import numpy as np
import pyart
import gc
import tempfile
import os
# We want cfgrib to be an optional dependency to ensure Windows compatibility
try:
import cfgrib
CFGRIB_AVAILABLE = True
except:
CFGRIB_AVAILABLE = False
# We really only need the API to download the data, make ECMWF API an
# optional dependency since not everyone will have a login from the start.
try:
from ecmwfapi import ECMWFDataServer
ECMWF_AVAILABLE = True
except:
ECMWF_AVAILABLE = False
from netCDF4 import Dataset
from datetime import datetime, timedelta
from scipy.interpolate import griddata, NearestNDInterpolator
from copy import deepcopy
[docs]def download_needed_era_data(Grid, start_date, end_date, file_name):
"""
This function will download the ERA interim data in the region
specified by the input Py-ART Grid within the interval specified by
start_date and end_date. This is useful for the batch processing of
files since the ECMWF API is limited to 20 queued requests at a time.
This is also useful if you want to store ERA interim data for future
use without having to download it again.
You need to have the ECMWF API and an ECMWF account set up in order to
use this feature. Go to this website for instructions on installing the
API and setting up your account:
https://confluence.ecmwf.int/display/WEBAPI/Access+ECMWF+Public+Datasets
Parameters
----------
Grid: Py-ART Grid
The input Py-ART Grid to modify.
start_date: datetime
The start date of the file to download.
end_date: datetime
The end date of the file to download.
file_name:
The name of the destination file.
"""
if ECMWF_AVAILABLE is False and file_name is None:
raise (ModuleNotFoundError,
("The ECMWF API is not installed. Go to" +
"https://confluence.ecmwf.int/display/WEBAPI" +
"/Access+ECMWF+Public+Datasets" +
" in order to use the auto download feature."))
print("Download ERA Interim data...")
# ERA interim data is in pressure coordinates
# Retrieve u, v, w, and geopotential
# Geopotential is needed to convert into height coordinates
retrieve_dict = {}
retrieve_dict['stream'] = "oper"
retrieve_dict['levtype'] = "pl"
retrieve_dict['param'] = "131.128/132.128/135.128/129.128"
retrieve_dict['dataset'] = "interim"
retrieve_dict['levelist'] = ("1/2/3/5/7/10/20/30/50/70/100/125/150/" +
"175/200/225/250/300/350/400/450/500/" +
"550/600/650/700/750/775/800/825/850/" +
"875/900/925/950/975/1000")
retrieve_dict['step'] = "0"
retrieve_dict['time'] = "00/06/12/18"
retrieve_dict['date'] = (start_date.strftime("%Y-%m-%d") + '/to/' +
end_date.strftime("%Y-%m-%d"))
retrieve_dict['class'] = "ei"
retrieve_dict['grid'] = "0.75/0.75"
N = "%4.1f" % Grid.point_latitude["data"].max()
S = "%4.1f" % Grid.point_latitude["data"].min()
E = "%4.1f" % Grid.point_longitude["data"].max()
W = "%4.1f" % Grid.point_longitude["data"].min()
retrieve_dict['area'] = N + "/" + W + "/" + S + "/" + E
retrieve_dict['format'] = "netcdf"
retrieve_dict['target'] = file_name
server = ECMWFDataServer()
server.retrieve(retrieve_dict)
[docs]def make_constraint_from_era_interim(Grid, file_name=None, vel_field=None):
"""
This function will read ERA Interim in NetCDF format and add it
to the Py-ART grid specified by Grid. PyDDA will automatically download
the ERA Interim data that you need for the scan. It will chose the domain
that is enclosed by the analysis grid and the time period that is closest
to the scan. It will then do a Nearest Neighbor interpolation of the
ERA-Interim u and v winds to the analysis grid.
You need to have the ECMWF API and an ECMWF account set up in order to
use this feature. Go to this website for instructions on installing the
API and setting up your account:
https://confluence.ecmwf.int/display/WEBAPI/Access+ECMWF+Public+Datasets
Parameters
----------
Grid: Py-ART Grid
The input Py-ART Grid to modify.
file_name: str or None
The netCDF file containing the ERA Interim data. Setting to None will
invoke the API in order to attempt to download the data. If the web
API is experiencing delays, it is better to use it to download the
file and then refer to it here. If this file does not exist
PyDDA will use the API to create the file.
dest_era_file: str or None
If this is not None, then the ERA file that is saved using the
automatic download feature will be saved
to this file for future reading. This is useful in case the
web API is experiencing delays. This is not used if file_name
is specified.
vel_field: str or None
The name of the velocity field in the Py-ART grid. Set to None to
have Py-DDA attempt to automatically detect it.
Returns
-------
new_Grid: Py-ART Grid
The Py-ART Grid with the ERA Interim data added into the "u_erainterim",
"v_erainterim", and "w_erainterim" fields.
