def plot_argo(
subtype='ARGOB', elements=(('SALNY',), 100), platform='029',
start='20180121/0000Z', stop='20180121/1559Z'
):
"""
Extract salinity profiles from ARGO buoy observation network data
Parameters
----------
subtype : str, optional
MetDB subtype name:
* 'ARGO' : for observation-only data from 2008-10-27 to 2018-07-01
* 'ARGOB' : for observation-only data from 2017-10-16 onwards
elements : tuple, optional
Field names and number of records to extract
platform : str, optional
Platform id
start : str, optional
Start date and time
stop : str, optional
End date and time
"""
req = mdbx.Query(subtype, elements, platform=platform, start=start,
stop=stop)
req.plot('SALNY', index=0, title='salinity',
plt_type='scatter', show=True, valid_min=10, map_buffer=5)
def plot_crete_dust():
"""SATAOD Crete dust case."""
date = '20180325'
plt = plot_sataod(
AREA=['42N', '25N', '12E', '35E'],
START='/'.join([date, '0000Z']), STOP='/'.join([date, '2359Z']),
# delta=(0.1, 0.1), plt_type='scatter',
delta=(.25, .25),
vmin=0, vmax=3,
map_res='l',
drawcountries=True,
gspacing=(5, 5),
title='MODIS Aqua+Terra aerosol optical depth (DT+DB) on ' + date,
cb_on=True, cb_title='AOD at 550nm []')
return plt
# def plot_wow_air_temp():
# """Plot WOW surface air temperature."""
SUBTYPE = 'WOW'
ELEMENTS = 'SRFC_AIR_TMPR'
AREA = ['63N', '49N', '11W', '3E']
kw_query = dict(area=AREA, start='TODAY-1/0830Z', stop='TODAY-1/0900Z')
kw_plot = dict(projection=ccrs.Robinson(), delta=(0.2, 0.2),
cmap='viridis', cb_on=True, use_cartopy=True, map_res='h',
gspacing=(4, 4), describe_data=True, vmin=270, vmax=290)
q = mdbx.Query(SUBTYPE, ELEMENTS, **kw_query)
return q.plot(ELEMENTS, **kw_plot)
def plot_sataod(
ELEMENT='AOD_NM550', AREA=None, START=None, STOP=None, constrain=None,
**kw
):
"""Plot MODIS AOD."""
q = mdbx.Query('SATAOD', ELEMENT, area=AREA,
start=START, stop=STOP,
constrain=constrain, keep=True)
return q.plot(ELEMENT, **kw)
def plot_sataod_test(
ELEMENT='AOD_NM550', AREA=None, START=None, STOP=None, constrain=None,
**kw
):
"""Plot MODIS AOD from mdb-test server."""
q = mdbx.Query('SATAOD', [ELEMENT, 'STLT_IDNY'], area=AREA,
start=START, stop=STOP,
ddict='\"/path/to/tests/SATAOD/retrieval_table\"', # update
hostname='mdb-test',
constrain=constrain, keep=True)
return q.plot(ELEMENT, **kw)
def plot_viirs_aod():
q = mdbx.Query(
'SATAOD', ['AOD_NM550', 'STLT_IDNY'],
# area=AREA, # ['63N', '49N', '12W', '4E']
start='20200210/0000Z',
stop='20200210/2359Z',
ddict='\"/path/to/tests/SATAOD/rtable_viirs\"',
hostname='mdb-test',
constrain={'STLT_IDNY': 224}
)
q.plot('AOD_NM550', use_cartopy=False, show=True)
def plot_atdnet_India(
subtype='ATDNET', elements='LGHN_STRK',
area=['40N', '0N', '60E', '100E'],
start='20190112/0000Z', stop='20190210/2300Z'
):
req = mdbx.Query(subtype, elements, start=start, stop=stop, area=area,
keep=True)
req.plot(elements, plt_type='scatter', s_marker='+', s_lw=0.1,
s_color='r', use_cartopy=True, drawcountries=True,
drawstates=True,
map_res='l', show=True)
def plot_msgradfd_geo():
"""MSG Cloud Top Temperature Full Disc."""
import cartopy.crs as ccrs
SUBTYPE = 'MSGRADFD'
ELEMENT = 'CLOD_TOP_TMPR'
req = mdbx.Query(
SUBTYPE, ELEMENT,
area=['67N', '67S', '67W', '67E'], # must specify
start='TODAY-1/1000Z',
stop='TODAY-1/1230Z', keep=True)
return req.plot(
ELEMENT, use_cartopy=True,
projection=ccrs.Geostationary(),
delta=(.5, .5), cb_on=True, describe_data=True)
def plot_snow_depth():
"""Plot Snow Depth show only 0 values."""
req = mdbx.Query(
'LNDSYB', 'SNOW_DPTH', area=['90N', '20N', '20W', '80E'],
start='20171220/0000Z', stop='20171220/2359Z')
plt_kw = dict(
plt_type='scatter',
s_marker='+', s_ms=5, s_color='k',
cb_on=False,
use_cartopy=True, map_res='h',
valid_min=0, valid_max=0,
show=True)
req.plot('SNOW_DPTH', cb_title='snow depth [m]', **plt_kw)
def plot_ascat_mergde_model_field():
"""Plot merged model data in ASCAT subtype."""
