def test(self):
from netCDF4 import Dataset as NetCDF
timeidx = 0
in_wrfnc = NetCDF(wrf_in)
if (varname == "interplevel"):
ref_ht_850 = _get_refvals(referent, "interplevel", repeat, multi)
hts = getvar(in_wrfnc, "z", timeidx=timeidx)
p = getvar(in_wrfnc, "pressure", timeidx=timeidx)
hts_850 = interplevel(hts, p, 850)
nt.assert_allclose(to_np(hts_850), ref_ht_850)
elif (varname == "vertcross"):
ref_ht_cross = _get_refvals(referent, "vertcross", repeat, multi)
hts = getvar(in_wrfnc, "z", timeidx=timeidx)
p = getvar(in_wrfnc, "pressure", timeidx=timeidx)
pivot_point = CoordPair(hts.shape[-1] // 2, hts.shape[-2] // 2)
ht_cross = vertcross(hts, p, pivot_point=pivot_point, angle=90.)
nt.assert_allclose(to_np(ht_cross), ref_ht_cross, rtol=.01)
elif (varname == "interpline"):
ref_t2_line = _get_refvals(referent, "interpline", repeat, multi)
t2 = getvar(in_wrfnc, "T2", timeidx=timeidx)
pivot_point = CoordPair(t2.shape[-1] // 2, t2.shape[-2] // 2)
t2_line1 = interpline(t2, pivot_point=pivot_point, angle=90.0)
nt.assert_allclose(to_np(t2_line1), ref_t2_line)
elif (varname == "vinterp"):
# Tk to theta
fld_tk_theta = _get_refvals(referent, "vinterp", repeat, multi)
fld_tk_theta = np.squeeze(fld_tk_theta)
tk = getvar(in_wrfnc, "temp", timeidx=timeidx, units="k")
interp_levels = [200, 300, 500, 1000]
field = vinterp(in_wrfnc,
field=tk,
vert_coord="theta",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
tol = 5 / 100.
atol = 0.0001
field = np.squeeze(field)
nt.assert_allclose(to_np(field), fld_tk_theta, tol, atol)
def variables(ff, t):
""" air density & qc for lwp"""
z = getvar(ff, "z", timeidx=t, units="m")
p = getvar(ff, "pressure", timeidx=t) #[hPa]
tk = getvar(ff, "tk", timeidx=t) #[K]
rho = p * 100.0 / (tk * 287.0)
del tk, p #[kg m-3]
qc = getvar(ff, "QCLOUD", timeidx=t)
qc *= 1.e3 #[g kg-1]
rhoxqc = rho * qc
del rho, qc
""" for CTT, Max.dBZ, 500 m W, 500 m Wind vel. """
dbz = getvar(ff, "REFL_10CM", timeidx=t) #radar reflectivity
ctt = getvar(ff, "ctt", timeidx=t, units="degC") #Cloud top T
rh = getvar(ff, "rh", timeidx=t) #relative humidity %
wa = getvar(ff, "wa", timeidx=t, units="m s-1")
ua = getvar(ff, "ua", timeidx=t, units="m s-1")
va = getvar(ff, "va", timeidx=t, units="m s-1")
w500 = interplevel(wa, z, 500.0)
del wa #500 m height
u500 = interplevel(ua, z, 500.0)
del ua
v500 = interplevel(va, z, 500.0)
del va
vel_500 = (u500**2 + v500**2)**0.5
del u500, v500
""" 0:MCAPE, 1:MCIN, 2:LCL, 3:LFC """
thermo = g_cape.get_2dcape(ff, timeidx=t)
lcl = thermo[2, :, :]
del thermo #m
#td = getvar(ff, "td", timeidx=t, units="K") #Dew point T
#print("Get all variables!")
return rhoxqc, z, dbz, ctt, rh, lcl, w500, vel_500
def plot_interact(tindex, level):
ua_interp = interplevel(ua, z, level)
va_interp = interplevel(va, z, level)
wa_interp = interplevel(wa, z, level)
fig1, ax1 = plt.subplots(figsize=(12, 10))
cb = ax1.contourf(z['west_east'].values,
z['south_north'].values,
wa_interp.isel(Time=tindex).values,levels=np.arange(-30,30,0.5),cmap='Spectral_r')
Q = ax1.quiver(z['west_east'].values,
z['south_north'].values,
ua_interp.isel(Time=tindex).values,
va_interp.isel(Time=tindex).values,pivot='middle',color='black',
units='width',width=0.0007,headwidth=10)
qk = ax1.quiverkey(Q, 0.92, .95, 5, r'$5 \frac{m}{s}$', labelpos='E',
coordinates='figure')
cb = plt.colorbar(cb, shrink=0.5, title='Vertical wind (m/s)')
ax1.set_title('Vertical motion (m/s) and winds (m/s) at time='+str(tindex)+' and level='+str(level))
plt.tight_layout()
plt.show()
def chi_from_wrf(file, time_index):
ncfile = Dataset(file, mode='r')
td = getvar(ncfile, "td", timeidx=time_index)
tc = getvar(ncfile, "tc", timeidx=time_index)
p = getvar(ncfile, "pressure", timeidx=time_index)
t850, t700 = [interplevel(tc, p, level).data for level in [850, 700]]
td850 = interplevel(td, p, 850).data
chaines = chi(t850, td850, t700)
return chaines
def calculate_icing_wrf(ncfile, lwc, lwc_threshold):
# levels(hPa): 0=100 1=150 2=200 3=250 4=300 5=350 6=400 7=450 8=500 9=550
# 10=600 11=650 12=700 13=750 14=800 15=850 16=900 17=950 18=1000
levels_list = [
100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750,
800, 850, 900, 950, 1000
]
total_levels = np.size(levels_list, 0)
################################################
# get temperature and relative humidity
################################################
temperature_var = getvar(ncfile, "tc")
rh_var = getvar(ncfile, "rh")
# Get the pressure for interpolating the variables to the correct pressure level
p = getvar(ncfile, "pressure")
################################################
# calculate icing probabilities values
################################################
grid_lats = rh_var.shape[1]
grid_lons = rh_var.shape[2]
probability_grid = np.ones([total_levels, grid_lats, grid_lons])
for current_level in range(total_levels):
# Interpolate the variables for the current pressure level
current_pressure = levels_list[current_level]
temperature_level = np.array(
interplevel(temperature_var, p, current_pressure))
rh_level = np.array(interplevel(rh_var, p, current_pressure))
# Make the lwc values binary: 0 for less than the threshold and 1 for above it
lwc_level = np.squeeze(lwc[current_level, :, :])
lwc_level = np.ceil(lwc_level - lwc_threshold)
for lats_idx in range(grid_lats):
for lons_idx in range(grid_lons):
RH_index = (np.abs(RH_probabilities[:, 0] -
rh_level[lats_idx, lons_idx])).argmin()
RH_value = RH_probabilities[RH_index, 1]
temperature_index = (
np.abs(temperature_probabilities[:, 0] -
temperature_level[lats_idx, lons_idx])).argmin()
temperature_value = temperature_probabilities[
temperature_index, 1]
probability_grid[current_level, lats_idx,
lons_idx] = lwc_level[
lats_idx,
lons_idx] * temperature_value * RH_value
return probability_grid
def calculate_lwc_wrf(ncfile):
# levels(hPa): 0=100 1=150 2=200 3=250 4=300 5=350 6=400 7=450 8=500 9=550
# 10=600 11=650 12=700 13=750 14=800 15=850 16=900 17=950 18=1000
levels_list = [
100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750,
800, 850, 900, 950, 1000
]
total_levels = np.size(levels_list, 0)
################################################
# get Virtual temperature, and CLWC.
# In the ecmwf version we needed specific humidity and temperature to
# calculate the virtual temperature. in WRF it is already calculated!
################################################
virtual_temperature_var = getvar(ncfile, "tv")
clwc_var = getvar(ncfile, "QCLOUD")
# Get the pressure for interpolating the variables to the correct pressure level
p = getvar(ncfile, "pressure")
################################################
# calculate liquid water content in g/m3 values
################################################
lwc_grid = np.ones([total_levels, clwc_var.shape[1], clwc_var.shape[2]])
Rd = 2.87 / 1000 # m3*mb/Kg*K -> m3*mb/g*K
for current_level in range(total_levels):
current_pressure = levels_list[current_level]
# Interpolate the variables for the current pressure level
virtual_temperature_grid = np.array(
interplevel(virtual_temperature_var, p, current_pressure))
clwc_grid = np.array(interplevel(clwc_var, p, current_pressure))
# Calculate LWC
air_density_grid = current_pressure / (Rd * virtual_temperature_grid)
lwc_grid[current_level] = clwc_grid * air_density_grid
################################################
# Debug messages
# print("Current Level: " + str(current_level))
# print("Max Air Density: " + str(air_density_grid[current_level].max()))
# print("Max CLWC: " + str(clwc_grid.max()))
# print("Max LWC: " + str(lwc_grid[current_level].max()))
# print"========================================="
################################################
return lwc_grid
def interp_z(ff, time, varname, speedup=1, cores=7):
'''
speedup 1: thread, It's may be faster.
'''
# global variables
zz = getvar(ff, "z", timeidx=time)
var = getvar(ff, str(varname), timeidx=time)
def wrf_interp(level):
result = interplevel(var, zz, level)
return result
(lev, nk) = delta_z() # dz for interpolation
ny = np.int32(len(var[0, :, 0]))
nx = np.int32(len(var[0, 0, :]))
var_m = np.zeros(shape=(nk, ny, nx), dtype=np.float32)
print("Time index: {}, Variable: {}".format(time, str(varname)))
print("After Interpolation (nk,nj,ni): \033[93m{}\033[0m".format(
var_m.shape))
if speedup == 1:
with ThreadPoolExecutor(max_workers=cores) as executor:
for k in range(nk):
#var_m[k,:,:] = executor.submit(interplevel,var,zz,lev[k]).result()
var_m[k, :, :] = executor.submit(wrf_interp, lev[k]).result()
else:
for k in range(nk):
var_m[k, :, :] = interplevel(var, zz, lev[k])
var_m = np.where(np.isnan(var_m), fill_value, var_m)
return var_m
def interp_data(fieldname, field, Z_AGL):
interp_levels = [
0, 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500,
6000, 6500, 7000
]
#interp_levels = [2000]
vars = {}
for level in interp_levels:
varname = fieldname + "_" + str(level) + "m_AGL"
if level == 0:
vars[varname] = field[:, 0, :, :]
else:
#
# interpolate to the requested level
#
vars[varname] = wrf.interplevel(field[:, :, :, :],
Z_AGL[:, :, :, :],
level,
meta=False)
return vars
def interp_data_concat(field, Z_AGL):
interp_levels = [
0, 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500,
6000, 6500, 7000
]
count = 0
for level in interp_levels:
count = count + 1
if level == 0:
interp_field = field[:, 0, :, :]
interp_field = interp_field[:, np.newaxis, :, :]
else:
#
# interpolate to the requested level
#
interp_field = wrf.interplevel(field[:, :, :, :],
Z_AGL[:, :, :, :],
level,
meta=False)
interp_field = interp_field[:, np.newaxis, :, :]
if count == 1:
interp_3d = interp_field
print(interp_3d.shape)
else:
interp_3d = np.concatenate((interp_3d, interp_field), axis=1)
print(interp_3d.shape)
return interp_3d
def regrid_and_sum(flx_ds, _boo=True):
ds_rl = flx_ds['CONC'].where(_boo).sum(co.RL).load()
# %%
import wrf
topo_ = (ds_rl[co.ZM] - ds_rl[co.TOPO])
ds_g = (topo_ / 500).round() * 500
# %%
res = wrf.interplevel(ds_rl.reset_coords(drop=True), ds_g, ds_g[co.ZM])
res = res.rename({'level': co.ZM})
res.name = 'CONC'
# %%
res_sum = res.sum([co.R_CENTER, co.TH_CENTER])
return res_sum
def WRF3D(WindArray, Pos_LLH):
# Interpolates spatially
# Assign the lat/lon/hei and find height variation in the model
[[lat], [lon],
[hei]] = [np.rad2deg(Pos_LLH[0]),
np.rad2deg(Pos_LLH[1]), Pos_LLH[2]]
# 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: 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) # Wind east [m/s]
wn = interp2pt(4) # Wind north [m/s]
wu = interp2pt(5) # Wind up [m/s]
tk = interp2pt(6) # Temperature [K]
pr = interp2pt(7) # Pressure [Pa]
rh = interp2pt(8) # Relative humidity []
# Compare:
rho_a = density_from_pressure(tk, pr, rh)
if np.isnan(rho_a): [hei, we, wn, wu, rho_a, tk] = WRF_history[-1]
Wind_ENU = np.vstack((we, wn, wu))
# Save the variables
WRF_history.append(np.vstack((hei, Wind_ENU, rho_a, tk)))
return Wind_ENU, rho_a, tk
def interpolate_vert(ncfile, start_lat, end_lat, start_lon, end_lon, var, time,
start_p, end_p, interval_p):
startpoint = CoordPair(lat=start_lat, lon=start_lon)
endpoint = CoordPair(lat=end_lat, lon=end_lon)
height = getvar(ncfile, 'height')
hgt = getvar(ncfile, 'HGT')
p = getvar(ncfile, 'pressure', timeidx=time)
f = getvar(ncfile, var, timeidx=time)
p_height = p[:, 0, 0]
bool, start_layer, end_layer = get_pressure_layer(p_height, start_p, end_p)
p_level = np.mgrid[p_height[start_layer].values:p_height[end_layer].