"""
if vel_field is None:
vel_field = pyart.config.get_field_name('corrected_velocity')
if ECMWF_AVAILABLE is False and file_name is None:
raise (ModuleNotFoundError,
("The ECMWF API is not installed. Go to" +
"https://confluence.ecmwf.int/display/WEBAPI" +
"/Access+ECMWF+Public+Datasets" +
" in order to use the auto download feature."))
grid_time = datetime.strptime(Grid.time["units"],
"seconds since %Y-%m-%dT%H:%M:%SZ")
hour_rounded_to_nearest_6 = int(6 * round(float(grid_time.hour)/6))
if hour_rounded_to_nearest_6 == 24:
grid_time = grid_time + timedelta(days=1)
grid_time = datetime(grid_time.year, grid_time.month,
grid_time.day, 0, grid_time.minute,
grid_time.second)
else:
grid_time = datetime(grid_time.year, grid_time.month,
grid_time.day,
hour_rounded_to_nearest_6,
grid_time.minute, grid_time.second)
if file_name is not None:
if not os.path.isfile(file_name):
raise FileNotFoundError(file_name + " not found!")
if file_name is None:
print("Download ERA Interim data...")
# ERA interim data is in pressure coordinates
# Retrieve u, v, w, and geopotential
# Geopotential is needed to convert into height coordinates
retrieve_dict = {}
retrieve_dict['stream'] = "oper"
retrieve_dict['levtype'] = "pl"
retrieve_dict['param'] = "131.128/132.128/135.128/129.128"
retrieve_dict['dataset'] = "interim"
retrieve_dict['levelist'] = ("1/2/3/5/7/10/20/30/50/70/100/125/150/" +
"175/200/225/250/300/350/400/450/500/" +
"550/600/650/700/750/775/800/825/850/" +
"875/900/925/950/975/1000")
retrieve_dict['step'] = "0"
retrieve_dict['time'] = "%02d" % hour_rounded_to_nearest_6
retrieve_dict['date'] = grid_time.strftime("%Y-%m-%d")
retrieve_dict['class'] = "ei"
retrieve_dict['grid'] = "0.75/0.75"
N = "%4.1f" % Grid.point_latitude["data"].max()
S = "%4.1f" % Grid.point_latitude["data"].min()
E = "%4.1f" % Grid.point_longitude["data"].max()
W = "%4.1f" % Grid.point_longitude["data"].min()
retrieve_dict['area'] = N + "/" + W + "/" + S + "/" + E
retrieve_dict['format'] = "netcdf"
tfile = tempfile.NamedTemporaryFile()
retrieve_dict['target'] = tfile.name
file_name = tfile.name
server = ECMWFDataServer()
server.retrieve(retrieve_dict)
time_step = 0
ERA_grid = Dataset(file_name, mode='r')
base_time = datetime.strptime(ERA_grid.variables["time"].units,
"hours since %Y-%m-%d %H:%M:%S.%f")
time_seconds = ERA_grid.variables["time"][:]
our_time = np.array([base_time + timedelta(seconds=int(x)) for x in time_seconds])
time_step = np.argmin(np.abs(base_time - grid_time))
analysis_grid_shape = Grid.fields[vel_field]['data'].shape
height_ERA = ERA_grid.variables["z"][:]
u_ERA = ERA_grid.variables["u"][:]
v_ERA = ERA_grid.variables["v"][:]
w_ERA = ERA_grid.variables["w"][:]
lon_ERA = ERA_grid.variables["longitude"][:]
lat_ERA = ERA_grid.variables["latitude"][:]
radar_grid_lat = Grid.point_latitude['data']
radar_grid_lon = Grid.point_longitude['data']
radar_grid_alt = Grid.point_z['data']
u_flattened = u_ERA[time_step].flatten()
v_flattened = v_ERA[time_step].flatten()
w_flattened = w_ERA[time_step].flatten()
the_shape = u_ERA.shape
lon_mgrid, lat_mgrid = np.meshgrid(lon_ERA, lat_ERA)
lon_mgrid = np.tile(lon_mgrid, (the_shape[1], 1, 1))
lat_mgrid = np.tile(lat_mgrid, (the_shape[1], 1, 1))
lon_flattened = lon_mgrid.flatten()
lat_flattened = lat_mgrid.flatten()
height_flattened = height_ERA[time_step].flatten()
height_flattened -= Grid.radar_altitude["data"]
u_interp = NearestNDInterpolator(
(height_flattened, lat_flattened, lon_flattened),
u_flattened, rescale=True)
v_interp = NearestNDInterpolator(
(height_flattened, lat_flattened, lon_flattened),
v_flattened, rescale=True)
w_interp = NearestNDInterpolator(
(height_flattened, lat_flattened, lon_flattened),
w_flattened, rescale=True)
u_new = u_interp(radar_grid_alt, radar_grid_lat, radar_grid_lon)
v_new = v_interp(radar_grid_alt, radar_grid_lat, radar_grid_lon)
w_new = w_interp(radar_grid_alt, radar_grid_lat, radar_grid_lon)
new_grid = deepcopy(Grid)
u_dict = {'data': u_new, 'long_name': "U from ERA-Interim", 'units': "m/s"}
v_dict = {'data': v_new, 'long_name': "V from ERA-Interim", 'units': "m/s"}
w_dict = {'data': w_new, 'long_name': "W from ERA-Interim", 'units': "m/s"}
new_grid.add_field("U_erainterim", u_dict, replace_existing=True)
new_grid.add_field("V_erainterim", v_dict, replace_existing=True)
new_grid.add_field("W_erainterim", w_dict, replace_existing=True)
# Free up memory
ERA_grid.close()
if 'tfile' in locals():
tfile.close()
return new_grid
[docs]def make_constraint_from_wrf(Grid, file_path, wrf_time,
radar_loc, vel_field=None):
"""
This function makes an initalization field based off of the u and w
from a WRF run in netCDF format.
