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extract_profile.py
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extract_profile.py
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"""
Extract a vertical atmospheric profile
from a WRF data product.
Units are all SI.
Angle in degrees [0, 360].
See argmument help by running with '-h' = '--help'
Install dependencies in a conda environment:
conda create --name wrf
conda activate wrf
conda install pip
pip install astropy pandas scipy numpy
pip install wrf-python netcdf4
"""
__author__ = "Hadrien A R Devillepoix"
__license__ = "MIT"
__version__ = "1.0"
import os
import warnings
from netCDF4 import Dataset
from wrf import getvar
import wrf
from astropy.time import Time
import numpy as np
from numpy import linalg as LA
from scipy.interpolate import griddata
import pandas as pd
def density_from_pressure(temperature, pressure, RH):
"""returns atmospheric density, (kg/m3)
for a single point given:
Pressure (Pascals, multiply mb by 100 to get Pascals)
temperature ( deg K)
RH (from 0 to 1 as fraction) """
# R = specific gas constant , J/(kg*degK) = 287.05 for dry air
Rd = 287.05
# http://www.baranidesign.com/air-density/air-density.htm
# http://wahiduddin.net/calc/density_altitude.htm
# Evaporation into the Atmosphere, Wilfried Brutsaert, p37
# saturation vapor pressure is a polynomial developed by Herman Wobus
e_so = 6.1078
c0 = 0.99999683
c1 = -0.90826951e-2
c2 = 0.78736169e-4
c3 = -0.61117958e-6
c4 = 0.43884187e-8
c5 = -0.29883885e-10
c6 = 0.21874425e-12
c7 = -0.17892321e-14
c8 = 0.11112018e-16
c9 = -0.30994571e-19
p = (c0 + temperature*(
c1 + temperature*(
c2 + temperature*(
c3 + temperature*(
c4 + temperature*(
c5 + temperature*(
c6 + temperature*(
c7 + temperature*(
c8 + temperature*(
c9))))))))))
sat_vp = e_so / p**8
Pv = sat_vp * RH
density = (pressure / (Rd * temperature)) * (1 - (0.378 * Pv / pressure))
return density
def WindDataExtraction(WindFileName, t0):
# Interpolates temporally (with fixed lat/lon/pres?)
from wrf import to_np
if isinstance(t0, np.ndarray): t0 = t0[0]
WindFile = Dataset(WindFileName)
times_all = wrf.extract_times(WindFile, timeidx=wrf.ALL_TIMES)
times_jd = np.array([Time(str(t), format='isot', scale='utc').jd for t in times_all])
idx_after = np.searchsorted(times_jd, t0)
idx_before = idx_after - 1
interp_factor = (t0 - times_jd[idx_before]) / (times_jd[idx_after] - times_jd[idx_before])
if interp_factor < 0 or interp_factor > 1:
print('WindWarning: The darkflight time is ouside the bounds of WindData' \
' by {0:.3f} times!'.format(interp_factor))
WindArray = []
for i in [idx_before, idx_after]:
hei_3d = np.array([to_np(getvar(WindFile,'z',timeidx=i))]) #[1,z,y,x]
NumberLevels = np.shape(hei_3d)[1] # Number heights
lat_3d = np.array([np.stack([to_np(getvar(WindFile,'lat',timeidx=i))]*NumberLevels, axis=0)]) #[1,z,y,x]
lon_3d = np.array([np.stack([to_np(getvar(WindFile,'lon',timeidx=i))]*NumberLevels, axis=0)]) #[1,z,y,x]
wen_3d = to_np(getvar(WindFile,'uvmet',timeidx=i)) #[2,z,y,x]
wu_3d = np.array([to_np(getvar(WindFile,'wa',timeidx=i))]) #[1,z,y,x]
temp_3d = np.array([to_np(getvar(WindFile,'tk',timeidx=i))]) #[1,z,y,x]
pres_3d = np.array([to_np(getvar(WindFile,'p',timeidx=i))]) #[1,z,y,x]
rh_3d = np.array([to_np(getvar(WindFile,'rh',timeidx=i))]) #[1,z,y,x]
# Construct WindArray = [lat,lon,hei,we,wn,wu,temp,pres,rh]
WindArray.append( np.vstack((lat_3d, lon_3d, hei_3d, wen_3d,
wu_3d, temp_3d, pres_3d, rh_3d)) )
WindArray = (1 - interp_factor) * WindArray[0] + interp_factor * WindArray[1]
return WindArray
def WRF3D(WindArray, lat, lon, hei):
# Interpolates spacially
# Find xy positions of the lat/lon
ang_dist2 = (WindArray[0,0] - lat)**2 + (WindArray[1,0] - lon)**2
min_index = np.argmin(ang_dist2); ncol = WindArray.shape[3]
xid = min_index % ncol; yid = min_index // ncol
lat_var = WindArray[0,0,yid-1:yid+2,xid-1:xid+2] #[3,3]
lon_var = WindArray[1,0,yid-1:yid+2,xid-1:xid+2] #[3,3]
hei_var = WindArray[2,:,yid-1:yid+2,xid-1:xid+2] #[z,3,3]