values:complex(str(end_layer - start_layer + 1) + 'j')]
f_vert = vertcross(f,
p,
wrfin=ncfile,
levels=p_level,
start_point=startpoint,
end_point=endpoint,
latlon=True)
if var == 'wa':
f_vert = f_vert * 200
print(f_vert)
#处理插值数据的维度
p_vert_list = f_vert.coords['vertical'].values
print("插值的压力为：", end='')
print(p_vert_list)
lon_vert_list, lat_vert_list = [], []
for i in range(len(f_vert.coords['xy_loc'])):
s = str(f_vert.coords['xy_loc'][i].values)
lon_vert_list.append(float(s[s.find('lon=') + 4:s.find('lon=') + 16]))
lat_vert_list.append(float(s[s.find('lat=') + 4:s.find('lat=') + 16]))
lon_vert_list = np.array(lon_vert_list)
lat_vert_list = np.array(lat_vert_list)
lon_vert_list = np.around(lon_vert_list, decimals=2)
lat_vert_list = np.around(lat_vert_list, decimals=2)
h_vert_list = []
h2h = height - hgt
for i in range(end_p, start_p - interval_p, -interval_p):
h_vert_list.append(float(np.max(interplevel(h2h, p, i)).values))
print("对应的高度为：", end='')
print(h_vert_list)
lat_vert_list = np.mgrid[lat_vert_list[0]:lat_vert_list[-1]:complex(
str(len(lat_vert_list)) + 'j')]
lon_vert_list = np.mgrid[lon_vert_list[0]:lon_vert_list[-1]:complex(
str(len(lon_vert_list)) + 'j')]
return f_vert, lat_vert_list, lon_vert_list, p_vert_list, h_vert_list
def interp_4d(ipvar, wrf_hgts, out_hgts):
""" Interpolates vertically to heights specified by
out_hgts using the interplevel function from wrf-python
-----------------------------------------------------
In: 4D field of variable to interpolate (ipvar)
Heights of the WRF simulation (wrf_hgts)
Heights to interpolate to (out_hgts)
-----------------------------------------------------
Out: Interpolated 4D field (out)
--------------------------------------
"""
shape = list(ipvar.shape)
shape[1] = len(out_hgts)
out = np.empty(shape, ipvar.dtype)
for k, out_hgt in enumerate(out_hgts):
out[:, k, :, :] = wrf.interplevel(ipvar, wrf_hgts, out_hgt)
return (out)
def wrf_interp(data_AGL, data_var):
"""Function to compute interpolation using wrf-python.
Args:
data_AGL (Xarray dask array): Data of heights above ground level.
data_var (Xarray dask array): Data of variable for interpolation.
Returns:
Data interpolated onto four fixed heights (1, 3, 5, and 7 km).
"""
import os
os.environ[
"PROJ_LIB"] = "/glade/work/molina/miniconda3/envs/python-tutorial/share/proj/"
import wrf
return (wrf.interplevel(data_var.squeeze(), data_AGL.squeeze(),
[1000, 3000, 5000, 7000]).expand_dims("Time"))
def from_agl_to_asl(
ds,
ds_var='conc_norm',
delta_z=500,
z_top=15000,
ds_var_name_out=None
):
log.ger.warning(
f'this will only work if ds z levels are constant')
import wrf
t_list = [co.ZM, co.R_CENTER, co.TH_CENTER]
d3d = ds[ds_var] # .sum( [ co.RL ] )
d3d_attrs = d3d.attrs
d3d = d3d.transpose(
co.RL, *t_list, transpose_coords=True
)
dz = d3d[co.TOPO] + np.round(d3d[co.ZM] / delta_z) * delta_z
d3d = d3d.reset_coords(drop=True)
dz = dz.transpose(*t_list, transpose_coords=True)
dz = dz.reset_coords(drop=True)
# %%
# print( d3d.shape )
# print( dz.shape )
# %%
z_lev = np.arange(delta_z / 2, z_top, delta_z)
da_interp = wrf.interplevel(d3d, dz, z_lev)
da_reinterp = da_interp.rename(level=co.ZM)
# %%
ds_chop = ds.isel({co.ZM: slice(0, len(da_reinterp[co.ZM]))})
for coord in list(ds.coords):
da_reinterp = da_reinterp.assign_coords(
**{coord: ds_chop[coord]})
if ds_var_name_out is not None:
da_reinterp.name = ds_var_name_out
# we do this in order to avoid the problem of setting attributes
# to none that cannot be saved using to netcdf.
da_reinterp.attrs = d3d_attrs
ds_reinterp = da_reinterp.to_dataset()
# todo: check that concentrations are the same after resampling
return ds_reinterp
def interp_to_isosurface_worker(ds: xr.Dataset, *, isovalues, space_dims,
**kwargs) -> xr.DataArray:
'''
Return an xr.DataArray with a block of data interpolated to an isosurface.
Input must be xr.Dataset object that is not lazy and not chunked.
Input dataset will have two variables with names "variable" and "level_variable"
the two arrays needed are passed as a dataset as xr.map_blocks cannot take a datarray as kwarg
Lazy/Dasky xr.DataArray objects do not support data assignment with .loc
like xr.DataArrays with numpy data.
'''
# make final data structure to populate with data
final = interp_to_isosurface_meta_template(ds, isovalues)
# record initial axis order; for reshaping back to initial before return
final_dims = final.variable.dims
ordered_dims = list(final_dims)
z = final.camps.z.name
ordered_dims.remove(z)
ordered_dims.insert(
0, z
) # ordered dims now has z as left-most axis for use with wrf interplevel
# go to working space shape
ds = ds.transpose(*ordered_dims) # input
final = final.transpose(*ordered_dims) # output
# order dims for work with interplevel
other_dims = [dim for dim in ds.dims if dim not in space_dims]
other_dim_arrays = [ds[dim].data for dim in other_dims]
# iterate over all the space sections applying interplevel
for dim_values in product(*other_dim_arrays):
loc = {k: v for k, v in zip(other_dims, dim_values)}
final.loc[loc] = interplevel(ds.variable.loc[loc],
ds.level_variable.loc[loc],
isovalues,
meta=False,
**kwargs)
# go back to initial shape and axis order
final = final.transpose(*final_dims)
return final
def main(folder, wrffolder, append, sourceid, plotmap, plotcurves, plotcontour,
plotcontourf, plotbarbs, resol):
#z_unit = 'km'
z_unit = 'dm'
z_unitStr = 'dam'
#u_unit = 'm s-1'
u_unit = 'kt'
w_unit = u_unit
w_unitStr = 'm/s'
w_unitStr = 'kt'
p_unit = 'hPa'
df_obs = pd.read_csv(folder + sourceid + '_json.txt', delimiter='\t')
df_obs['time'] = pd.to_datetime(df_obs['CurrentTime'], unit='s')
df_obs['air_temperature'] = df_obs['Temperature']
df_obs['pressure'] = df_obs['Barometric Pressure']
df_obs['wind_speed'] = df_obs[
'Wind Speed'] * 1.852 / 3.6 # Convert from knots to m/s
if z_unit == "km":
feetScale = 0.3048 / 1000
elif z_unit == "m":
feetScale = 0.3048
elif z_unit == "dm":
feetScale = 0.3048 / 10
df_obs['z'] = df_obs['Alt'].to_numpy(
) * feetScale # Convert from feet to decameters (dam)
# Get data from WRF file
i_domain = 10
isOutside = True
while isOutside:
if i_domain < 0:
print('Parts of the flightpath for ' + str(sourceid) +
' is not inside the solution domain')
break
i_domain -= 1
try:
ncfile = Dataset(wrffolder + '/wrfout_d0' + str(i_domain) + '.nc')
except:
continue
fields = ['T', 'wind_speed', 'wind_from_direction']
lat_e = df_obs['Latitude'].to_numpy()
lon_e = df_obs['Longitude'].to_numpy()
z_e = df_obs['z'].to_numpy()
time_e = df_obs['time'].to_numpy()
dummy = pd.DataFrame(
{'time': wrf.getvar(ncfile, 'Times', wrf.ALL_TIMES)})
time = dummy['time'].to_numpy()
indices = (time[0] <= time_e) & (time_e <= time[-1])
if not np.any(indices):
raise OutOfRange('The mode-S data is out of range')
lat_e = lat_e[indices]
lon_e = lon_e[indices]
z_e = z_e[indices]
time_e = time_e[indices]
x = np.zeros((lat_e.shape[0], 4))
xy = wrf.ll_to_xy(ncfile, lat_e, lon_e, as_int=False, meta=False)
if ncfile.MAP_PROJ_CHAR == 'Cylindrical Equidistant' and i_domain == 1:
xy[0] += 360 / 0.25
x[:, 0] = xy[0]
x[:, 1] = xy[1]
e_we = ncfile.dimensions['west_east'].size
e_sn = ncfile.dimensions['south_north'].size
if np.any(xy < 0) or np.any(xy[0] > e_we - 1) or np.any(
xy[1] > e_sn - 1):
print('Flight path is outside domain d0' + str(i_domain))
continue
else:
print('Extracting WRF-data from domain d0' + str(i_domain))
if plotcurves:
xy2 = np.zeros(xy.T.shape)
xy2[:, :] = xy[:, :].T
#zgrid = wrf.interp2dxy(wrf.getvar(ncfile,'z',units=z_unit),xy2,meta=False)
zgrid = wrf.interp2dxy(wrf.g_geoht.get_height(ncfile,
units=z_unit),
xy2,
meta=False) # Get geopotential height
for i in range(0, len(z_e)):
f_time = interp1d(zgrid[:, i],
range(0, zgrid.shape[0]),
kind='linear')
x[i, 2] = f_time(z_e[i])
f_time = interp1d(time.astype('int'),
range(0, len(time)),
kind='linear')
x[:, 3] = f_time(time_e.astype('int'))
df_wrf = pd.DataFrame()
df_wrf['time'] = time_e
df_wrf['air_temperature'] = linInterp(
wrf.getvar(ncfile, 'tc', wrf.ALL_TIMES, meta=False), x)
df_wrf['pressure'] = linInterp(
wrf.g_pressure.get_pressure(ncfile,
wrf.ALL_TIMES,
meta=False,
units=p_unit), x)
df_wrf['wind_speed'] = linInterp(
wrf.g_wind.get_destag_wspd(ncfile, wrf.ALL_TIMES, meta=False),
x)
df_wrf['wind_from_direction'] = linInterp(
wrf.g_wind.get_destag_wdir(ncfile, wrf.ALL_TIMES, meta=False),
x)
if df_wrf.isnull().values.any():
print('Some points are outside the domain ' + str(i_domain))
continue
else:
isOutside = False
else:
isOutside = False
if plotcurves:
# Plot data
sharex = True
#fields = np.array([['air_temperature','wind_speed','pressure'],
# ['windDirX', 'windDirY','z']])
#ylabels = np.array([['Temperature [°C]', 'Wind speed [m/s]', 'Pressure ['+p_unit+']'],
