longitude[j, i + 1], latitude[j, i + 1])
dx[:, i + 1] = dx[:, i]
xdiff_sign = np.sign(longitude[0, 1] - longitude[0, 0])
ydiff_sign = np.sign(latitude[1, 0] - latitude[0, 0])
return xdiff_sign * dx * units.meter, ydiff_sign * dy * units.meter
###############################################
# Create NCSS object to access the NetcdfSubset
# ---------------------------------------------
# Data from NOMADS GFS 0.5 deg Analysis Archive
# https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-forcast-system-gfs
dt = datetime(2017, 4, 5, 12)
ncss = NCSS(
'https://nomads.ncdc.noaa.gov/thredds/ncss/grid/gfs-004-anl/'
'{0:%Y%m}/{0:%Y%m%d}/gfsanl_4_{0:%Y%m%d}_{0:%H}00_000.grb2'.format(dt))
# Create lat/lon box for location you want to get data for
query = ncss.query().time(dt)
query.lonlat_box(north=65, south=15, east=310, west=220)
query.accept('netcdf4')
# Request data for vorticity
query.variables('Geopotential_height', 'Temperature', 'U-component_of_wind',
'V-component_of_wind')
data = ncss.get_data(query)
# Pull out variables you want to use
hght_var = data.variables['Geopotential_height']
temp_var = data.variables['Temperature']
for j in range(latitude.shape[0]):
_, _, dx[j, i] = g.inv(longitude[j, i], latitude[j, i],
longitude[j, i + 1], latitude[j, i + 1])
dx[:, i + 1] = dx[:, i]
xdiff_sign = np.sign(longitude[0, 1] - longitude[0, 0])
ydiff_sign = np.sign(latitude[1, 0] - latitude[0, 0])
return xdiff_sign * dx * units.meter, ydiff_sign * dy * units.meter
######################################
# Set up access to the data
# Create NCSS object to access the NetcdfSubset
ncss = NCSS(
'https://nomads.ncdc.noaa.gov/thredds/ncss/grid/gfs-004-anl/201608/20160822/'
'gfsanl_4_20160822_1800_003.grb2')
# Create lat/lon box for location you want to get data for
query = ncss.query()
query.lonlat_box(north=50, south=30, east=-80, west=-115)
query.time(datetime(2016, 8, 22, 21))
# Request data for geopotential height
query.variables('Geopotential_height', 'U-component_of_wind',
'V-component_of_wind')
query.vertical_level(100000)
data = ncss.get_data(query)
# Pull out variables you want to use
height_var = data.variables['Geopotential_height']
from metpy.units import units
from netCDF4 import num2date
import numpy as np
import numpy.ma as ma
from scipy.ndimage import gaussian_filter
from siphon.ncss import NCSS
###########################
# **Get the data**
#
# This example will use data from the North American Mesoscale Model Analysis
# (https://nomads.ncdc.gov/) for 12 UTC 27 April 2011.
base_url = 'https://www.ncei.noaa.gov/thredds/ncss/grid/namanl/'
dt = datetime(2011, 4, 27)
ncss = NCSS('{}{dt:%Y%m}/{dt:%Y%m%d}/namanl_218_{dt:%Y%m%d}_'
'1800_000.grb'.format(base_url, dt=dt))
# Query for required variables
gfsdata = ncss.query().all_times()
gfsdata.variables(
'Geopotential_height_isobaric', 'u-component_of_wind_isobaric',
'v-component_of_wind_isobaric', 'Temperature_isobaric',
'Relative_humidity_isobaric',
'Best_4_layer_lifted_index_layer_between_two_pressure_'
'difference_from_ground_layer', 'Absolute_vorticity_isobaric',
'Pressure_reduced_to_MSL_msl',
'Dew_point_temperature_height_above_ground').add_lonlat()
# Set the lat/lon box for the data to pull in.
gfsdata.lonlat_box(-135, -60, 15, 65)
def give_me_latest_gfs():
best_gfs = 'http://thredds.ucar.edu/thredds/catalog/grib/NCEP/GFS/Global_0p5deg/catalog.xml'
latest_gfs = get_latest_access_url(best_gfs, "NetcdfSubset")
ncss = NCSS(latest_gfs)
