconda3/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.8) or chardet (5.0.0)/charset_normalizer (2.0.4) doesn't match a supported version!
  warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "
[2022-11-28 19:19:40,724] {podaac_data_downloader.py:243} INFO - Found 10 total files to download
[2022-11-28 19:19:40,772] {podaac_data_downloader.py:268} INFO - 2022-11-28 19:19:40.772325 SKIPPED: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SMODE_LX_SAILDRONES_V1/SMODE_PFC_saildrone_adcp_1074.nc
[2022-11-28 19:19:40,812] {podaac_data_downloader.py:268} INFO - 2022-11-28 19:19:40.812388 SKIPPED: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SMODE_LX_SAILDRONES_V1/SMODE_PFC_saildrone_adcp_1075.nc
[2022-11-28 19:19:40,852] {podaac_data_downloader.py:268} INFO - 2022-11-28 19:19:40.852458 SKIPPED: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SMODE_LX_SAILDRONES_V1/SMODE_PFC_saildrone_adcp_1062.nc
[2022-11-28 19:19:40,878] {podaac_data_downloader.py:268} INFO - 2022-11-28 19:19:40.878249 SKIPPED: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SMODE_LX_SAILDRONES_V1/SMODE_PFC_saildrone_nonadcp_1062.nc
[2022-11-28 19:19:40,905] {podaac_data_downloader.py:268} INFO - 2022-11-28 19:19:40.905060 SKIPPED: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SMODE_LX_SAILDRONES_V1/SMODE_PFC_saildrone_nonadcp_1074.nc
[2022-11-28 19:19:40,925] {podaac_data_downloader.py:268} INFO - 2022-11-28 19:19:40.925428 SKIPPED: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SMODE_LX_SAILDRONES_V1/SMODE_PFC_saildrone_nonadcp_1075.nc
[2022-11-28 19:19:40,982] {podaac_data_downloader.py:268} INFO - 2022-11-28 19:19:40.982226 SKIPPED: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SMODE_LX_SAILDRONES_V1/SMODE_PFC_saildrone_adcp_1072.nc
[2022-11-28 19:19:41,043] {podaac_data_downloader.py:268} INFO - 2022-11-28 19:19:41.043138 SKIPPED: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SMODE_LX_SAILDRONES_V1/SMODE_PFC_saildrone_adcp_1073.nc
[2022-11-28 19:19:41,077] {podaac_data_downloader.py:268} INFO - 2022-11-28 19:19:41.077772 SKIPPED: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SMODE_LX_SAILDRONES_V1/SMODE_PFC_saildrone_nonadcp_1072.nc
[2022-11-28 19:19:41,114] {podaac_data_downloader.py:268} INFO - 2022-11-28 19:19:41.114363 SKIPPED: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SMODE_LX_SAILDRONES_V1/SMODE_PFC_saildrone_nonadcp_1073.nc
[2022-11-28 19:19:41,114] {podaac_data_downloader.py:287} INFO - Downloaded Files: 0
[2022-11-28 19:19:41,114] {podaac_data_downloader.py:288} INFO - Failed Files:     0
[2022-11-28 19:19:41,114] {podaac_data_downloader.py:289} INFO - Skipped Files:    10
[2022-11-28 19:19:41,375] {podaac_access.py:122} INFO - CMR token successfully deleted
[2022-11-28 19:19:41,375] {podaac_data_downloader.py:299} INFO - END

Non-ADCP data

Single saildrone

ds = xr.open_dataset(data_path + short_name + '/SMODE_PFC_saildrone_nonadcp_1073.nc').isel(trajectory=0)

ds

A few quick-and-dirty plots

ds.plot.scatter(x='longitude',y='latitude')
plt.title('SD-'+str(int(ds.trajectory)))

Text(0.5, 1.0, 'SD-1073')

# plot seabird-37 temperature data
ds.TEMP_SBE37_MEAN.plot()
plt.title('SD-'+str(int(ds.trajectory)))

Text(0.5, 1.0, 'SD-1073')