# ['Wind direction - X','Wind direction - Y', 'Altitude ['+z_unitStr+']']])
fields = np.array([['air_temperature', 'wind_speed'],
['windDirX', 'windDirY']])
ylabels = np.array([['Temperature [°C]', 'Wind speed [m/s]'],
['Wind direction - X', 'Wind direction - Y']])
df_obs['windDirX'] = np.cos(np.radians(df_obs['Wind Direction']))
df_obs['windDirY'] = np.sin(np.radians(df_obs['Wind Direction']))
df_wrf['windDirX'] = np.cos(np.radians(df_wrf['wind_from_direction']))
df_wrf['windDirY'] = np.sin(np.radians(df_wrf['wind_from_direction']))
df_wrf['z'] = z_e
fig, axs = plt.subplots(fields.shape[0],
fields.shape[1],
sharex=sharex)
mng = plt.get_current_fig_manager()
#mng.resize(*mng.window.maxsize())
#mng.frame.Maximize(True)
mng.window.showMaximized()
title = 'Comparison between Mode-S data and WRF simulations for flight ' + sourceid
if plotcurves:
title += '. WRF data from domain d0' + str(
i_domain) + ' with grid resolution {:.1f} km'.format(
max(ncfile.DX, ncfile.DY) / 1000)
fig.suptitle(title)
for i in range(0, fields.shape[0]):
for j in range(0, fields.shape[1]):
axs[i, j].plot(df_wrf.time.to_numpy(),
df_wrf[fields[i, j]].to_numpy(),
'b',
label='WRF forecast')
axs[i, j].plot(df_obs.time.to_numpy(),
df_obs[fields[i, j]].to_numpy(),
'r',
label='Observation data')
axs[i, j].legend()
axs[i, j].set(xlabel='Time', ylabel=ylabels[i, j])
axs[i, j].set_xlim(time_e[0], time_e[-1])
axs[1, 0].set_ylim(-1, 1)
axs[1, 1].set_ylim(-1, 1)
plt.show()
fig.savefig(folder + '/' + sourceid + '_curves.png', dpi=400)
if plotmap:
# Extract the pressure, geopotential height, and wind variables
ncfile = Dataset(wrffolder + '/wrfout_d01.nc')
p = wrf.g_pressure.get_pressure(ncfile, units=p_unit)
#p = getvar(ncfile, "pressure",units=p_unit)
z = getvar(ncfile, "z", units=z_unit)
ua = getvar(ncfile, "ua", units=u_unit)
va = getvar(ncfile, "va", units=u_unit)
wspd = getvar(ncfile, "wspd_wdir", units=w_unit)[0, :]
# Interpolate geopotential height, u, and v winds to 500 hPa
ht_500 = interplevel(z, p, 500)
u_500 = interplevel(ua, p, 500)
v_500 = interplevel(va, p, 500)
wspd_500 = interplevel(wspd, p, 500)
# Get the lat/lon coordinates
lats, lons = latlon_coords(ht_500)
# Get the map projection information
cart_proj = get_cartopy(ht_500)
# Create the figure
fig = plt.figure(figsize=(12, 9))
ax = plt.axes(projection=cart_proj)
# Download and add the states and coastlines
name = 'admin_0_boundary_lines_land'
#name = 'admin_1_states_provinces_lines'
#name = "admin_1_states_provinces_shp"
bodr = cfeature.NaturalEarthFeature(category='cultural',
name=name,
scale=resol,
facecolor='none')
land = cfeature.NaturalEarthFeature('physical', 'land', \
scale=resol, edgecolor='k', facecolor=cfeature.COLORS['land'])
ocean = cfeature.NaturalEarthFeature('physical', 'ocean', \
scale=resol, edgecolor='none', facecolor=cfeature.COLORS['water'])
lakes = cfeature.NaturalEarthFeature('physical', 'lakes', \
scale=resol, edgecolor='b', facecolor=cfeature.COLORS['water'])
rivers = cfeature.NaturalEarthFeature('physical', 'rivers_lake_centerlines', \
scale=resol, edgecolor='b', facecolor='none')
ax.add_feature(land, facecolor='beige', zorder=0)
ax.add_feature(ocean, linewidth=0.2, zorder=0)
ax.add_feature(lakes, linewidth=0.2, zorder=1)
ax.add_feature(bodr, edgecolor='k', zorder=1, linewidth=0.5)
#ax.add_feature(rivers, linewidth=0.5,zorder=1)
if plotcontour:
# Add the 500 hPa geopotential height contour
levels = np.arange(100., 2000., 10.)
contours = plt.contour(to_np(lons),
to_np(lats),
to_np(ht_500),
levels=levels,
colors="forestgreen",
linewidths=1,
transform=ccrs.PlateCarree(),
zorder=3)
plt.clabel(contours, inline=1, fontsize=10, fmt="%i")
if plotcontourf:
try:
with open(home + '/kode/colormaps/SINTEF1.json') as f:
json_data = json.load(f)
SINTEF1 = np.reshape(json_data[0]['RGBPoints'], (-1, 4))
cmap = ListedColormap(fill_colormap(SINTEF1))
except:
print(
'SINTEF1 colormap not found (can be found at https://github.com/Zetison/colormaps.git)'
)
cmap = get_cmap("rainbow")
# Add the wind speed contours
levels = np.linspace(20, 120, 101)
wspd_contours = plt.contourf(to_np(lons),
to_np(lats),
to_np(wspd_500),
levels=levels,
cmap=cmap,
alpha=0.7,
antialiased=True,
transform=ccrs.PlateCarree(),
zorder=2)
cbar_wspd = plt.colorbar(wspd_contours,
ax=ax,
orientation="horizontal",
pad=.05,
shrink=0.5,
aspect=30,
ticks=range(10, 110, 10))
cbar_wspd.ax.set_xlabel('Wind speeds [' + w_unitStr +
'] at 500 mbar height')
# Add the 500 hPa wind barbs, only plotting every nthb data point.
nthb = 10
if plotbarbs:
plt.barbs(to_np(lons[::nthb, ::nthb]),
to_np(lats[::nthb, ::nthb]),
to_np(u_500[::nthb, ::nthb]),
to_np(v_500[::nthb, ::nthb]),
transform=ccrs.PlateCarree(),
length=6,
zorder=3)
track = sgeom.LineString(
zip(df_obs['Longitude'].to_numpy(), df_obs['Latitude'].to_numpy()))
fullFlightPath = ax.add_geometries([track],
ccrs.PlateCarree(),
facecolor='none',
zorder=4,
edgecolor='red',
linewidth=2,
label='Flight path')
track = sgeom.LineString(zip(lon_e, lat_e))
flightPath = ax.add_geometries([track],
ccrs.PlateCarree(),
facecolor='none',
zorder=4,
linestyle=':',
edgecolor='green',
linewidth=2,
label='Extracted flight path')
#plt.legend(handles=[fullFlightPath])
#blue_line = mlines.Line2D([], [], color='red',linestyle='--', label='Flight path',linewidth=2)
#plt.legend(handles=[blue_line])
# Set the map bounds
ax.set_xlim(cartopy_xlim(ht_500))
ax.set_ylim(cartopy_ylim(ht_500))
#ax.gridlines(draw_labels=True)
ax.gridlines()
startdate = pd.to_datetime(str(
time_e[0])).strftime("%Y-%m-%d %H:%M:%S")
plt.title('Flight ' + sourceid +
', with visualization of WRF simulation (' + startdate +
') at 500 mbar Height (' + z_unitStr + '), Wind Speed (' +
w_unitStr + '), Barbs (' + w_unitStr + ')')
# Plot domain boundaries
infile_d01 = wrffolder + '/wrfout_d01.nc'
cart_proj, xlim_d01, ylim_d01 = get_plot_element(infile_d01)
infile_d02 = wrffolder + '/wrfout_d02.nc'
_, xlim_d02, ylim_d02 = get_plot_element(infile_d02)
infile_d03 = wrffolder + '/wrfout_d03.nc'
_, xlim_d03, ylim_d03 = get_plot_element(infile_d03)
infile_d04 = wrffolder + '/wrfout_d04.nc'
_, xlim_d04, ylim_d04 = get_plot_element(infile_d04)
# d01
ax.set_xlim([
xlim_d01[0] - (xlim_d01[1] - xlim_d01[0]) / 15,
xlim_d01[1] + (xlim_d01[1] - xlim_d01[0]) / 15
])
ax.set_ylim([
ylim_d01[0] - (ylim_d01[1] - ylim_d01[0]) / 15,
ylim_d01[1] + (ylim_d01[1] - ylim_d01[0]) / 15
])
# d01 box
textSize = 10
colors = ['blue', 'orange', 'brown', 'deepskyblue']
linewidth = 1
txtscale = 0.003
ax.add_patch(
mpl.patches.Rectangle((xlim_d01[0], ylim_d01[0]),
xlim_d01[1] - xlim_d01[0],
ylim_d01[1] - ylim_d01[0],
fill=None,
lw=linewidth,
edgecolor=colors[0],
zorder=10))
ax.text(xlim_d01[0],
ylim_d01[0] + (ylim_d01[1] - ylim_d01[0]) * (1 + txtscale),
'd01',
size=textSize,
color=colors[0],
zorder=10)
# d02 box
ax.add_patch(
mpl.patches.Rectangle((xlim_d02[0], ylim_d02[0]),
xlim_d02[1] - xlim_d02[0],
ylim_d02[1] - ylim_d02[0],
fill=None,
lw=linewidth,
edgecolor=colors[1],
zorder=10))
ax.text(xlim_d02[0],
ylim_d02[0] + (ylim_d02[1] - ylim_d02[0]) * (1 + txtscale * 3),
'd02',
size=textSize,
color=colors[1],
zorder=10)
# d03 box
ax.add_patch(
mpl.patches.Rectangle((xlim_d03[0], ylim_d03[0]),
xlim_d03[1] - xlim_d03[0],
ylim_d03[1] - ylim_d03[0],
fill=None,
lw=linewidth,
edgecolor=colors[2],
zorder=10))
ax.text(xlim_d03[0],
ylim_d03[0] + (ylim_d03[1] - ylim_d03[0]) *
(1 + txtscale * 3**2),
'd03',
size=textSize,
color=colors[2],
zorder=10)
# d04 box
ax.add_patch(
mpl.patches.Rectangle((xlim_d04[0], ylim_d04[0]),
xlim_d04[1] - xlim_d04[0],
ylim_d04[1] - ylim_d04[0],
fill=None,
lw=linewidth,
edgecolor=colors[3],
zorder=10))
ax.text(xlim_d04[0],
ylim_d04[0] + (ylim_d04[1] - ylim_d04[0]) *
(1 + txtscale * 3**3),
'd04',
size=textSize,
color=colors[3],
zorder=10)
plt.show()
fig.savefig(folder + '/' + sourceid + '_map.png', dpi=400)
def process_wrfout_manual(DIR_WRFOUT, wrfout_file, start=None, end=None, save_file=True):
"""
Processes the wrfout file -- calculates GHI and wind power denity (WPD) and writes these variables
to wrfout_processed_d01.nc data file to be used by the regridding script (wrf2era_error.ncl) in
wrf_era5_diff().
This method makes use of two different packages that are not endogeneous to optwrf. The first is
pvlib.wrfcast, which is a module for processing WRF output data that I have customized based on the
pvlib.forecast model. The purpose of this is to eventually be able to use this method to calculate
PV output from systems installed at any arbitrary location within your WRF model domain (this
is not yet implemented). I have use this wrfcast module to calculate the GHI from WRF output data
The second package is the wrf module maintained by NCAR, which reproduces some of the funcionality
of NCL in Python. I use this to interpolate the wind speed to 100m.
