Source code for pydda.initialization.wind_fields

import numpy as np
import pyart
import gc
import os
import tempfile

# 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 RegularGridInterpolator, interp1d, griddata
from scipy.interpolate import NearestNDInterpolator
from copy import deepcopy



[docs]def make_initialization_from_era_interim(Grid, file_name=None, vel_field=None, dest_era_file=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. 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. 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. dest_era_file: If this is not None, PyDDA will save the interpolated grid into this file. 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_3 = int(3 * round(float(grid_time.hour)/3)) if hour_rounded_to_nearest_3 == 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_3, 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'] = "%d" % grid_time.hour 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" if dest_era_file is not None: retrieve_dict['target'] = dest_era_file file_name = dest_era_file else: tfile = tempfile.NamedTemporaryFile() retrieve_dict['target'] = tfile.name file_name = tfile.name server = ECMWFDataServer() server.retrieve(retrieve_dict) 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) # Free up memory ERA_grid.close() if 'tfile' in locals(): tfile.close() return u_new, v_new, w_new
[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_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. The shape will be the same shape as the fields in Grid and will correspond to the same x, y, and z locations as in Grid. v: 3D ndarray The initialization v field. The shape will be the same shape as the fields in Grid and will correspond to the same x, y, and z locations as in Grid. w: 3D ndarray The initialization w field. The shape will be the same shape as the fields in Grid and will correspond to the same x, y, and z locations as 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) # 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. The shape will be the same shape as the fields in Grid and will correspond to the same x, y, and z locations as in Grid. """ 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 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) del grb_u, grb_v, grb_w, lat, lon del the_grib gc.collect() return u_new, v_new, w_new