# plot data as a timeseries
fig, ax = plt.subplots(figsize=(12,4))
ds.TEMP_SBE37_MEAN.plot(x='time',ax=ax)
ax.set_xlabel('')
plt.title('SD-'+str(int(ds.trajectory)))

Text(0.5, 1.0, 'SD-1073')

# plot dat as a function of longitude
fig, ax = plt.subplots(figsize=(12,4))
ds.TEMP_SBE37_MEAN.plot(x='longitude',ax=ax)
plt.title('SD-'+str(int(ds.trajectory)))

Text(0.5, 1.0, 'SD-1073')

im = ds.plot.scatter(x='longitude',y='latitude',c=ds.TEMP_SBE37_MEAN)
plt.colorbar(im,label=ds.TEMP_SBE37_MEAN.attrs['standard_name'])
plt.title('SD-'+str(int(ds.trajectory)))

Text(0.5, 1.0, 'SD-1073')

# time speed of saildrone speed relative to ground
fig, ax = plt.subplots(figsize=(12,4))
ds.SOG_FILTERED_MEAN.plot(x='time')
plt.title('SD-'+str(int(ds.trajectory)))

Text(0.5, 1.0, 'SD-1073')

Example: calculate density, a derived quantitty

ds['sigma0'] = gsw.sigma0(ds.SAL_SBE37_MEAN,ds.TEMP_SBE37_MEAN)

# ds.sigma0.attrs['standard_name'] = 'sea_water_potential_density'
# ds.sigma0.attrs['long_name'] = 'Seawater potential density'
# ds.sigma0.attrs['units'] = 'kg m^{-3}'

# Now plot temperature, salinity and density
fig, axs = plt.subplots(1,3,figsize=(18,4))

variables = [
    ds.TEMP_SBE37_MEAN, 
    ds.SAL_SBE37_MEAN, 
    ds.sigma0
]

cmaps = ['jet', 'viridis','Spectral_r']

for  ax, var, cmap in zip(axs, variables, cmaps):

    im = ax.scatter(ds.longitude,ds.latitude,c=var,cmap=cmap)
    fig.colorbar(im,ax=ax,label=var.attrs['long_name'] + ' [' + var.attrs['units'] + ']')

All saildrones

Oftentimes it is conveninent to have all saildrone data in a single dataset for easy manipulation and comparisons.

files = glob.glob(data_path + short_name + '/*_nonadcp_*')

drop_vars = ['TEMP_DEPTH_HALFMETER_MEAN',
             'TEMP_DEPTH_HALFMETER_STDDEV',
             'corrected_backscatter_at_650nm',
             'particulate_organic_carbon',
             'calibrated_chlorophyll'
            ]

# loop through files (i.e., different saildrones),
# open dataset, drop variables not available for all
# saildrones and concatenate datasets along the trajectory
# dimension

for file in files:

    foo = xr.open_dataset(file)
    
    for drop_var in drop_vars:
        if drop_var in foo.variables:
            foo = foo.drop(drop_var)
    
    foo = foo.isel(trajectory=0).swap_dims(
        {'obs':'time'}
    ).expand_dims('trajectory')

    if file == files[0]:
        nonadcp = foo
    else:
        nonadcp = xr.concat([nonadcp,foo],dim='trajectory')

# single saildrone dataset is a subset of nonadcp dataset
nonadcp73 = nonadcp.sel(trajectory=1073.)

# project 1073's trajectory
nonadcp73.plot.scatter(x='longitude',y='latitude')

# plot all trajectories
for sd in nonadcp.trajectory:
    nonadcp.sel(trajectory=sd).plot.scatter(x='longitude',y='latitude',label=int(sd.values))
    
plt.legend()
plt.title('')

Text(0.5, 1.0, '')