Only u and v are used from the WRF netCDF file.
Parameters
----------
Grid: Py-ART Grid object
This is the Py-ART Grid containing the coordinates for the
analysis grid.
file_path: str
This is the path to the WRF grid
wrf_time: datetime
The timestep to derive the intialization field from.
radar_loc: tuple
The (X, Y) location of the radar in the WRF grid. The output
coordinate system will be centered around this location
and given the same grid specification that is specified
in Grid.
vel_field: str, or None
This string contains the name of the velocity field in the
Grid. None will try to automatically detect this value.
Returns
-------
Grid: Py-ART Grid object
This Py-ART Grid will contain the model u, v, and w.
"""
# Parse names of velocity field
if vel_field is None:
vel_field = pyart.config.get_field_name('corrected_velocity')
analysis_grid_shape = Grid.fields[vel_field]['data'].shape
u = np.ones(analysis_grid_shape)
v = np.ones(analysis_grid_shape)
w = np.zeros(analysis_grid_shape)
# Load WRF grid
wrf_cdf = Dataset(file_path, mode='r')
W_wrf = wrf_cdf.variables['W'][:]
V_wrf = wrf_cdf.variables['V'][:]
U_wrf = wrf_cdf.variables['U'][:]
PH_wrf = wrf_cdf.variables['PH'][:]
PHB_wrf = wrf_cdf.variables['PHB'][:]
alt_wrf = (PH_wrf+PHB_wrf)/9.81
new_grid_x = Grid.point_x['data']
new_grid_y = Grid.point_y['data']
new_grid_z = Grid.point_z['data']
# Find timestep from datetime
time_wrf = wrf_cdf.variables['Times']
ntimes = time_wrf.shape[0]
dts_wrf = []
for i in range(ntimes):
x = ''.join([x.decode() for x in time_wrf[i]])
dts_wrf.append(datetime.strptime(x, '%Y-%m-%d_%H:%M:%S'))
dts_wrf = np.array(dts_wrf)
timestep = np.where(dts_wrf == wrf_time)
if(len(timestep[0]) == 0):
raise ValueError(("Time " + str(wrf_time) + " not found in WRF file!"))
x_len = wrf_cdf.__getattribute__('WEST-EAST_GRID_DIMENSION')
y_len = wrf_cdf.__getattribute__('SOUTH-NORTH_GRID_DIMENSION')
dx = wrf_cdf.DX
dy = wrf_cdf.DY
x = np.arange(0, x_len)*dx-radar_loc[0]*1e3
y = np.arange(0, y_len)*dy-radar_loc[1]*1e3
z = np.mean(alt_wrf[timestep[0], :, :, :], axis=(0, 2, 3))
x, y, z = np.meshgrid(x, y, z)
z = np.squeeze(alt_wrf[timestep[0], :, :, :])
z_stag = (z[1:, :, :]+z[:-1, :, :])/2.0
x_stag = (x[:, :, 1:]+x[:, :, :-1])/2.0
y_stag = (y[:, 1:, :]+y[:, :-1, :])/2.0
W_wrf = np.squeeze(W_wrf[timestep[0], :, :, :])
V_wrf = np.squeeze(V_wrf[timestep[0], :, :, :])
U_wrf = np.squeeze(U_wrf[timestep[0], :, :, :])
w = griddata((z_stag, y, x), W_wrf,
(new_grid_z, new_grid_y, new_grid_x),
fill_value=0.)
v = griddata((z, y_stag, x), V_wrf,
(new_grid_z, new_grid_y, new_grid_x),
fill_value=0.)