return ncss
import matplotlib.pyplot as plt
import metpy.calc as mcalc
from metpy.units import units
from netCDF4 import num2date
import numpy as np
import numpy.ma as ma
from scipy.ndimage import gaussian_filter
from siphon.ncss import NCSS
###########################
# **Get the data**
#
# This example will use data from the North American Mesoscale Model Analysis
# (https://nomads.ncdc.gov/) for 12 UTC 27 April 2011.
ncss = NCSS(
'https://nomads.ncdc.noaa.gov/thredds/ncss/grid/namanl/201104/20110427/'
'namanl_218_20110427_1800_000.grb')
# Query for required variables
gfsdata = ncss.query().all_times()
gfsdata.variables('Geopotential_height', 'u_wind', 'v_wind', 'Temperature',
'Relative_humidity', 'Best_4-layer_lifted_index',
'Absolute_vorticity', 'Pressure_reduced_to_MSL',
'Dew_point_temperature').add_lonlat()
# Set the lat/lon box for the data to pull in.
gfsdata.lonlat_box(-135, -60, 15, 65)
# Actually getting the data
data = ncss.get_data(gfsdata)
from netCDF4 import num2date
import numpy as np
from metpy.units import units
import scipy.ndimage as ndimage
from siphon.catalog import TDSCatalog
from siphon.ncss import NCSS
# =============================================================================
# RETRIEVE RAP AND HRRR DATA
# =============================================================================
RAP = 'http://thredds-jetstream.unidata.ucar.edu/thredds/catalog/grib/NCEP/RAP/CONUS_20km/latest.xml'
HRRR= 'http://thredds-jetstream.unidata.ucar.edu/thredds/catalog/grib/NCEP/HRRR/CONUS_2p5km/latest.xml'
GFS = 'http://thredds-jetstream.unidata.ucar.edu/thredds/catalog/grib/NCEP/GFS/Global_0p5deg/latest.xml'
DATA = TDSCatalog(GFS)
BEST_DATA = list(DATA.datasets.values())[0]
NCSS_DATA = NCSS(BEST_DATA.access_urls['NetcdfSubset'])
NOW = datetime.utcnow()
LATEST_DATA = NCSS_DATA.query().time(NOW).accept('netcdf4')
# =============================================================================
# UPPER-AIR VARIABLES
# =============================================================================
# 250: JET STREAM, GEOPOTENTIAL HEIGHT, POTENTIAL VORTICITY, IRROTATIONAL WIND
def 250hPa_GFS_jet_stream_SLP(lon_west, lon_east, lat_south, lat_north):
def 250hPa_GFS_jet_stream_jet_dyn(lon_west, lon_east, lat_south, lat_north):
# 500: VORTICITY, GEOPOTENTIAL HEIGHT, VORTICITY ADVECTION
def 500hPa_GFS_vorticity(lon_west, lon_east, lat_south, lat_north):
LATEST_DATA.variables('Geopotential_height_isobaric', 'u-component_of_wind_isobaric', 'v-component_of_wind_isobaric').add_lonlat()
LATEST_DATA.lonlat_box(lon_west, lon_east, lat_south, lat_north)
LATEST_DATA.vertical_level(50000)
import cartopy.feature as cfeature
import matplotlib.gridspec as gridspec
import matplotlib.pylab as plt
import metpy.calc as mpcalc
from metpy.units import units
from netCDF4 import num2date
import numpy as np
import scipy.ndimage as ndimage
from siphon.ncss import NCSS
#######################################
# Data Aquisition
# ---------------
# Open the example netCDF data
ncss = NCSS('https://www.ncei.noaa.gov/thredds/ncss/grid/namanl/'
'201604/20160416/namanl_218_20160416_1800_000.grb')
now = datetime.utcnow()
# Query for Latest GFS Run
hgt = ncss.query().time(datetime(2016, 4, 16, 18)).accept('netcdf')
hgt.variables('Geopotential_height_isobaric', 'u-component_of_wind_isobaric',
'v-component_of_wind_isobaric').add_lonlat()
# Actually getting the data
ds = ncss.get_data(hgt)
lon = ds.variables['lon'][:]
lat = ds.variables['lat'][:]
times = ds.variables[
ds.variables['Geopotential_height_isobaric'].dimensions[0]]
for j in range(latitude.shape[0]):
_, _, dx[j, i] = g.inv(longitude[j, i], latitude[j, i],
longitude[j, i + 1], latitude[j, i + 1])
dx[:, i + 1] = dx[:, i]
xdiff_sign = np.sign(longitude[0, 1] - longitude[0, 0])
ydiff_sign = np.sign(latitude[1, 0] - latitude[0, 0])
return xdiff_sign * dx * units.meter, ydiff_sign * dy * units.meter
#######################################
# Data Aquisition
# ---------------
# Open the example netCDF data
ncss = NCSS('http://nomads.ncdc.noaa.gov/thredds/ncss/grid/namanl/'
'201604/20160416/namanl_218_20160416_1800_000.grb')