ADCP data

Single file: saildrone 1072

adcp72 = xr.open_dataset(data_path + short_name + '/SMODE_PFC_saildrone_adcp_1072.nc').isel(trajectory=0)
adcp73 = xr.open_dataset(data_path + short_name + '/SMODE_PFC_saildrone_adcp_1073.nc').isel(trajectory=0)

# Slice the data in time, assigning a 3-day subset

# virtual mooring
# t0 = np.datetime64('2021-11-23T12')
# t1 = np.datetime64('2021-11-28T12')

# formation sailing
# t0 = np.datetime64('2021-11-01T04')
# t1 = np.datetime64('2021-11-01T18')

# comparison with oceanus
t0 = np.datetime64('2021-11-06T16')
t1 = np.datetime64('2021-11-06T20')

subset72 = adcp72.where((adcp72.time>=t0)&(adcp72.time<=t1),drop=True) 
subset73 = adcp73.where((adcp73.time>=t0)&(adcp73.time<=t1),drop=True)

depth_bin = 2

fig, ax = plt.subplots(figsize=(12,8))

for subset in [subset72,subset73]:

    ax.quiver(
        subset.longitude,
        subset.latitude,
        subset.vel_east.isel(cell_depth=depth_bin),
        subset.vel_north.isel(cell_depth=depth_bin),
        scale = 6
    )

ax.set_ylim(37.18,37.26)
ax.set_aspect(np.cos(37.22*np.pi/180))

# Example: vertical sections
fig, axs = plt.subplots(2,1, figsize=(12,10))
subset72.vel_east.where(subset72.percent_good>95).plot(x='longitude',y='cell_depth',ax=axs[0])
subset72.vel_north.where(subset72.percent_good>95).plot(x='longitude',y='cell_depth',ax=axs[1])
[ax.set_ylim(80,0) for ax in axs]

# Example: calculate and plot vertical shear
fig, ax = plt.subplots(figsize=(12,4))
kw = {'vmin': -0.03,'vmax': 0.03,'cmap': 'RdBu_r'}

subset.vel_north.differentiate('cell_depth').where(subset.percent_good>95).plot(x='time',**kw)

plt.ylim(80,0)
plt.xlim(t0,t1)

Compare saildrone ADCP data with R/V Oceanus data

A major goal of the S-MODE Pilot was to compare saildrone ADCP data. Here’s one example of a simple saildrone-Oceanus velocity comparison.

# this is not working for the Oceanus ADCP
start_time = '2021-10-20T00:00:00Z'
start_time = '2021-08-01T00:00:00Z'
end_time = '2021-11-08T00:00:00Z'

short_name = 'SMODE_LX_SHIPBOARD_ADCP_V1'
!podaac-data-downloader -c $short_name -d data/$short_name --start-date $start_time --end-date $end_time -e .nc4

/Users/crocha/miniconda3/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.8) or chardet (5.0.0)/charset_normalizer (2.0.4) doesn't match a supported version!
  warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "
[2022-11-28 19:46:35,848] {podaac_data_downloader.py:243} INFO - Found 2 total files to download
[2022-11-28 19:46:35,885] {podaac_data_downloader.py:268} INFO - 2022-11-28 19:46:35.885322 SKIPPED: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SMODE_LX_SHIPBOARD_ADCP_V1/S-MODE_PFC_OC2108A_adcp_os75nb.nc4
[2022-11-28 19:46:35,967] {podaac_data_downloader.py:268} INFO - 2022-11-28 19:46:35.967540 SKIPPED: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SMODE_LX_SHIPBOARD_ADCP_V1/S-MODE_PFC_OC2108A_adcp_wh300.nc4
[2022-11-28 19:46:35,967] {podaac_data_downloader.py:287} INFO - Downloaded Files: 0
[2022-11-28 19:46:35,967] {podaac_data_downloader.py:288} INFO - Failed Files:     0
[2022-11-28 19:46:35,967] {podaac_data_downloader.py:289} INFO - Skipped Files:    2
[2022-11-28 19:46:36,245] {podaac_access.py:122} INFO - CMR token successfully deleted
[2022-11-28 19:46:36,245] {podaac_data_downloader.py:299} INFO - END