u = griddata((z, y, x_stag), U_wrf,
(new_grid_z, new_grid_y, new_grid_x),
fill_value=0.)
u_dict = {'data': u, 'long_name': "U from WRF", 'units': "m/s"}
v_dict = {'data': v, 'long_name': "V from WRF", 'units': "m/s"}
w_dict = {'data': w, 'long_name': "W from WRF", 'units': "m/s"}
Grid.add_field("U_wrf", u_dict, replace_existing=True)
Grid.add_field("V_wrf", v_dict, replace_existing=True)
Grid.add_field("W_wrf", w_dict, replace_existing=True)
return Grid
[docs]def add_hrrr_constraint_to_grid(Grid, file_path):
"""
This function will read an HRRR GRIB2 file and create the constraining
u, v, and w fields for the model constraint
Parameters
----------
Grid: Py-ART Grid
The Py-ART Grid to use as the grid specification. The HRRR values
will be interpolated to the Grid's specficiation and added as a field.
file_path: string
The path to the GRIB2 file to load.
Returns
-------
Grid: Py-ART Grid
This returns the Py-ART grid with the HRRR u, and v fields added.
"""
if(CFGRIB_AVAILABLE is False):
raise RuntimeError(("The cfgrib optional dependency needs to be " +
"installed for the HRRR integration feature."))
the_grib = cfgrib.open_file(
file_path, filter_by_keys={'typeOfLevel': 'isobaricInhPa'})
# Load the HRRR data and tranform longitude coordinates
grb_u = the_grib.variables['u']
grb_v = the_grib.variables['v']
grb_w = the_grib.variables['w']
gh = the_grib.variables['gh']
lat = the_grib.variables['latitude'].data[:, :]
lon = the_grib.variables['longitude'].data[:, :]
lon[lon > 180] = lon[lon > 180] - 360
# Convert geometric height to geopotential height
EARTH_MEAN_RADIUS = 6.3781e6
gh = gh.data[:, :, :]
height = (EARTH_MEAN_RADIUS*gh)/(EARTH_MEAN_RADIUS-gh)
height = height - Grid.radar_altitude['data']
radar_grid_lat = Grid.point_latitude['data']
radar_grid_lon = Grid.point_longitude['data']
radar_grid_alt = Grid.point_z['data']
lat_min = radar_grid_lat.min()
lat_max = radar_grid_lat.max()
lon_min = radar_grid_lon.min()
lon_max = radar_grid_lon.max()
lon_r = np.tile(lon, (height.shape[0], 1, 1))
lat_r = np.tile(lat, (height.shape[0], 1, 1))
lon_flattened = lon_r.flatten()
lat_flattened = lat_r.flatten()
height_flattened = gh.flatten()
the_box = np.where(np.logical_and.reduce(
(lon_flattened >= lon_min, lat_flattened >= lat_min,
lon_flattened <= lon_max, lat_flattened <= lat_max)))[0]
lon_flattened = lon_flattened[the_box]
lat_flattened = lat_flattened[the_box]
height_flattened = height_flattened[the_box]
u_flattened = grb_u.data[:, :, :].flatten()
u_flattened = u_flattened[the_box]
u_interp = NearestNDInterpolator(
(height_flattened, lat_flattened, lon_flattened),
u_flattened, rescale=True)
u_new = u_interp(radar_grid_alt, radar_grid_lat, radar_grid_lon)
v_flattened = grb_v.data[:, :, :].flatten()
v_flattened = v_flattened[the_box]
v_interp = NearestNDInterpolator(
(height_flattened, lat_flattened, lon_flattened),
v_flattened, rescale=True)
v_new = v_interp(radar_grid_alt, radar_grid_lat, radar_grid_lon)
w_flattened = grb_w.data[:, :, :].flatten()
w_flattened = w_flattened[the_box]
w_interp = NearestNDInterpolator(
(height_flattened, lat_flattened, lon_flattened),
w_flattened, rescale=True)
w_new = w_interp(radar_grid_alt, radar_grid_lat, radar_grid_lon)
new_grid = deepcopy(Grid)
u_dict = {'data': u_new, 'long_name': "U from HRRR ", 'units': "m/s"}
v_dict = {'data': v_new, 'long_name': "V from HRRR ", 'units': "m/s"}
w_dict = {'data': w_new, 'long_name': "W from HRRR ", 'units': "m/s"}
new_grid.add_field("U_hrrr", u_dict, replace_existing=True)
new_grid.add_field("V_hrrr", v_dict, replace_existing=True)
new_grid.add_field("W_hrrr", w_dict, replace_existing=True)
# Free up memory
del grb_u, grb_v, grb_w, lat, lon
del the_grib
gc.collect()
return new_grid