now = datetime.utcnow()
# Query for Latest GFS Run
hgt = ncss.query().time(datetime(2016, 4, 16, 18)).accept('netcdf')
hgt.variables('Geopotential_height', 'u_wind', 'v_wind').add_lonlat()
# Actually getting the data
ds = ncss.get_data(hgt)
lon = ds.variables['lon'][:]
lat = ds.variables['lat'][:]
times = ds.variables[ds.variables['Geopotential_height'].dimensions[0]]
vtime = num2date(times[:], units=times.units)
from metpy.units import units
import metpy.calc as mpcalc
from netCDF4 import num2date
import numpy as np
from siphon.catalog import TDSCatalog
from siphon.ncss import NCSS
import scipy.ndimage as ndimage
gfs = TDSCatalog('http://atm.ucar.edu/thredds/catalog/grib/'
'NCEP/GFS/Global_0p25deg/catalog.xml')
dataset = list(gfs.datasets.values())[1]
print(dataset.access_urls)
# Create NCSS object to access the NetcdfSubset
ncss = NCSS(dataset.access_urls['NetcdfSubset'])
# get current date and time
now = datetime.utcnow()
now = datetime(now.year, now.month, now.day, now.hour)
# define time range you want the data for
start = now
end = now + timedelta(hours=12)
query = ncss.query()
query.time_range(start, end)
# query.lonlat_box(north=70, south=20, east=310., west=230.)
query.accept('netcdf4')
query.variables('Temperature_surface',
'Relative_humidity_entire_atmosphere_single_layer',
def setup(self):
"""Set up for tests with a default valid query."""
dt = datetime(2015, 6, 12, 15, 0, 0)
self.ncss = NCSS(self.server + self.url_path)
self.nq = self.ncss.query().lonlat_point(-105, 40).time(dt)
self.nq.variables('Temperature_isobaric', 'Relative_humidity_isobaric')
cmap=colormap,
transform=ccrs.PlateCarree())
cax = plt.subplot(gs[1])
cbar = plt.colorbar(contourfill,
cax=cax,
orientation='horizontal',
extend='max',
extendrect=True)
# Latest GFS Dataset
cat = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/grib/'
'NCEP/GFS/Global_0p5deg/latest.xml')
#cat.datasets = [GFS_Global_0p5deg_20201128_1200.grib2]
best_ds = list(cat.datasets.values())[0]
ncss = NCSS(best_ds.access_urls['NetcdfSubset']) #NetCDF subset service object
now = datetime.utcnow()
data_hght = queryData(ncss, "height")
data_wind = queryData(ncss, "wind")
latitudes = data_hght.variables['lat'][:]
longitudes = data_hght.variables['lon'][:]
heights250hPa = data_hght.variables['Geopotential_height_isobaric'][:]
# Smooth the 250-hPa heights using a gaussian filter from scipy.ndimage
hgt_250, longitudes = cutil.add_cyclic_point(heights250hPa, coord=longitudes)
Z_250 = ndimage.gaussian_filter(hgt_250[0, 0, :, :], sigma=3, order=0)
u250 = (units(data_wind.variables['u-component_of_wind_isobaric'].units) *
# Helper function for finding proper time variable
def find_time_var(var, time_basename='time'):
for coord_name in var.coordinates.split():
if coord_name.startswith(time_basename):
return coord_name
raise ValueError('No time variable found for ' + var.name)
###############################################
# Create NCSS object to access the NetcdfSubset
# ---------------------------------------------
# Data from NCEI GFS 0.5 deg Analysis Archive
base_url = 'https://www.ncei.noaa.gov/thredds/ncss/grid/gfs-g4-anl-files/'
dt = datetime(2018, 1, 4, 12)
ncss = NCSS('{}{dt:%Y%m}/{dt:%Y%m%d}/gfsanl_4_{dt:%Y%m%d}'
'_{dt:%H}00_000.grb2'.format(base_url, dt=dt))
# Create lat/lon box for location you want to get data for
query = ncss.query().time(dt)
query.lonlat_box(north=65, south=15, east=310, west=220)
query.accept('netcdf')
# Request data for model "surface" data
query.variables('Pressure_reduced_to_MSL_msl',
'Apparent_temperature_height_above_ground',
'u-component_of_wind_height_above_ground',
'v-component_of_wind_height_above_ground')
data = ncss.get_data(query)
###############################################
# Begin data maipulation
def get_ensemble_point(point,
variables=['Temperature_height_above_ground_ens'],
start=datetime.utcnow() - timedelta(hours=12),
end=datetime.utcnow() + timedelta(hours=48)):
"""
Retrieves the latest ("best") ensemble forecast valid at a single point from the Unidata THREDDS server using
the Unidata siphon library.