With the help of these two packages, the remaineder of the methods claculates the WPD, formats the
data to be easily compatible with other methods, and writes the data to a NetCDF file.
"""
# Absolute path to wrfout data file
datapath = DIR_WRFOUT + wrfout_file
# Read in the wrfout file using the netCDF4.Dataset method (I think you can also do this with an xarray method)
netcdf_data = netCDF4.Dataset(datapath)
# Create an xarray.Dataset from the wrf qurery_variables.
query_variables = [
'T2',
'CLDFRA',
'COSZEN',
'SWDDNI',
'SWDDIF',
'height_agl',
'wspd',
'wdir',
]
met_data = _wrf2xarray(netcdf_data, query_variables)
variables = {
'XLAT': 'XLAT',
'XLONG': 'XLONG',
'T2': 'temp_air',
'CLDFRA': 'cloud_fraction',
'COSZEN': 'cos_zenith',
'SWDDNI': 'dni',
'SWDDIF': 'dhi',
'height_agl': 'height_agl',
'wspd': 'wspd',
'wdir': 'wdir',
}
# Rename the variables
met_data = xr.Dataset.rename(met_data, variables)
# Convert air temp from kelvin to celsius and calculate global horizontal irradiance
# using the WRF forecast model methods from pvlib package
fm = WRF()
met_data['temp_air'] = fm.kelvin_to_celsius(met_data['temp_air'])
met_data['ghi'] = fm.dni_and_dhi_to_ghi(met_data['dni'], met_data['dhi'], met_data['cos_zenith'])
# Interpolate wind speeds to 100m height
met_data['wind_speed100'] = wrf.interplevel(met_data['wspd'], met_data['height_agl'], 100)
# Calculate the wind power density
met_data['wpd'] = calc_wpd(met_data['wind_speed100'])
# Drop unnecessary coordinates
met_data = xr.Dataset.reset_coords(met_data, ['XTIME', 'level'], drop=True)
# Slice the wrfout data if start and end times ares specified
if start and end is not None:
met_data = met_data.sel(Time=slice(start, end))
# Save the output file if specified.
if save_file:
# Write the processed data to a wrfout NetCDF file
new_filename = DIR_WRFOUT + 'processed_' + wrfout_file
met_data.to_netcdf(path=new_filename)
else:
return met_data
def wrf_interp(level):
result = interplevel(var, zz, level)
return result
def get_data_vslice(data_path,
slice_width=1,
slice_index=0,
slice_z=None,
zres=None,
slice_type='vy',
t_start=0,
t_end=0,
force=False):
if slice_type == 'vy' or slice_type == 'vx':
file_name = data_path + '/wrfout_' + slice_type + '_slice_index_' + str(
slice_index) + '_t_start_' + str(t_start) + '_t_end_' + str(
t_end) + '.pkl'
if slice_type == 'h':
file_name = data_path + '/wrfout_' + slice_type + '_slice_z_' + str(
slice_z) + '_t_start_' + str(t_start) + '_t_end_' + str(
t_end) + '.pkl'
if slice_type == 'h' and (slice_z is None or zres is None):
print(
'Error: Si se hace un corte horizontal hay que especificar slice_z y zres'
)
return
#Vamos a guardar una cross section a x o y constantes. Si fijamos un x o un y, tomamos width puntos alrededor de ese x o y para poder calcular
#derivadas espaciales centradas en el corte. La idea es tener todos los datos necesarios para poder hacer calculos sobre ese corte en particular (derivadas temporales y espaciales).
if os.path.exists(file_name) & (not force):
with open(file_name, 'rb') as handle:
my_data = pickle.load(handle)
else:
my_data = dict()
my_data['file_name'] = file_name
my_data['times'] = []
my_data['file_list'] = glob.glob(
data_path +
'/wrfout_d01_*') #Busco todos los wrfout en la carpeta indicada.
my_data['file_list'].sort()
#Leo el primer archivo de la lista de donde voy a tomar el perfil de referencia.
ncfile = Dataset(my_data['file_list'][0])
tmp = to_np(getvar(ncfile, "P"))
nx = tmp.shape[2] #Order is Z=0 , Y=1 , X=0
ny = tmp.shape[1]
nz = tmp.shape[0]
nt = len(my_data['file_list'])
if (t_start != 0 or t_end != 0) and (t_end > t_start):
ts = t_start
te = t_end
else:
ts = 0
te = nt
if slice_type == 'vy' or slice_type == 'vx': #Vertical slice
if slice_type == 'vy':
ys = slice_index - slice_width
ye = slice_index + slice_width + 1
xs = 0
xe = nx
if slice_type == 'vx':
xs = slice_index - slice_width
xe = slice_index + slice_width + 1
ys = 0
ye = ny
zs = 0
ze = nz
snz = ze - zs
if slice_type == 'h': #Horizontal slice
ys = 0
ye = ny
xs = 0
xe = nx
snz = 2 * slice_width + 1
snx = xe - xs
sny = ye - ys
snt = te - ts
my_data['nx'] = snx
my_data['ny'] = sny
my_data['nz'] = snz
my_data['nt'] = snt
#Allocate memory
my_data['p'] = np.zeros((snz, sny, snx, snt))
my_data['u'] = np.zeros((snz, sny, snx, snt))
my_data['v'] = np.zeros((snz, sny, snx, snt))
my_data['w'] = np.zeros((snz, sny, snx, snt))
my_data['ref'] = np.zeros((snz, sny, snx, snt))
my_data['t'] = np.zeros((snz, sny, snx, snt))
my_data['qv'] = np.zeros((snz, sny, snx, snt))
my_data['qc'] = np.zeros((snz, sny, snx, snt))
my_data['qr'] = np.zeros((snz, sny, snx, snt))
my_data['qi'] = np.zeros((snz, sny, snx, snt))
my_data['qs'] = np.zeros((snz, sny, snx, snt))
my_data['qg'] = np.zeros((snz, sny, snx, snt))
my_data['z'] = np.zeros((snz, sny, snx, snt))
my_data['thetae'] = np.zeros((snz, sny, snx, snt))
my_data['theta'] = np.zeros((snz, sny, snx, snt))
my_data['rh'] = np.zeros((snz, sny, snx, snt))
my_data['h_diabatic'] = np.zeros((snz, sny, snx, snt))
my_data['qv_diabatic'] = np.zeros((snz, sny, snx, snt))
my_data['file_list'] = my_data['file_list'][ts:te]
#Leo los datos y los guardo en el diccionario del corte.
for itime in range(ts, te):
ctime = itime - ts
print('Reading file ' + my_data['file_list'][ctime])
ncfile = Dataset(my_data['file_list'][ctime])
if slice_type == 'vy' or slice_type == 'vx': #Vertical slice
my_data['p'][:, :, :,
ctime] = to_np(getvar(ncfile, "pres",
units='Pa'))[zs:ze, ys:ye,
xs:xe]
my_data['u'][:, :, :,
ctime] = to_np(getvar(ncfile, "ua",
units='ms-1'))[zs:ze, ys:ye,
xs:xe]
my_data['v'][:, :, :,
ctime] = to_np(getvar(ncfile, "va",
units='ms-1'))[zs:ze, ys:ye,
xs:xe]
my_data['w'][:, :, :,
ctime] = to_np(getvar(ncfile, "wa",
units='ms-1'))[zs:ze, ys:ye,
xs:xe]
my_data['ref'][:, :, :,
ctime] = to_np(getvar(ncfile,
"dbz"))[zs:ze, ys:ye,
xs:xe]
my_data['t'][:, :, :,
ctime] = to_np(getvar(ncfile, "temp",
units='K'))[zs:ze, ys:ye,
xs:xe]
my_data['qv'][:, :, :,
ctime] = to_np(getvar(ncfile,
"QVAPOR"))[zs:ze, ys:ye,
xs:xe]
my_data['qc'][:, :, :,
ctime] = to_np(getvar(ncfile,
"QCLOUD"))[zs:ze, ys:ye,
xs:xe]
my_data['qr'][:, :, :,
ctime] = to_np(getvar(ncfile,
"QRAIN"))[zs:ze, ys:ye,
xs:xe]
my_data['qi'][:, :, :,
ctime] = to_np(getvar(ncfile,
"QICE"))[zs:ze, ys:ye,
xs:xe]
my_data['qs'][:, :, :,
ctime] = to_np(getvar(ncfile,
"QSNOW"))[zs:ze, ys:ye,
xs:xe]
my_data['qg'][:, :, :,
ctime] = to_np(getvar(ncfile,
"QGRAUP"))[zs:ze, ys:ye,
xs:xe]
my_data['z'][:, :, :,
ctime] = to_np(getvar(ncfile, "height",
units='m'))[zs:ze, ys:ye,
xs:xe]
my_data['thetae'][:, :, :, ctime] = to_np(
getvar(ncfile, "theta_e", units='K'))[zs:ze, ys:ye, xs:xe]
my_data['theta'][:, :, :, ctime] = to_np(
getvar(ncfile, "theta", units='K'))[zs:ze, ys:ye, xs:xe]
my_data['rh'][:, :, :,
ctime] = to_np(getvar(ncfile,
"rh"))[zs:ze, ys:ye, xs:xe]
try:
my_data['h_diabatic'][:, :, :, ctime] = to_np(
getvar(ncfile, "H_DIABATIC"))[zs:ze, ys:ye, xs:xe]
my_data['qv_diabatic'][:, :, :, ctime] = to_np(
getvar(ncfile, "QV_DIABATIC"))[zs:ze, ys:ye, xs:xe]
except:
print('Warning no diabatic data found')
my_data['times'].append(
dt.datetime.strptime(
os.path.basename(my_data['file_list'][ctime])[11:],
'%Y-%m-%d_%H:%M:%S'))
if slice_type == 'h': #Horizontal slice at constant height
z_levels = np.arange(slice_z - slice_width * zres,
slice_z + 2.0 * slice_width * zres, zres)
z_ = getvar(ncfile, "height", units='m')
p_ = getvar(ncfile, "pres", units='Pa')
u_ = getvar(ncfile, "ua", units='ms-1')
v_ = getvar(ncfile, "va", units='ms-1')
w_ = getvar(ncfile, "wa", units='ms-1')
ref_ = getvar(ncfile, "dbz")
t_ = getvar(ncfile, "temp", units='K')
qv_ = getvar(ncfile, "QVAPOR")
qc_ = getvar(ncfile, "QCLOUD")
qr_ = getvar(ncfile, "QRAIN")
qi_ = getvar(ncfile, "QICE")
qs_ = getvar(ncfile, "QSNOW")
qg_ = getvar(ncfile, "QGRAUP")
thetae_ = getvar(ncfile, "theta_e", units='K')
theta_ = getvar(ncfile, "theta", units='K')
rh_ = getvar(ncfile, "rh")
try:
h_diabatic_ = getvar(ncfile, "H_DIABATIC")
qv_diabatic_ = getvar(ncfile, "QV_DIABATIC")
my_data['h_diabatic'][:, :, :, ctime] = interplevel(
h_diabatic_, z_, z_levels)
my_data['qv_diabatic'][:, :, :, ctime] = interplevel(
qv_diabatic_, z_, z_levels)
except:
print('Warning no diabatic data found')
my_data['p'][:, :, :, ctime] = interplevel(p_, z_, z_levels)
my_data['u'][:, :, :, ctime] = interplevel(u_, z_, z_levels)
my_data['v'][:, :, :, ctime] = interplevel(v_, z_, z_levels)
my_data['w'][:, :, :, ctime] = interplevel(w_, z_, z_levels)
my_data['ref'][:, :, :,
ctime] = interplevel(ref_, z_, z_levels)
my_data['t'][:, :, :, ctime] = interplevel(t_, z_, z_levels)
my_data['qv'][:, :, :, ctime] = interplevel(qv_, z_, z_levels)
my_data['qc'][:, :, :, ctime] = interplevel(qc_, z_, z_levels)