SUBTYPE = 'ASCAT'
MERGED_FIELD = 'MODL_SRFC_HGHT'
AREA = ['63N', '49N', '12W', '4E']
# Merged fields are not defined in the subtype table, so first we do it:
# 1. manually:
# >>> subtypes.DTYPE_MAPS[SUBTYPE][MERGED_FIELD] = 'f4'
#
# or 2. using set_element_dtype() method:
# >>> req = mdbx.Query(SUBTYPE, ELEMENTS, ...)
# >>> req.set_elem ent_dtype(ELEMENTS, dtypes)
#
# or 3. using fix_unknown keyword in extrtact() or plot() methods:
# >>> req.extract(fix_unknown=True)
# >>> req.plot(fix_unknown=True)
# Then send the MetDB request with mergeback option enabled
req = mdbx.Query(SUBTYPE, MERGED_FIELD, stop='TODAY-1/2345Z', area=AREA,
keep=True, merged=True)
map_title = '{}:{} {}-{}'.format(
SUBTYPE, MERGED_FIELD, req.start, req.stop)
plot_kw = dict(
fix_unknown=True,
title=map_title,
cb_on=True, cb_title='model surface height [m]',
use_cartopy=True,
gspacing=(4, 4),
describe_data=True,
cmap='viridis',
plt_type='tricontour',
c_levels=30,
show=True,
)
req.plot(MERGED_FIELD, **plot_kw)
def extract_giirs():
"""
Define subtype/elements and extract GIIRS data from mdb-test.
GIIRS Radiance stored at 1650 channels
"""
SUBTYPE = 'GIIRS'
ELEMENTS = ['LNGD_LW', 'LTTD_LW', (('CHNL_RDNC',), 1650)]
AREA = ['90N', '90S', '180W', '180E']
START, STOP = '20200130/0500Z', '20200130/0959Z'
# GIIRS entries are not in subtype definition, so update manually
GIIRS_dict = {
'GIIRS': {
'BAND_TYPE': 'i4', # band (set to missing)
'BAND_TYPE_LW': 'i4', # band (2=LW)
'BAND_TYPE_MW': 'i2', # band (3=MW)
'CHNL_END_LW': 'f4', # endChannel
'CHNL_END_MW': 'f4', # endChannel
'CHNL_NMBR': 'i4', # channelNumber
'CHNL_RDNC': 'f4',
'CHNL_RDNC_NSE': 'f4', # channelRadiance (use for noise, NEdR)
'CHNL_RPLTN_CONT': 'i4', # extndDelayedDescriptorReplicationFactor
'CHNL_STRT_LW': 'f4', # startChannel
'CHNL_STRT_MW': 'f4', # startChannel
# confidenceFlag (dataset "QF_LWElementExploration").
# 0=valid, 1=invalid, 15=missing
'CNFC_FLAG_LW': 'i4',
# confidenceFlag (dataset "QF_MWElementExploration").
# 0=valid, 1=invalid, 15=missing
'CNFC_FLAG_MW': 'i4',
'DAY': 'f4',
'FOR_NMBR': 'f4', # fieldOfRegardNumber (attribute "Dwell number")
'FOV_NMBR': 'i4',
'HOUR': 'i4',
# l1ProcessingFlag (attribute "Data Quality"). 0=OK, 1=not OK
'L1_PRCSG_FLAG': 'i2',
'LNGD': 'f4',
'LNGD_LW': 'f4',
'LNGD_MW': 'f4',
'LTTD': 'f4',
'LTTD_LW': 'f4',
'LTTD_MW': 'f4',
'MINT': 'i4',
'MNTH': 'i4',
'ORGNG_GNRTG_CNTR': 'S20', # centre (CCT C-1)
'ORGNG_GNRTG_CNTR2': 'S20', # subCentre (CCT C-12)
'RAD_FLGS': 'i4', # radianceTypeFlags (4=apodized, 5=unapodized)
'RCPT_DAY': 'i4',
'RCPT_HOUR': 'i4',
'RCPT_MINT': 'i4',
'RCPT_MNTH': 'i4',
'RCPT_YEAR': 'i4',
'SCAN_LINE_NMBR': 'i4',
'SCND': 'i4',
'SOLR_AZMH_LW': 'f4', # solarAzimuth
'SOLR_AZMH_MW': 'f4', # solarAzimuth
'SOLR_ZNTH_ANGL_LW': 'f4', # solarZenithAngle
'SOLR_ZNTH_ANGL_MW': 'f4', # solarZenithAngle
'STLT_AZMH_LW': 'f4', # bearingOrAzimuth
'STLT_AZMH_MW': 'f4', # bearingOrAzimuth
'STLT_CLAS': 'i4', # satelliteClassification (383=FY-4)
'STLT_IDNY': 'i4',