Requires:
point -> A tuple of (lat, lon) of the point we are trying to retrieve
variables -> A list of variables we want to retrieve. Check this page for a full list:
http://thredds.ucar.edu/thredds/metadata/grib/NCEP/GEFS/Global_1p0deg_Ensemble/members/Best?metadata=variableMap
start -> A datetime object of the earliest time to look for an ensemble initialization,
default is current time minus 12 hours
end -> The last time for which we want ensemble forecast output. Default is current time plus 48 hours.
Returns:
A dictionary with one item being the list of valid times in the data ('times') and the rest of the items
being numpy arrays of nTimes x nEnsmems for each variable requested
"""
# Import the Siphon utilities
from siphon.catalog import TDSCatalog
from siphon.ncss import NCSS
# In Siphon, we connect to a thredds catalog. Here's the address for the GEFS
catalog = 'http://thredds.ucar.edu/thredds/catalog/grib/NCEP/GEFS/Global_1p0deg_Ensemble/members/catalog.xml'
best_model = TDSCatalog(catalog)
# We select a specific dataset in this catalog, in this case the "best" (most recent) ensemble run
best_ds = list(best_model.datasets.values())[2]
ncss = NCSS(best_ds.access_urls['NetcdfSubset'])
# Here we format our subsetting query. We specify the exact point we want,
# the time range, and the variables we are requesting. We're also going
# to retrieve the data in a netcdf-like format
query = ncss.query()
query.lonlat_point(point[1], point[0])
query.time_range(start, end)
query.variables(*variables)
query.accept('netcdf')
# Actually get the data
data = ncss.get_data(query)
# Format our output into a dictionary
output = {}
for v in variables:
# After the squeeze, this is a nTimes x nEns array
output[v] = np.squeeze(data.variables[v][:])
#print output[v].shape
# Also, add times
# The 'time' variable is hours since "time_coverage_start"
# Get this in datetime format
raw_hours = list(np.squeeze(data.variables['time'][:]))
init_time = datetime.strptime(str(data.time_coverage_start),
'%Y-%m-%dT%H:%M:%SZ')
output['times'] = [init_time + timedelta(hours=int(x)) for x in raw_hours]
# Return a dictionary
return output
from ipywidgets import interact_manual
import matplotlib.pyplot as plt
import metpy.calc as mcalc
from metpy.units import units
from netCDF4 import num2date
import numpy as np
from siphon.ncss import NCSS
#######################################
# **Getting the data**
#
# In this example, NARR reanalysis data for 18 UTC 04 April 1987 from the National Centers
# for Environmental Information (https://nomads.ncdc.noaa.gov) will be used.
# Link to NetCDF subset service for NAM analysis data
ncss = NCSS('https://nomads.ncdc.noaa.gov/thredds/ncss/grid/narr-a/198704/19870404/'
'narr-a_221_19870404_1800_000.grb')
# Bring in needed data
modeldata = ncss.query().all_times()
modeldata.variables('Geopotential_height',
'u_wind',
'v_wind',
'Temperature',
'Specific_humidity').add_lonlat()