my_data['qr'][:, :, :, ctime] = interplevel(qr_, z_, z_levels)
my_data['qi'][:, :, :, ctime] = interplevel(qi_, z_, z_levels)
my_data['qs'][:, :, :, ctime] = interplevel(qs_, z_, z_levels)
my_data['qg'][:, :, :, ctime] = interplevel(qg_, z_, z_levels)
my_data['thetae'][:, :, :,
ctime] = interplevel(thetae_, z_, z_levels)
my_data['theta'][:, :, :,
ctime] = interplevel(theta_, z_, z_levels)
my_data['rh'][:, :, :, ctime] = interplevel(rh_, z_, z_levels)
my_data['times'].append(
dt.datetime.strptime(
os.path.basename(my_data['file_list'][ctime])[11:],
'%Y-%m-%d_%H:%M:%S'))
if slice_type == 'h':
for iz, my_z in enumerate(z_levels):
my_data['z'][iz, :, :, :] = my_z
if nt == 1:
my_data['dt'] = 1.0
else:
my_data['dt'] = (my_data['times'][1] - my_data['times'][0]).seconds
my_data['dx'] = ncfile.DX
my_data['dy'] = ncfile.DY
my_data['file_name'] = file_name
my_data['slice_type'] = slice_type
my_data['slice_index'] = slice_index
my_data['slice_width'] = slice_width
my_data['slice_z'] = slice_z
my_data['zres'] = zres
my_data['ts'] = ts
my_data['te'] = te
my_data['p'] = np.float32(my_data['p'])
my_data['u'] = np.float32(my_data['u'])
my_data['v'] = np.float32(my_data['v'])
my_data['w'] = np.float32(my_data['w'])
my_data['ref'] = np.float32(my_data['ref'])
my_data['t'] = np.float32(my_data['t'])
my_data['qv'] = np.float32(my_data['qv'])
my_data['qc'] = np.float32(my_data['qc'])
my_data['qr'] = np.float32(my_data['qr'])
my_data['qi'] = np.float32(my_data['qi'])
my_data['qs'] = np.float32(my_data['qs'])
my_data['qg'] = np.float32(my_data['qg'])
my_data['z'] = np.float32(my_data['z'])
my_data['thetae'] = np.float32(my_data['thetae'])
my_data['theta'] = np.float32(my_data['theta'])
my_data['rh'] = np.float32(my_data['rh'])
my_data['h_diabatic'] = np.float32(my_data['h_diabatic'])
my_data['qv_diabatic'] = np.float32(my_data['qv_diabatic'])
with open(file_name, 'wb') as handle:
pickle.dump(my_data, handle, protocol=pickle.HIGHEST_PROTOCOL)
return my_data
############# ESPECIFICAR EL CAMPO DE PRESION QUE SE QUIERA #############
nivel = 700
# SE EXTRAEN LAS VARIABLES A UTILIZAR
# SE HACE UN CICLO PARA TODOS LOS TIEMPOS DEL WRF_FILE
for tidx in range(0, tpos) : # len(tpos)
p = getvar(wrf_file, "pressure", tidx) # presion
z = getvar(wrf_file, "z", tidx, units = 'm') # geopotencial
w = getvar(wrf_file, "wa", tidx, units="m s-1") # vel vertical
ter = getvar(wrf_file, "ter", tidx) # terreno alturas
# Se interpolan las variables al campo de presion correspondiente para podee graficarlas
w = interplevel(w, p, nivel)
# Se extraen lat-lon
lats, lons = latlon_coords(p)
# Se extrae info de la proyeccion
cart_proj = get_cartopy(p)
############################################## GRAFICADO ##############################################
fig = plt.figure(figsize=(12,9))
ax = plt.axes(projection=cart_proj)
# Aniadiendo los sombreados
#levels = [25, 30, 35, 40, 50, 60, 70, 80, 90, 100, 110, 120]
levels = np.arange(-1.5, 1.5, 0.25)
def set_height(self, h=1000, update=True):
"""
h (float) :: height in metres.
"""
if np.any(self.Xsmooth == None):
print(
"Field has not been smoothed. Turbulence cannot be calculated."
)
if h == None:
h = 1000
if self.isvertical == False:
print(
"Variable not defined on vertical layers. Not interpolating.")
self.Xinterp = self.X
if not np.any(self.Xsmooth == None):
self.Xsmoothinterp = self.Xsmooth
else:
zmin = np.array(wrf.to_np(np.min(np.max(self.z, axis=(2, 3)))))
zmax = np.array(wrf.to_np(np.max(np.min(self.z, axis=(2, 3)))))
if (h < zmin) or (h > zmax):
print("Height = " + str(h) + \
"\nMay produce missing values if not between " + str(zmin) + " and " + str(zmax))
print("Interpolating " + self.str_id + " onto height " + str(h) +
"...")
self.Xinterp = wrf.interplevel(self.X, self.z, h)
if not np.any(self.Xsmooth == None):
self.Xsmoothinterp = wrf.interplevel(self.Xsmooth, self.z, h)
print("Done.")
nans = np.sum(np.isnan(np.array(wrf.to_np(self.Xinterp))))
if nans > 0:
print("Warning: Field contains " + str(nans) + " NaNs.")
elif nans == np.size(self.Xinterp):
print("Warning: Field only contains NaNs.")
else:
print("Field contains no NaNs.")
self.h = h
if not (np.any(self.Xsmoothinterp == None)
or np.any(self.Xinterp == None)):
self.dX = np.array(wrf.to_np(self.Xinterp)) - np.array(
wrf.to_np(self.Xsmoothinterp))
self.turb_ratiointerp = self.dX / self.Xsmoothinterp
if self.derived:
self.U.set_height(h, update=False)
self.V.set_height(h, update=False)
if update:
self.update(2)
self.update(3)
if self.autoplot:
plot_field(self)
return
# Open the NetCDF file
filename = files_path + '/' + req_year + req_month + req_day + dir_hour\
+ '/wrfout_d01_' + req_year + '-' + req_month + '-' + req_day + '_' + req_hour + '_00_00'
ncfile = Dataset(filename)
######################################################################################################
# pressure levels interpolation
######################################################################################################
# Extract the pressure, geopotential height, and wind variables
# p = getvar(ncfile, "pressure")
p = getvar(ncfile, "pressure")
LWC = getvar(ncfile, "QCLOUD") #, meta=False)
# Interpolate geopotential height
LWC_750 = interplevel(LWC, p, 750)
# Get the lat/lon coordinates
lats, lons = latlon_coords(LWC_750)
# Get the basemap object
bm = get_basemap(LWC_750)
# Create the figure
fig = plt.figure(figsize=(12, 9))
ax = plt.axes()
# Convert the lat/lon coordinates to x/y coordinates in the projection space
x, y = bm(to_np(lons), to_np(lats))
# Add the 500 hPa geopotential height contours
def test(self):
try:
from netCDF4 import Dataset as NetCDF
except:
pass
try:
from PyNIO import Nio
except:
pass
if not multi:
timeidx = 0
if not pynio:
in_wrfnc = NetCDF(wrf_in)
else:
# Note: Python doesn't like it if you reassign an outer scoped
# variable (wrf_in in this case)
if not wrf_in.endswith(".nc"):
wrf_file = wrf_in + ".nc"
else:
wrf_file = wrf_in
in_wrfnc = Nio.open_file(wrf_file)
else:
timeidx = None
if not pynio:
nc = NetCDF(wrf_in)
in_wrfnc = [nc for i in xrange(repeat)]
else:
if not wrf_in.endswith(".nc"):
wrf_file = wrf_in + ".nc"
else:
wrf_file = wrf_in
nc = Nio.open_file(wrf_file)
in_wrfnc = [nc for i in xrange(repeat)]
if (varname == "interplevel"):
ref_ht_500 = _get_refvals(referent, "z_500", repeat, multi)
hts = getvar(in_wrfnc, "z", timeidx=timeidx)
p = getvar(in_wrfnc, "pressure", timeidx=timeidx)
hts_500 = interplevel(hts, p, 500)
nt.assert_allclose(to_np(hts_500), ref_ht_500)
elif (varname == "vertcross"):
ref_ht_cross = _get_refvals(referent, "ht_cross", repeat, multi)
ref_p_cross = _get_refvals(referent, "p_cross", repeat, multi)
hts = getvar(in_wrfnc, "z", timeidx=timeidx)
p = getvar(in_wrfnc, "pressure", timeidx=timeidx)
pivot_point = CoordPair(hts.shape[-1] / 2, hts.shape[-2] / 2)
ht_cross = vertcross(hts, p, pivot_point=pivot_point, angle=90.)
nt.assert_allclose(to_np(ht_cross), ref_ht_cross, rtol=.01)
# Test opposite
p_cross1 = vertcross(p, hts, pivot_point=pivot_point, angle=90.0)
nt.assert_allclose(to_np(p_cross1), ref_p_cross, rtol=.01)
# Test point to point
start_point = CoordPair(0, hts.shape[-2] / 2)
end_point = CoordPair(-1, hts.shape[-2] / 2)
p_cross2 = vertcross(p,
hts,
start_point=start_point,
end_point=end_point)
nt.assert_allclose(to_np(p_cross1), to_np(p_cross2))
elif (varname == "interpline"):
ref_t2_line = _get_refvals(referent, "t2_line", repeat, multi)
t2 = getvar(in_wrfnc, "T2", timeidx=timeidx)
pivot_point = CoordPair(t2.shape[-1] / 2, t2.shape[-2] / 2)
t2_line1 = interpline(t2, pivot_point=pivot_point, angle=90.0)
nt.assert_allclose(to_np(t2_line1), ref_t2_line)
# Test point to point
start_point = CoordPair(0, t2.shape[-2] / 2)
end_point = CoordPair(-1, t2.shape[-2] / 2)
t2_line2 = interpline(t2,
start_point=start_point,
end_point=end_point)
nt.assert_allclose(to_np(t2_line1), to_np(t2_line2))
elif (varname == "vinterp"):
# Tk to theta
fld_tk_theta = _get_refvals(referent, "fld_tk_theta", repeat,
multi)
fld_tk_theta = np.squeeze(fld_tk_theta)
tk = getvar(in_wrfnc, "temp", timeidx=timeidx, units="k")
interp_levels = [200, 300, 500, 1000]
field = vinterp(in_wrfnc,
field=tk,
vert_coord="theta",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
tol = 5 / 100.
atol = 0.0001
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_tk_theta)))
nt.assert_allclose(to_np(field), fld_tk_theta, tol, atol)
# Tk to theta-e
fld_tk_theta_e = _get_refvals(referent, "fld_tk_theta_e", repeat,
multi)
fld_tk_theta_e = np.squeeze(fld_tk_theta_e)
interp_levels = [200, 300, 500, 1000]
field = vinterp(in_wrfnc,
field=tk,
vert_coord="theta-e",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
tol = 3 / 100.
