import numpy as np
import pyart
# We want cfgrib to be an optional dependency to ensure Windows compatibility
try:
import cfgrib
CFGRIB_AVAILABLE = True
except:
CFGRIB_AVAILABLE = False
from netCDF4 import Dataset
from datetime import datetime
from scipy.interpolate import RegularGridInterpolator, interp1d, griddata
from scipy.interpolate import NearestNDInterpolator
from copy import deepcopy
[docs]def make_constant_wind_field(Grid, wind=(0.0, 0.0, 0.0), vel_field=None):
"""
This function makes a constant wind field given a wind vector.
This function is useful for specifying the intialization arrays
for get_dd_wind_field.
Parameters
==========
Grid: Py-ART Grid object
This is the Py-ART Grid containing the coordinates for the analysis
grid.
wind: 3-tuple of floats
The 3-tuple specifying the (u,v,w) of the wind field.
vel_field: String
The name of the velocity field. None will automatically
try to detect this field.
Returns
=======
u: 3D float array
Returns a 3D float array containing the u component of the wind field.
The shape will be the same shape as the fields in Grid.
v: 3D float array
Returns a 3D float array containing the v component of the wind field.
The shape will be the same shape as the fields in Grid.
w: 3D float array
Returns a 3D float array containing the u component of the wind field.
The shape will be the same shape as the fields in Grid.
"""
# 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 = wind[0]*np.ones(analysis_grid_shape)
v = wind[1]*np.ones(analysis_grid_shape)
w = wind[2]*np.ones(analysis_grid_shape)
u = np.ma.filled(u, 0)
v = np.ma.filled(v, 0)
w = np.ma.filled(w, 0)
return u, v, w
[docs]def make_wind_field_from_profile(Grid, profile, vel_field=None):
"""
This function makes a 3D wind field from a sounding.
This function is useful for using sounding data as an initialization
for get_dd_wind_field.
Parameters
==========
Grid: Py-ART Grid object
This is the Py-ART Grid containing the coordinates for the analysis
grid.
profile: PyART HorizontalWindProfile
This is the HorizontalWindProfile of the sounding
wind: 3-tuple of floats
The 3-tuple specifying the (u,v,w) of the wind field.
vel_field: String
The name of the velocity field in Grid. None will automatically
try to detect this field.
Returns
=======
u: 3D float array
Returns a 3D float array containing the u component of the wind field.
The shape will be the same shape as the fields in Grid.
v: 3D float array
Returns a 3D float array containing the v component of the wind field.
The shape will be the same shape as the fields in Grid.
w: 3D float array
Returns a 3D float array containing the u component of the wind field.
The shape will be the same shape as the fields in Grid.
"""
# 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)
u_back = profile.u_wind
v_back = profile.v_wind
z_back = profile.height
u_interp = interp1d(
z_back, u_back, bounds_error=False, fill_value='extrapolate')
v_interp = interp1d(
z_back, v_back, bounds_error=False, fill_value='extrapolate')
u_back2 = u_interp(np.asarray(Grid.z['data']))
v_back2 = v_interp(np.asarray(Grid.z['data']))
for i in range(analysis_grid_shape[0]):
u[i] = u_back2[i]
v[i] = v_back2[i]
u = np.ma.filled(u, 0)
v = np.ma.filled(v, 0)
w = np.ma.filled(w, 0)
return u, v, w
[docs]def make_test_divergence_field(Grid, wind_vel, z_ground, z_top, radius,
back_u, back_v, x_center, y_center):
"""
This function makes a test field with wind convergence at the surface
and divergence aloft.
This function makes a useful test for the mass continuity equation.
Parameters
----------
Grid: Py-ART Grid object
This is the Py-ART Grid containing the coordinates for the analysis
grid.
wind_vel: float
The maximum wind velocity.
z_ground: float
The bottom height where the maximum convergence occurs
z_top: float
The height where the maximum divergence occurrs
back_u: float
The u component of the wind outside of the area of convergence.
back_v: float
The v component of the wind outside of the area of convergence.
x_center: float
The X-coordinate of the center of the area of convergence in the
Grid's coordinates.
y_center: float
The Y-coordinate of the center of the area of convergence in the
Grid's coordinates.
Returns
-------
u_init, v_init, w_init: ndarrays of floats
Initial U, V, W field
"""
x = Grid.point_x['data']
y = Grid.point_y['data']
z = Grid.point_z['data']
theta = np.arctan2(x - x_center, y - y_center)
phi = np.pi*((z - z_ground)/(z_top - z_ground))
r = np.sqrt(np.square(x - x_center) + np.square(y - y_center))
u = wind_vel*(r/radius)**2*np.cos(phi)*np.sin(theta)*np.ones(x.shape)
v = wind_vel*(r/radius)**2*np.cos(phi)*np.cos(theta)*np.ones(x.shape)
w = np.zeros(x.shape)
u[r > radius] = back_u
v[r > radius] = back_v
u = np.ma.array(u)
v = np.ma.array(v)
w = np.ma.array(w)
return u, v, w
[docs]def make_background_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. Only u and v are used from the WRF 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
-------
u: 3D ndarray
The initialization u field.
v: 3D ndarray
The initialization v field.
w: 3D ndarray
The initialization w field.
"""
# 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.)
return u, v, w
def make_intialization_from_hrrr(Grid, file_path):
"""
This function will read an HRRR GRIB2 file and return initial guess
u, v, and w fields from the model
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.Dataset.from_path(
file_path, filter_by_keys={'typeOfLevel': 'isobaricInhPa'})
# Load the HRR 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_v.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)
return u_new, v_new, w_new