ds = xr.open_dataset('data/SMODE_LX_SHIPBOARD_ADCP_V1/S-MODE_PFC_OC2108A_adcp_wh300.nc4',drop_variables='depth').isel(trajectory=0)
ds['depth'] =  Dataset('data/SMODE_LX_SHIPBOARD_ADCP_V1/S-MODE_PFC_OC2108A_adcp_wh300.nc4')['depth'][0]

subset_oceanus = ds.where((ds.time>=t0)&(ds.time<=t1),drop=True)

# interpolate to subset72 time
# TODO: try to resample with averaging to 5 minutes before interpolating (e.g.,  subset_oceanus.resample(time='5min').mean())
subset_oceanus = subset_oceanus.interp({'time': subset72.time})

/Users/crocha/miniconda3/lib/python3.9/site-packages/xarray/core/missing.py:562: FutureWarning: Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.
  imin = index.get_loc(minval, method="nearest")
/Users/crocha/miniconda3/lib/python3.9/site-packages/xarray/core/missing.py:563: FutureWarning: Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.
  imax = index.get_loc(maxval, method="nearest")

# interpolate to saildrone depth to oceanus depth
subset72['depth'] = subset72.cell_depth + 1.9  # add depth of the instrument 1.9
subset72 = subset72.swap_dims({'cell_depth': 'depth'})
subset72 = subset72.interp({'depth': subset_oceanus.depth})

/Users/crocha/miniconda3/lib/python3.9/site-packages/xarray/core/missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.
  imin = index.get_loc(minval, method="nearest")
/Users/crocha/miniconda3/lib/python3.9/site-packages/xarray/core/missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.
  imax = index.get_loc(maxval, method="nearest")

Visual comparison

depth_bin = 1
sc = 6

fig, ax = plt.subplots(figsize=(12,8))

q1 = plt.quiver(
        subset72.longitude,
        subset72.latitude,
        subset72.vel_east.isel(depth=depth_bin),
        subset72.vel_north.isel(depth=depth_bin),
        color='C00',scale=sc
)


q2 = plt.quiver(
        subset_oceanus.longitude,
        subset_oceanus.latitude,
        subset_oceanus.zonal_velocity_component.isel(depth=depth_bin),
        subset_oceanus.meridional_velocity_component.isel(depth=depth_bin),
        color='C02', scale=sc
)

plt.quiverkey(q1, .1, 0.815, .5, 'SD-1072',)
plt.quiverkey(q2, .1, 0.620, .5, 'R/V Oceanus',)

ax.set_ylim(37.18,37.26)
ax.set_aspect(np.cos(37.22*np.pi/180))

fig, axs = plt.subplots(2,1,figsize=(12,14))

kw = {'vmin': -.4,'vmax': +.4,'cmap': 'RdBu_r'}

subset_oceanus.zonal_velocity_component.plot(x='time',y='depth',ax=axs[0],**kw)
subset72.vel_east.plot(x='time',y='depth',ax=axs[1])

axs[0].set_title('R/V Oceanus')
axs[1].set_title('SD-1072')

[ax.set_ylim(80,0) for ax in axs]

Quantitative comparison: calculate and plot velocity differences

subset_oceanus = subset_oceanus.where(subset_oceanus.depth<=70)
subset72 =  subset72.where(subset_oceanus.depth<=70)

du = subset_oceanus.zonal_velocity_component-subset72.vel_east
dv = subset_oceanus.meridional_velocity_component-subset72.vel_north

_ = plt.hist(dv.values.flatten(),bins=np.arange(-.15,.156,.01))

du.mean().values, dv.mean().values

(array(-0.01207606), array(0.00744287))

du.std().values, dv.std().values

(array(0.03616049), array(0.04748247))