atol = 50.0001
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_tk_theta_e)/fld_tk_theta_e)*100)
nt.assert_allclose(to_np(field), fld_tk_theta_e, tol, atol)
# Tk to pressure
fld_tk_pres = _get_refvals(referent, "fld_tk_pres", repeat, multi)
fld_tk_pres = np.squeeze(fld_tk_pres)
interp_levels = [850, 500]
field = vinterp(in_wrfnc,
field=tk,
vert_coord="pressure",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_tk_pres)))
nt.assert_allclose(to_np(field), fld_tk_pres, tol, atol)
# Tk to geoht_msl
fld_tk_ght_msl = _get_refvals(referent, "fld_tk_ght_msl", repeat,
multi)
fld_tk_ght_msl = np.squeeze(fld_tk_ght_msl)
interp_levels = [1, 2]
field = vinterp(in_wrfnc,
field=tk,
vert_coord="ght_msl",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_tk_ght_msl)))
nt.assert_allclose(to_np(field), fld_tk_ght_msl, tol, atol)
# Tk to geoht_agl
fld_tk_ght_agl = _get_refvals(referent, "fld_tk_ght_agl", repeat,
multi)
fld_tk_ght_agl = np.squeeze(fld_tk_ght_agl)
interp_levels = [1, 2]
field = vinterp(in_wrfnc,
field=tk,
vert_coord="ght_agl",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_tk_ght_agl)))
nt.assert_allclose(to_np(field), fld_tk_ght_agl, tol, atol)
# Hgt to pressure
fld_ht_pres = _get_refvals(referent, "fld_ht_pres", repeat, multi)
fld_ht_pres = np.squeeze(fld_ht_pres)
z = getvar(in_wrfnc, "height", timeidx=timeidx, units="m")
interp_levels = [500, 50]
field = vinterp(in_wrfnc,
field=z,
vert_coord="pressure",
interp_levels=interp_levels,
extrapolate=True,
field_type="ght",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_ht_pres)))
nt.assert_allclose(to_np(field), fld_ht_pres, tol, atol)
# Pressure to theta
fld_pres_theta = _get_refvals(referent, "fld_pres_theta", repeat,
multi)
fld_pres_theta = np.squeeze(fld_pres_theta)
p = getvar(in_wrfnc, "pressure", timeidx=timeidx)
interp_levels = [200, 300, 500, 1000]
field = vinterp(in_wrfnc,
field=p,
vert_coord="theta",
interp_levels=interp_levels,
extrapolate=True,
field_type="pressure",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_pres_theta)))
nt.assert_allclose(to_np(field), fld_pres_theta, tol, atol)
# Theta-e to pres
fld_thetae_pres = _get_refvals(referent, "fld_thetae_pres", repeat,
multi)
fld_thetae_pres = np.squeeze(fld_thetae_pres)
eth = getvar(in_wrfnc, "eth", timeidx=timeidx)
interp_levels = [850, 500, 5]
field = vinterp(in_wrfnc,
field=eth,
vert_coord="pressure",
interp_levels=interp_levels,
extrapolate=True,
field_type="theta-e",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
#print (np.amax(np.abs(to_np(field) - fld_thetae_pres)))
nt.assert_allclose(to_np(field), fld_thetae_pres, tol, atol)
def main(args):
fname = args.file
save_file = args.save_file
# List of variables that are already included in the WRF output and that we want to compute using the wrf-python
variables = dict(primary=[
'XLAT', 'XLONG', 'T2', 'SWDOWN', 'LWUPB', 'GLW', 'PSFC', 'RAINC',
'RAINNC', 'RAINSH', 'SNOWNC', 'SST', 'DIFFUSE_FRAC', 'LANDMASK',
'LAKEMASK', 'PBLH', 'TSK', 'UST'
],
computed=['rh2', 'slp', 'mdbz'])
# Generate height table for interpolation of U and V components
gen_heights = [
20, 320, 20
] # minimum height, maximum height, distance between heights
# Output time units
time_units = 'seconds since 1970-01-01 00:00:00'
os.makedirs(os.path.dirname(save_file), exist_ok=True)
# create_dir(os.path.dirname(save_file))
# Create list of heights between min and max height separated by a stride value defined above
heights = list(np.arange(gen_heights[0], gen_heights[1], gen_heights[2]))
heights.append(gen_heights[1])
# Open using netCDF toolbox
ncfile = xr.open_dataset(fname)
original_global_attributes = ncfile.attrs
ncfile = ncfile._file_obj.ds
# Load primary variables and append to list
primary_vars = {}
for var in variables['primary']:
primary_vars[var] = cf.delete_attr(getvar(ncfile, var))
# Calculate diagnostic variables defined above and add to dictionary
diagnostic_vars = {}
for dvar in variables['computed']:
diagnostic_vars[dvar.upper()] = cf.delete_attr(getvar(ncfile, dvar))
# Subtract terrain height from height above sea level
new_z = getvar(ncfile, 'z') - getvar(ncfile, 'ter')
# Calculate u and v components of wind rotated to Earth coordinates
uvm = getvar(ncfile, 'uvmet')
# interpolate u and v components of wind to 0-200m by 10m
uvtemp = interplevel(uvm, new_z, heights, default_fill(np.float32))
uvtemp = uvtemp.rename({'level': 'height'})
utemp, vtemp = cf.split_uvm(uvtemp)
# Calculate 10m u and v components of wind rotated to Earth coordinates and split into separate variables
primary_vars['U10'], primary_vars['V10'] = cf.split_uvm(
getvar(ncfile, 'uvmet10'))
# Concatenate the list of calculated u and v values into data array. Append to diagnostic_vars list
diagnostic_vars['U'] = xr.concat(utemp, dim='height')
diagnostic_vars['V'] = xr.concat(vtemp, dim='height')
# Interpolate u and v components of wind to Boundary Layer Heights for wind gust calculation
uvpblh = interplevel(uvm, new_z, primary_vars['PBLH'])
uvpblh = cf.delete_attr(uvpblh).drop(['u_v'
]) # drop unnecessary attributes
upblh = uvpblh[0].rename('upblh')
vpblh = uvpblh[1].rename('vpblh')
# Calculate wind gust
sfcwind = np.sqrt(primary_vars['U10']**2 + primary_vars['V10']**2)
pblwind = np.sqrt(upblh**2 + vpblh**2)
delwind = pblwind - sfcwind
pblval = primary_vars['PBLH'] / 2000
pblval = pblval.where(
pblval < 0.5, other=0.5
) # if the value is less than 0.5, keep it. otherwise, change to 0.5
delwind = delwind * (1 - pblval)
gust = sfcwind + delwind
gust = gust.drop('level') # drop the 'level' coordinate
gust = gust.astype(np.float32)
# Create xarray dataset of primary and diagnostic variables
ds = xr.Dataset({**primary_vars, **diagnostic_vars})
ds['U'] = ds.U.astype(np.float32)
ds['V'] = ds.V.astype(np.float32)
ds['height'] = ds.height.astype(np.int32)
try:
del ds.U.attrs['vert_units']
del ds.V.attrs['vert_units']
except KeyError:
pass
ds['Times'] = np.array([
pd.Timestamp(ds.Time.data).strftime('%Y-%m-%d_%H:%M:%S')
]).astype('<S19')
ds = ds.expand_dims('Time', axis=0)
# Add description and units for lon, lat dimensions for georeferencing
ds['XLAT'].attrs['description'] = 'latitude'
ds['XLAT'].attrs['units'] = 'degree_north'
ds['XLONG'].attrs['description'] = 'longitude'
ds['XLONG'].attrs['units'] = 'degree_east'
# Set XTIME attribute
ds['XTIME'].attrs['units'] = 'minutes'
# Set lon attributes
ds['XLONG'].attrs['long_name'] = 'Longitude'
ds['XLONG'].attrs['standard_name'] = 'longitude'
ds['XLONG'].attrs['short_name'] = 'lon'
ds['XLONG'].attrs['units'] = 'degrees_east'
ds['XLONG'].attrs['axis'] = 'X'
ds['XLONG'].attrs['valid_min'] = np.float32(-180.0)
ds['XLONG'].attrs['valid_max'] = np.float32(180.0)
# Set lat attributes
ds['XLAT'].attrs['long_name'] = 'Latitude'
ds['XLAT'].attrs['standard_name'] = 'latitude'
ds['XLAT'].attrs['short_name'] = 'lat'
ds['XLAT'].attrs['units'] = 'degrees_north'
ds['XLAT'].attrs['axis'] = 'Y'
ds['XLAT'].attrs['valid_min'] = np.float32(-90.0)
ds['XLAT'].attrs['valid_max'] = np.float32(90.0)
# Set depth attributes
ds['height'].attrs['long_name'] = 'Height Above Ground Level'
ds['height'].attrs['standard_name'] = 'height'
ds['height'].attrs[
'comment'] = 'Derived from subtracting terrain height from height above sea level'
ds['height'].attrs['units'] = 'm'
ds['height'].attrs['axis'] = 'Z'
ds['height'].attrs['positive'] = 'up'
# Set u attributes
ds['U'].attrs['long_name'] = 'Eastward Wind Component'
ds['U'].attrs['standard_name'] = 'eastward_wind'
ds['U'].attrs['short_name'] = 'u'
ds['U'].attrs['units'] = 'm s-1'
ds['U'].attrs['description'] = 'earth rotated u'
ds['U'].attrs['valid_min'] = np.float32(-300)
ds['U'].attrs['valid_max'] = np.float32(300)
# Set v attributes
ds['V'].attrs['long_name'] = 'Northward Wind Component'
ds['V'].attrs['standard_name'] = 'northward_wind'
ds['V'].attrs['short_name'] = 'v'
ds['V'].attrs['units'] = 'm s-1'
ds['V'].attrs['description'] = 'earth rotated v'
ds['V'].attrs['valid_min'] = np.float32(-300)
ds['V'].attrs['valid_max'] = np.float32(300)
# Set u10 attributes
ds['U10'].attrs['long_name'] = 'Eastward Wind Component - 10m'
ds['U10'].attrs['standard_name'] = 'eastward_wind'
ds['U10'].attrs['short_name'] = 'u'
ds['U10'].attrs['units'] = 'm s-1'
ds['U10'].attrs['description'] = '10m earth rotated u'
ds['U10'].attrs['valid_min'] = np.float32(-300)
ds['U10'].attrs['valid_max'] = np.float32(300)
# Set v10 attributes
ds['V10'].attrs['long_name'] = 'Northward Wind Component - 10m'
ds['V10'].attrs['standard_name'] = 'northward_wind'
ds['V10'].attrs['short_name'] = 'v'
ds['V10'].attrs['units'] = 'm s-1'
ds['V10'].attrs['description'] = '10m earth rotated v'
ds['V10'].attrs['valid_min'] = np.float32(-300)
ds['V10'].attrs['valid_max'] = np.float32(300)
# set primary attributes
ds['GLW'].attrs[
'standard_name'] = 'surface_downwelling_longwave_flux_in_air'
ds['GLW'].attrs['long_name'] = 'Surface Downwelling Longwave Flux'
ds['LWUPB'].attrs['standard_name'] = 'surface_upwelling_longwave_flux'
ds['LWUPB'].attrs['long_name'] = 'Surface Upwelling Longwave Flux'
ds['PSFC'].attrs['standard_name'] = 'surface_air_pressure'
ds['PSFC'].attrs['long_name'] = 'Air Pressure at Surface'
ds['RH2'].attrs['standard_name'] = 'relative_humidity'
ds['RH2'].attrs['long_name'] = 'Relative Humidity'
ds['SLP'].attrs['standard_name'] = 'air_pressure_at_sea_level'
ds['SLP'].attrs['long_name'] = 'Air Pressure at Sea Level'
ds['SWDOWN'].attrs[
'standard_name'] = 'surface_downwelling_shortwave_flux_in_air'
ds['SWDOWN'].attrs['long_name'] = 'Surface Downwelling Shortwave Flux'
ds['T2'].attrs['standard_name'] = 'air_temperature'
ds['T2'].attrs['long_name'] = 'Air Temperature at 2m'
ds['RAINC'].attrs['long_name'] = 'Accumulated Total Cumulus Precipitation'
ds['RAINNC'].attrs[
'long_name'] = 'Accumulated Total Grid Scale Precipitation'
ds['RAINSH'].attrs[
'long_name'] = 'Accumulated Shallow Cumulus Precipitation'
ds['SNOWNC'].attrs['standard_name'] = 'surface_snow_thickness'
ds['SNOWNC'].attrs[
'long_name'] = 'Accumulated Total Grid Scale Snow and Ice'
ds['SNOWNC'].attrs['description'] = '{}; water equivalent'.format(
ds['SNOWNC'].description)
ds['SST'].attrs['standard_name'] = 'sea_surface_temperature'
ds['SST'].attrs['long_name'] = 'Sea Surface Temperature'
ds['DIFFUSE_FRAC'].attrs[
'long_name'] = 'Diffuse Fraction of Surface Shortwave Irradiance'
ds['MDBZ'].attrs['long_name'] = 'Maximum Radar Reflectivity'
ds['TSK'].attrs['long_name'] = 'Surface Skin Temperature'
ds['LANDMASK'].attrs['standard_name'] = 'land_binary_mask'
ds['LANDMASK'].attrs['long_name'] = 'Land Mask'
ds['LAKEMASK'].attrs['long_name'] = 'Lake Mask'
ds['PBLH'].attrs[
'long_name'] = 'Height of the Top of the Planetary Boundary Layer (PBL)'
ds['UST'].attrs['long_name'] = 'Friction Velocity'
ds['XTIME'].attrs['long_name'] = 'minutes since simulation start'
# add the calculated wind gust to the dataset
windgust_attrs = dict(
long_name='Near Surface Wind Gust',
description=
'Calculated wind gust, computed by mixing down momentum from the level at the '
'top of the planetary boundary layer',
units='m s-1')
ds['WINDGUST'] = xr.Variable(gust.dims, gust.values, attrs=windgust_attrs)
ds['WINDGUST'] = ds['WINDGUST'].expand_dims('Time', axis=0)
# Set time attribute
ds['Time'].attrs['standard_name'] = 'time'
datetime_format = '%Y%m%dT%H%M%SZ'
created = pd.Timestamp(pd.datetime.utcnow()).strftime(
datetime_format) # creation time Timestamp
time_start = pd.Timestamp(pd.Timestamp(
ds.Time.data[0])).strftime(datetime_format)
time_end = pd.Timestamp(pd.Timestamp(
ds.Time.data[0])).strftime(datetime_format)
global_attributes = OrderedDict([
('title', 'Rutgers Weather Research and Forecasting Model'),
('summary', 'Processed netCDF containing subset of RUWRF output'),
('keywords', 'Weather Advisories > Marine Weather/Forecast'),
('Conventions', 'CF-1.7'),
('naming_authority', 'edu.rutgers.marine.rucool'),
('history',
'Hourly WRF raw output processed into new hourly file with selected variables.'