# Find the variable at a certain altitude as a 2D array [3,3] #linear interpolation!
interp_horiz = lambda entry_no: wrf.interplevel(
field3d=WindArray[entry_no,:,yid-1:yid+2,xid-1:xid+2],
vert=hei_var, desiredlev=hei, missing=np.nan).data #<---RuntimeWarning originates from here
# 2D interpolate [1,]
latlon = np.vstack((lat_var.flatten(), lon_var.flatten())).T
interp2pt = lambda entry_no: griddata(latlon,
interp_horiz(entry_no).flatten(), np.array([lat,lon]))
with warnings.catch_warnings():
warnings.simplefilter("ignore")
we = interp2pt(3)[0] # Wind east [m/s]
wn = interp2pt(4)[0] # Wind north [m/s]
wu = interp2pt(5)[0] # Wind up [m/s]
tk = interp2pt(6)[0] # Temperature [K]
pr = interp2pt(7)[0] # Pressure [Pa]
rh = interp2pt(8)[0] # Relative humidity []
wind_horizontal = LA.norm([we, wn])
wind_direction = (-np.rad2deg(np.arctan2(wn, we)) + 270 ) % 360.
rho_a = density_from_pressure(tk, pr, rh)
if np.isnan(rho_a):
return {}
#Wind_ENU = np.vstack((we, wn, wu))
return {"height": hei,
"temperature": tk,
"pressure": pr,
"relative_humidity": rh,
"wind_horizontal": wind_horizontal,
"wind_direction": wind_direction,
"wind_east": we,
"wind_north": wn,
"wind_up": wu,
"density": rho_a}
def main(wrf_file, ref_lat, ref_lon, ref_time):
ref_time = Time(ref_time)
# read the data
WindArray = WindDataExtraction(wrf_file, ref_time.jd)
wind_dics = []
# iterate of a range of heights
for hei in np.arange(0., 32e3, 100.):
wind_dics += [WRF3D(WindArray, ref_lat, ref_lon, hei)]
# dump interpolated results to a dataframe
df = pd.DataFrame.from_records(wind_dics).dropna()
print(df)
ofname = os.path.join(os.path.dirname(wrf_file),
f'vertical_profile_{os.path.basename(wrf_file)}_{ref_time}_{ref_lat:.5f}_{ref_lon:.5f}.csv')
df.to_csv(ofname, index=False)
def parse_simple_config(ifile):
from configparser import ConfigParser
from itertools import chain
parser = ConfigParser()
with open(ifile) as lines:
# add a dummy section header because ConfigParser can't bloody deal with a simple config file
lines = chain(("[dummy_section]",), lines)
parser.read_file(lines)
return parser['dummy_section']
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='Extract vertical profile from WRF. Either provide a WRF bash config file, OR set lat/long/time.')
parser.add_argument("-w", "--WRF", type=str, required=True, help="path to WRF file")
parser.add_argument("-i", "--ifile", type=str, help="path to WRF config file")
parser.add_argument("-lat", type=str, help="latitude (decimal degrees, WGS84)")
parser.add_argument("-lon", type=str, help="longitude (decimal degrees, WGS84)")
parser.add_argument("-time", type=str, help="UTC time (ISO 8601 string, e.g. 2020-08-14T18:30:00)")
args = parser.parse_args()
if args.ifile:
if not os.path.isfile(args.ifile):
print(f'path to WRF config file invalid ({args.ifile})')
exit(1)
conf = parse_simple_config(args.ifile)
ref_time = conf['EVENT_TIME'].replace('"', '')
ref_lon = float(conf['EVENT_LON'].replace('"', ''))
ref_lat = float(conf['EVENT_LAT'].replace('"', ''))
else:
if not args.time or not args.lon or not args.lat:
print()
parser.print_help()
exit(1)
ref_time = args.time
ref_lon = float(args.lon)
ref_lat = float(args.lat)
wrf_file = args.WRF
if not os.path.isfile(wrf_file):
print(f'path to WRF file invalid ({wrf_file})')
exit(1)
main(wrf_file, ref_lat, ref_lon, ref_time)