'STLT_INST': 'i4', # satelliteInstruments (962=GIIRS, CCT C-8)
'STLT_ZNTH_ANGL_LW': 'f4', # satelliteZenithAngle
'STLT_ZNTH_ANGL_MW': 'f4', # satelliteZenithAngle
# heightOfStation (m, to nearest 100m, geostationary height range)
'STTN_HGHT': 'f4',
'WAVE_NMBR_END_LW': 'f4', # waveNumber (end)
'WAVE_NMBR_END_MW': 'f4', # waveNumber (end)
'WAVE_NMBR_STRT_LW': 'f4', # waveNumber (start)
'WAVE_NMBR_STRT_MW': 'f4', # waveNumber (start)
'YEAR': 'i4',
}
}
mdbx.subtypes.DTYPE_MAPS.update(GIIRS_dict)
# -- Create a Query instance (update actual ddict path)
req = mdbx.Query(
SUBTYPE, ELEMENTS, area=AREA, start=START, stop=STOP,
ddict='\"/path/to/tests/GIIRS/tables/retrieval_table_GIIRS\"',
hostname='mdb-test'
)
data = req.extract(fix_unknown=True)
return data
def plot_sataod_india_covid19(year_ref='2019', mmmdd='Apr-07'):
"""Plot accumulated (from 31-March) AOD difference between 2020 and 2019.
Parameters
----------
mmmdd : str, optional
mmm-dd to accumulate AOD from baseline date (Mar-31) of respective year
"""
# India lock-down started on 2020-03-24
from ypylib.utils import XYZ
import numpy as np
from datetime import datetime
# import cartopy.crs as ccrs
stopd = datetime.strptime(mmmdd, '%b-%d').strftime('%m%d')
pngfile = os.path.expandvars('$HOME/MODIS_diff_India_' + mmmdd + '.png')
kw = dict(
area=['38N', '5N', '60E', '99E'],
start=year_ref + '0331/0000Z', stop=year_ref + stopd + '/2359Z',
# constrain={'STLT_IDNY': 784}, # aqua: 784, terra: 783
)
plt_kw = dict(
# projection=ccrs.Robinson(),
cb_extend='both',
cmap='seismic',
delta=(0.25, 0.25),
drawcountries=True,
drawstates_ind=True,
fillcontinents=True,
gspacing=(10, 10),
gzorder=3,
limit=[[60, 99], [5, 38]],
mask_ocean=True,
vmax=0.2,
vmin=-0.2,
# plt_type='contour', delta=(0.5, 0.5),
# c_levels=[-1, -0.5, -0.2, -0.1, 0, 0.1, 0.2, 0.5, 1]
)
q1 = mdbx.Query('SATAOD', 'AOD_NM550', **kw)
data = q1.extract()
gd1, xc, yc = XYZ(data['LNGD'], data['LTTD'], data['AOD_NM550']).griddata(
limit=plt_kw['limit'], delta=plt_kw['delta'], order=True
)
kw.update({'start': '20200331/0000Z', 'stop': '2020' + stopd + '/2359Z'})
q2 = mdbx.Query('SATAOD', 'AOD_NM550', **kw)
data = q2.extract()
gd2, xc, yc = XYZ(data['LNGD'], data['LTTD'], data['AOD_NM550']).griddata(
limit=plt_kw['limit'], delta=plt_kw['delta'], order=True
)
diff = gd2.data - gd1.data
xx, yy = np.meshgrid(xc, yc)
plt = XYZ(xx.ravel(), yy.ravel(), diff.ravel()).mapdata(
cb_title='{} (2020-{})'.format(r'$\Delta \tau_{550}$', year_ref),
**plt_kw
)
# -- title
plt.suptitle(
'Change in AOD over the Indian subcontinent '
'during Covid-19 lockdown'
)
plt.title('Data source: MODIS Terra & Aqua; Period: 31 March to ' +
datetime.strptime(mmmdd, '%b-%d').strftime('%d %b'))
# -- credit
plt.text(
99.5, 5.5, 'Credit: Met Office|NASA', color='#555555',
rotation='vertical', family='monospace', va='bottom'
)
# -- add logo
logo = plt.imread(os.path.expandvars(
'$HOME/bin/MO_SQUARE_black_mono_for_light_backg_RBG.png'
))
plt.imshow(logo, extent=(60, 69, 5, 13), zorder=3, alpha=1)
plt.savefig(pngfile)