), ('processing_level', 'Level 2'),
('comment', 'WRF Model operated by RUCOOL'),
('acknowledgement',
'This data is provided by the Rutgers Center for Ocean Observing Leadership. Funding is provided by the New Jersey Board of Public Utilities).'
), ('standard_name_vocabulary', 'CF Standard Name Table v41'),
('date_created', created), ('creator_name', 'Joseph Brodie'),
('creator_email', '*****@*****.**'),
('creator_url', 'rucool.marine.rutgers.edu'),
('institution',
'Center for Ocean Observing and Leadership, Department of Marine & Coastal Sciences, Rutgers University'
),
('project',
'New Jersey Board of Public Utilities - Offshore Wind Energy - RUWRF Model'
), ('geospatial_lat_min', -90), ('geospatial_lat_max', 90),
('geospatial_lon_min', -180), ('geospatial_lon_max', 180),
('geospatial_vertical_min', 0.0), ('geospatial_vertical_max', 0.0),
('geospatial_vertical_positive', 'down'),
('time_coverage_start', time_start), ('time_coverage_end', time_end),
('creator_type', 'person'),
('creator_institution', 'Rutgers University'),
('contributor_name', 'Joseph Brodie'),
('contributor_role', 'Director of Atmospheric Research'),
('geospatial_lat_units', 'degrees_north'),
('geospatial_lon_units', 'degrees_east'), ('date_modified', created),
('date_issued', created), ('date_metadata_modified', created),
('keywords_vocabulary', 'GCMD Science Keywords'),
('platform', 'WRF Model Run'), ('cdm_data_type', 'Grid'),
('references',
'http://maracoos.org/node/146 https://rucool.marine.rutgers.edu/facilities https://rucool.marine.rutgers.edu/data'
)
])
global_attributes.update(original_global_attributes)
ds = ds.assign_attrs(global_attributes)
# Add compression to all variables
encoding = {}
for k in ds.data_vars:
encoding[k] = {'zlib': True, 'complevel': 1}
# add the encoding for time so xarray exports the proper time.
# Also remove compression from dimensions. They should never have fill values
encoding['Time'] = dict(units=time_units,
calendar='gregorian',
zlib=False,
_FillValue=False,
dtype=np.double)
encoding['XLONG'] = dict(zlib=False, _FillValue=False)
encoding['XLAT'] = dict(zlib=False, _FillValue=False)
encoding['height'] = dict(zlib=False, _FillValue=False, dtype=np.int32)
ds.to_netcdf(save_file,
encoding=encoding,
format='netCDF4',
engine='netcdf4',
unlimited_dims='Time')
def test(self):
try:
from netCDF4 import Dataset as NetCDF
except ImportError:
pass
try:
import Nio
except ImportError:
pass
timeidx = 0 if not multi else None
pat = os.path.join(dir, pattern)
wrf_files = glob.glob(pat)
wrf_files.sort()
wrfin = []
for fname in wrf_files:
if not pynio:
f = NetCDF(fname)
try:
f.set_always_mask(False)
except AttributeError:
pass
wrfin.append(f)
else:
if not fname.endswith(".nc"):
_fname = fname + ".nc"
else:
_fname = fname
f = Nio.open_file(_fname)
wrfin.append(f)
if (varname == "interplevel"):
ref_ht_500 = _get_refvals(referent, "z_500", multi)
ref_p_5000 = _get_refvals(referent, "p_5000", multi)
ref_ht_multi = _get_refvals(referent, "z_multi", multi)
ref_p_multi = _get_refvals(referent, "p_multi", multi)
ref_ht2_500 = _get_refvals(referent, "z2_500", multi)
ref_p2_5000 = _get_refvals(referent, "p2_5000", multi)
ref_ht2_multi = _get_refvals(referent, "z2_multi", multi)
ref_p2_multi = _get_refvals(referent, "p2_multi", multi)
ref_p_lev2d = _get_refvals(referent, "p_lev2d", multi)
hts = getvar(wrfin, "z", timeidx=timeidx)
p = getvar(wrfin, "pressure", timeidx=timeidx)
wspd_wdir = getvar(wrfin, "wspd_wdir", timeidx=timeidx)
# Make sure the numpy versions work first
hts_500 = interplevel(to_np(hts), to_np(p), 500)
hts_500 = interplevel(hts, p, 500)
# Note: the '*2*' versions in the reference are testing
# against the new version of interplevel in NCL
nt.assert_allclose(to_np(hts_500), ref_ht_500)
nt.assert_allclose(to_np(hts_500), ref_ht2_500)
# Make sure the numpy versions work first
p_5000 = interplevel(to_np(p), to_np(hts), 5000)
p_5000 = interplevel(p, hts, 5000)
nt.assert_allclose(to_np(p_5000), ref_p_5000)
nt.assert_allclose(to_np(p_5000), ref_p2_5000)
hts_multi = interplevel(to_np(hts), to_np(p),
[1000., 850., 500., 250.])
hts_multi = interplevel(hts, p, [1000., 850., 500., 250.])
nt.assert_allclose(to_np(hts_multi), ref_ht_multi)
nt.assert_allclose(to_np(hts_multi), ref_ht2_multi)
p_multi = interplevel(to_np(p), to_np(hts),
[500., 2500., 5000., 10000.])
p_multi = interplevel(p, hts, [500., 2500., 5000., 10000.])
nt.assert_allclose(to_np(p_multi), ref_p_multi)
nt.assert_allclose(to_np(p_multi), ref_p2_multi)
pblh = getvar(wrfin, "PBLH", timeidx=timeidx)
p_lev2d = interplevel(to_np(p), to_np(hts), to_np(pblh))
p_lev2d = interplevel(p, hts, pblh)
nt.assert_allclose(to_np(p_lev2d), ref_p_lev2d)
# Just make sure these run below
wspd_wdir_500 = interplevel(to_np(wspd_wdir), to_np(p), 500)
wspd_wdir_500 = interplevel(wspd_wdir, p, 500)
wspd_wdir_multi = interplevel(to_np(wspd_wdir), to_np(p),
[1000, 500, 250])
wdpd_wdir_multi = interplevel(wspd_wdir, p, [1000, 500, 250])
wspd_wdir_pblh = interplevel(to_np(wspd_wdir), to_np(hts), pblh)
wspd_wdir_pblh = interplevel(wspd_wdir, hts, pblh)
if multi:
wspd_wdir_pblh_2 = interplevel(to_np(wspd_wdir), to_np(hts),
pblh[0, :])
wspd_wdir_pblh_2 = interplevel(wspd_wdir, hts, pblh[0, :])
# Since PBLH doesn't change in this case, it should match
# the 0 time from previous computation. Note that this
# only works when the data has 1 time step that is repeated.
# If you use a different case with multiple times,
# this will probably fail.
nt.assert_allclose(to_np(wspd_wdir_pblh_2[:, 0, :]),
to_np(wspd_wdir_pblh[:, 0, :]))
nt.assert_allclose(to_np(wspd_wdir_pblh_2[:, -1, :]),
to_np(wspd_wdir_pblh[:, 0, :]))
elif (varname == "vertcross"):
ref_ht_cross = _get_refvals(referent, "ht_cross", multi)
ref_p_cross = _get_refvals(referent, "p_cross", multi)
ref_ht_vertcross1 = _get_refvals(referent, "ht_vertcross1", multi)
ref_ht_vertcross2 = _get_refvals(referent, "ht_vertcross2", multi)
ref_ht_vertcross3 = _get_refvals(referent, "ht_vertcross3", multi)
hts = getvar(wrfin, "z", timeidx=timeidx)
p = getvar(wrfin, "pressure", timeidx=timeidx)
pivot_point = CoordPair(hts.shape[-1] / 2, hts.shape[-2] / 2)
# Beginning in wrf-python 1.3, users can select number of levels.
# Originally, for pressure, dz was 10, so let's back calculate
# the number of levels.
p_max = np.floor(np.amax(p) / 10) * 10 # bottom value
p_min = np.ceil(np.amin(p) / 10) * 10 # top value
p_autolevels = int((p_max - p_min) / 10)
# Make sure the numpy versions work first
ht_cross = vertcross(to_np(hts),
to_np(p),
pivot_point=pivot_point,
angle=90.,
autolevels=p_autolevels)
ht_cross = vertcross(hts,
p,
pivot_point=pivot_point,
angle=90.,
autolevels=p_autolevels)
try:
nt.assert_allclose(to_np(ht_cross), ref_ht_cross, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(ht_cross) - ref_ht_cross)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
lats = hts.coords["XLAT"]
lons = hts.coords["XLONG"]
# Test the manual projection method with lat/lon
# Only do this for the non-multi case, since the domain
# might be moving
if not multi:
if lats.ndim > 2: # moving nest
lats = lats[0, :]
lons = lons[0, :]
ll_point = ll_points(lats, lons)
pivot = CoordPair(lat=lats[int(lats.shape[-2] / 2),
int(lats.shape[-1] / 2)],
lon=lons[int(lons.shape[-2] / 2),
int(lons.shape[-1] / 2)])
v1 = vertcross(hts,
p,
wrfin=wrfin,
pivot_point=pivot_point,
angle=90.0)
v2 = vertcross(hts,
p,
projection=hts.attrs["projection"],
ll_point=ll_point,
pivot_point=pivot_point,
angle=90.)
try:
nt.assert_allclose(to_np(v1), to_np(v2), atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(v1) - to_np(v2))
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Test opposite
p_cross1 = vertcross(p, hts, pivot_point=pivot_point, angle=90.0)
try:
nt.assert_allclose(to_np(p_cross1), ref_p_cross, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(p_cross1) - ref_p_cross)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Test point to point
start_point = CoordPair(0, hts.shape[-2] / 2)
end_point = CoordPair(-1, hts.shape[-2] / 2)
p_cross2 = vertcross(p,
hts,
start_point=start_point,
end_point=end_point)
try:
nt.assert_allclose(to_np(p_cross1),
to_np(p_cross2),
atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(p_cross1) - to_np(p_cross2))
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Check the new vertcross routine
pivot_point = CoordPair(hts.shape[-1] / 2, hts.shape[-2] / 2)
ht_cross = vertcross(hts,
p,
pivot_point=pivot_point,
angle=90.,
latlon=True)
try:
nt.assert_allclose(to_np(ht_cross),
to_np(ref_ht_vertcross1),
atol=.005)
except AssertionError:
absdiff = np.abs(to_np(ht_cross) - to_np(ref_ht_vertcross1))
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
levels = [1000., 850., 700., 500., 250.]