# Set the lat/lon box for the data you want to pull in.
# lonlat_box(north_lat,south_lat,east_lon,west_lon)
modeldata.lonlat_box(-140, -60, 16, 60)
# Actually getting the data
data = ncss.get_data(modeldata)
def setup(self):
dt = datetime(2015, 6, 12, 15, 0, 0)
self.ncss = NCSS(self.server + self.urlPath)
self.nq = self.ncss.query().lonlat_point(-105, 40).time(dt)
self.nq.variables('Temperature_isobaric', 'Relative_humidity_isobaric')
'\n' + str(np.int(data[mxy[i], mxx[i]])),
color=color,
size=12,
clip_on=True,
fontweight='bold',
horizontalalignment='center',
verticalalignment='top',
transform=transform)
###############################
# Get NARR data
dattim = datetime(1999, 1, 3, 0)
ncss = NCSS(
'https://www.ncei.noaa.gov/thredds/ncss/grid/narr-a-files/{0:%Y%m}/{0:%Y%m%d}/'
'narr-a_221_{0:%Y%m%d}_{0:%H}00_000.grb'.format(dattim))
query = ncss.query()
query.all_times().variables(
'Pressure_reduced_to_MSL_msl',
'Geopotential_height_isobaric').add_lonlat().accept('netcdf')
data = ncss.get_data(query)
###############################
# Extract data into variables
# Grab pressure levels
plev = list(data.variables['isobaric1'][:])
# Grab lat/lons and make all lons 0-360
lats = data.variables['lat'][:]
from metpy.calc import get_wind_components
from metpy.calc import reduce_point_density
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, StationPlot
from metpy.plots.wx_symbols import current_weather, sky_cover, wx_code_map
from metpy.units import units
# Request METAR data from TDS
metar = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/nws/metar/'
'ncdecoded/catalog.xml')
dataset = list(metar.datasets.values())[0]
print(list(dataset.access_urls))
# Access netcdf subset and use siphon to request data
ncss_url = dataset.access_urls['NetcdfSubset']
ncss = NCSS(ncss_url)
print(ncss.variables)
# get current date and time
now = datetime.utcnow()
now = datetime(now.year, now.month, now.day, now.hour)
# define time range you want the data for
start = now - timedelta(days=1)
end = now
# build the query
query = ncss.query()
query.lonlat_box(-5.5, 31.8, 42.7, 52.5)
query.time(now)
query.variables('air_temperature', 'dew_point_temperature', 'wind_speed',
import cartopy.feature as cfeature
import cartopy.util as cutil
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import metpy.calc as mpcalc
from netCDF4 import num2date
import numpy as np
import scipy.ndimage as ndimage
from siphon.catalog import TDSCatalog
from siphon.ncss import NCSS
# Latest GFS Dataset
cat = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/grib/'
'NCEP/GFS/Global_0p5deg/latest.xml')
best_ds = list(cat.datasets.values())[0]
ncss = NCSS(best_ds.access_urls['NetcdfSubset'])
# Set the time to current
now = datetime.utcnow()
# Query for Latest GFS Run
gfsdata = ncss.query().time(now).accept('netcdf4')
gfsdata.variables('Geopotential_height_isobaric',
'u-component_of_wind_isobaric',
'v-component_of_wind_isobaric').add_lonlat()
# Set the lat/lon box for the data you want to pull in.
# lonlat_box(north_lat,south_lat,east_lon,west_lon)
gfsdata.lonlat_box(0, 360, 0, 90)
# Set desired level 50000 = 50000 Pa = 500 hPa
%matplotlib inline
# <headingcell level=2>
# Extract HRRR data using Unidata's Siphon package
# <codecell>
# Resolve the latest HRRR dataset
from siphon.catalog import TDSCatalog
latest_hrrr = TDSCatalog('http://thredds-jumbo.unidata.ucar.edu/thredds/catalog/grib/HRRR/CONUS_3km/surface/latest.xml')
hrrr_ds = list(latest_hrrr.datasets.values())[0]
# Set up access via NCSS
from siphon.ncss import NCSS
ncss = NCSS(hrrr_ds.access_urls['NetcdfSubset'])
# Create a query to ask for all times in netcdf4 format for
# the Temperature_surface variable, with a bounding box
query = ncss.query()
# <codecell>
dap_url = hrrr_ds.access_urls['OPENDAP']
# <codecell>
query.all_times().accept('netcdf4').variables('u-component_of_wind_height_above_ground',
'v-component_of_wind_height_above_ground')
query.lonlat_box(45, 41., -63, -71.5)