ht_cross = vertcross(hts,
p,
pivot_point=pivot_point,
angle=90.,
levels=levels,
latlon=True)
try:
nt.assert_allclose(to_np(ht_cross),
to_np(ref_ht_vertcross2),
atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(ht_cross) - to_np(ref_ht_vertcross2))
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
idxs = (0, slice(None)) if lats.ndim > 2 else (slice(None), )
start_lat = np.amin(
lats[idxs]) + .25 * (np.amax(lats[idxs]) - np.amin(lats[idxs]))
end_lat = np.amin(
lats[idxs]) + .65 * (np.amax(lats[idxs]) - np.amin(lats[idxs]))
start_lon = np.amin(
lons[idxs]) + .25 * (np.amax(lons[idxs]) - np.amin(lons[idxs]))
end_lon = np.amin(
lons[idxs]) + .65 * (np.amax(lons[idxs]) - np.amin(lons[idxs]))
start_point = CoordPair(lat=start_lat, lon=start_lon)
end_point = CoordPair(lat=end_lat, lon=end_lon)
ll_point = ll_points(lats[idxs], lons[idxs])
ht_cross = vertcross(hts,
p,
start_point=start_point,
end_point=end_point,
projection=hts.attrs["projection"],
ll_point=ll_point,
latlon=True,
autolevels=1000)
try:
nt.assert_allclose(to_np(ht_cross),
to_np(ref_ht_vertcross3),
atol=.01)
except AssertionError:
absdiff = np.abs(to_np(ht_cross) - to_np(ref_ht_vertcross3))
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
if multi:
ntimes = hts.shape[0]
for t in range(ntimes):
hts = getvar(wrfin, "z", timeidx=t)
p = getvar(wrfin, "pressure", timeidx=t)
ht_cross = vertcross(hts,
p,
start_point=start_point,
end_point=end_point,
wrfin=wrfin,
timeidx=t,
latlon=True,
autolevels=1000)
refname = "ht_vertcross_t{}".format(t)
ref_ht_vertcross = _get_refvals(referent, refname, False)
try:
nt.assert_allclose(to_np(ht_cross),
to_np(ref_ht_vertcross),
atol=.01)
except AssertionError:
absdiff = np.abs(
to_np(ht_cross) - to_np(ref_ht_vertcross))
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
elif (varname == "interpline"):
ref_t2_line = _get_refvals(referent, "t2_line", multi)
ref_t2_line2 = _get_refvals(referent, "t2_line2", multi)
ref_t2_line3 = _get_refvals(referent, "t2_line3", multi)
t2 = getvar(wrfin, "T2", timeidx=timeidx)
pivot_point = CoordPair(t2.shape[-1] / 2, t2.shape[-2] / 2)
# Make sure the numpy version works
t2_line1 = interpline(to_np(t2),
pivot_point=pivot_point,
angle=90.0)
t2_line1 = interpline(t2, pivot_point=pivot_point, angle=90.0)
nt.assert_allclose(to_np(t2_line1), ref_t2_line)
# Test the new NCL wrf_user_interplevel result
try:
nt.assert_allclose(to_np(t2_line1), ref_t2_line2)
except AssertionError:
absdiff = np.abs(to_np(t2_line1) - ref_t2_line2)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Test the manual projection method with lat/lon
lats = t2.coords["XLAT"]
lons = t2.coords["XLONG"]
if multi:
if lats.ndim > 2: # moving nest
lats = lats[0, :]
lons = lons[0, :]
ll_point = ll_points(lats, lons)
pivot = CoordPair(lat=lats[int(lats.shape[-2] / 2),
int(lats.shape[-1] / 2)],
lon=lons[int(lons.shape[-2] / 2),
int(lons.shape[-1] / 2)])
l1 = interpline(t2,
wrfin=wrfin,
pivot_point=pivot_point,
angle=90.0)
l2 = interpline(t2,
projection=t2.attrs["projection"],
ll_point=ll_point,
pivot_point=pivot_point,
angle=90.)
try:
nt.assert_allclose(to_np(l1), to_np(l2))
except AssertionError:
absdiff = np.abs(to_np(l1) - to_np(l2))
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Test point to point
start_point = CoordPair(0, t2.shape[-2] / 2)
end_point = CoordPair(-1, t2.shape[-2] / 2)
t2_line2 = interpline(t2,
start_point=start_point,
end_point=end_point)
try:
nt.assert_allclose(to_np(t2_line1), to_np(t2_line2))
except AssertionError:
absdiff = np.abs(to_np(t2_line1) - t2_line2)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Now test the start/end with lat/lon points
start_lat = float(
np.amin(lats) + .25 * (np.amax(lats) - np.amin(lats)))
end_lat = float(
np.amin(lats) + .65 * (np.amax(lats) - np.amin(lats)))
start_lon = float(
np.amin(lons) + .25 * (np.amax(lons) - np.amin(lons)))
end_lon = float(
np.amin(lons) + .65 * (np.amax(lons) - np.amin(lons)))
start_point = CoordPair(lat=start_lat, lon=start_lon)
end_point = CoordPair(lat=end_lat, lon=end_lon)
t2_line3 = interpline(t2,
wrfin=wrfin,
timeidx=0,
start_point=start_point,
end_point=end_point,
latlon=True)
try:
nt.assert_allclose(to_np(t2_line3), ref_t2_line3, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(t2_line3) - ref_t2_line3)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Test all time steps
if multi:
refnc = NetCDF(referent)
ntimes = t2.shape[0]
for t in range(ntimes):
t2 = getvar(wrfin, "T2", timeidx=t)
line = interpline(t2,
wrfin=wrfin,
timeidx=t,
start_point=start_point,
end_point=end_point,
latlon=True)
refname = "t2_line_t{}".format(t)
refline = refnc.variables[refname][:]
try:
nt.assert_allclose(to_np(line),
to_np(refline),
atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(line) - refline)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
refnc.close()
# Test NCLs single time case
if not multi:
refnc = NetCDF(referent)
ref_t2_line4 = refnc.variables["t2_line4"][:]
t2 = getvar(wrfin, "T2", timeidx=0)
line = interpline(t2,
wrfin=wrfin,
timeidx=0,
start_point=start_point,
end_point=end_point,
latlon=True)
try:
nt.assert_allclose(to_np(line),
to_np(ref_t2_line4),
atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(line) - ref_t2_line4)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
finally:
refnc.close()
elif (varname == "vinterp"):
# Tk to theta
fld_tk_theta = _get_refvals(referent, "fld_tk_theta", multi)
fld_tk_theta = np.squeeze(fld_tk_theta)
tk = getvar(wrfin, "temp", timeidx=timeidx, units="k")
interp_levels = [200, 300, 500, 1000]
# Make sure the numpy version works
field = vinterp(wrfin,
field=to_np(tk),
vert_coord="theta",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = vinterp(wrfin,
field=tk,
vert_coord="theta",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
tol = 0 / 100.
atol = 0.0001
field = np.squeeze(field)
try:
nt.assert_allclose(to_np(field), fld_tk_theta)
except AssertionError:
absdiff = np.abs(to_np(field) - fld_tk_theta)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Tk to theta-e
fld_tk_theta_e = _get_refvals(referent, "fld_tk_theta_e", multi)
fld_tk_theta_e = np.squeeze(fld_tk_theta_e)
interp_levels = [200, 300, 500, 1000]
field = vinterp(wrfin,
field=tk,
vert_coord="theta-e",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
try:
nt.assert_allclose(to_np(field), fld_tk_theta_e, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(field) - fld_tk_theta_e)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Tk to pressure
fld_tk_pres = _get_refvals(referent, "fld_tk_pres", multi)
fld_tk_pres = np.squeeze(fld_tk_pres)
interp_levels = [850, 500]
field = vinterp(wrfin,
field=tk,
vert_coord="pressure",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
try:
nt.assert_allclose(to_np(field), fld_tk_pres, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(field) - fld_tk_pres)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Tk to geoht_msl
fld_tk_ght_msl = _get_refvals(referent, "fld_tk_ght_msl", multi)
fld_tk_ght_msl = np.squeeze(fld_tk_ght_msl)
interp_levels = [1, 2]
field = vinterp(wrfin,
field=tk,
vert_coord="ght_msl",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
try:
nt.assert_allclose(to_np(field), fld_tk_ght_msl, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(field) - fld_tk_ght_msl)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Tk to geoht_agl
fld_tk_ght_agl = _get_refvals(referent, "fld_tk_ght_agl", multi)
fld_tk_ght_agl = np.squeeze(fld_tk_ght_agl)
interp_levels = [1, 2]
field = vinterp(wrfin,
field=tk,
vert_coord="ght_agl",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
try:
nt.assert_allclose(to_np(field), fld_tk_ght_agl, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(field) - fld_tk_ght_agl)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Hgt to pressure
fld_ht_pres = _get_refvals(referent, "fld_ht_pres", multi)
fld_ht_pres = np.squeeze(fld_ht_pres)
z = getvar(wrfin, "height", timeidx=timeidx, units="m")
interp_levels = [500, 50]
field = vinterp(wrfin,
field=z,
vert_coord="pressure",
interp_levels=interp_levels,
extrapolate=True,
field_type="ght",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
try:
nt.assert_allclose(to_np(field), fld_ht_pres, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(field) - fld_ht_pres)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Pressure to theta
fld_pres_theta = _get_refvals(referent, "fld_pres_theta", multi)
fld_pres_theta = np.squeeze(fld_pres_theta)
p = getvar(wrfin, "pressure", timeidx=timeidx)
interp_levels = [200, 300, 500, 1000]
field = vinterp(wrfin,
field=p,
vert_coord="theta",
interp_levels=interp_levels,
extrapolate=True,
field_type="pressure",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
try:
nt.assert_allclose(to_np(field), fld_pres_theta, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(field) - fld_pres_theta)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
# Theta-e to pres
fld_thetae_pres = _get_refvals(referent, "fld_thetae_pres", multi)
fld_thetae_pres = np.squeeze(fld_thetae_pres)
eth = getvar(wrfin, "eth", timeidx=timeidx)
interp_levels = [850, 500, 5]
field = vinterp(wrfin,
field=eth,
vert_coord="pressure",
interp_levels=interp_levels,
extrapolate=True,
field_type="theta-e",
timeidx=timeidx,
log_p=True)
field = np.squeeze(field)
try:
nt.assert_allclose(to_np(field), fld_thetae_pres, atol=.0001)
except AssertionError:
absdiff = np.abs(to_np(field) - fld_thetae_pres)
maxdiff = np.amax(absdiff)
print(maxdiff)
print(np.argwhere(absdiff == maxdiff))
raise
def make_result_file(opts):
infilename = os.path.expanduser(
os.path.join(opts.outdir, "ci_test_file.nc"))
outfilename = os.path.expanduser(
os.path.join(opts.outdir, "ci_result_file.nc"))
with Dataset(infilename) as infile, Dataset(outfilename, "w") as outfile:
for varname in WRF_DIAGS:
var = getvar(infile, varname)
add_to_ncfile(outfile, var, varname)
for interptype in INTERP_METHS:
if interptype == "interpline":
hts = getvar(infile, "z")
p = getvar(infile, "pressure")
hts_850 = interplevel(hts, p, 850.)
add_to_ncfile(outfile, hts_850, "interplevel")
if interptype == "vertcross":
hts = getvar(infile, "z")
p = getvar(infile, "pressure")
pivot_point = CoordPair(hts.shape[-1] // 2, hts.shape[-2] // 2)
ht_cross = vertcross(hts,
p,
pivot_point=pivot_point,
angle=90.)
add_to_ncfile(outfile, ht_cross, "vertcross")
if interptype == "interpline":
t2 = getvar(infile, "T2")
pivot_point = CoordPair(t2.shape[-1] // 2, t2.shape[-2] // 2)
t2_line = interpline(t2, pivot_point=pivot_point, angle=90.0)
add_to_ncfile(outfile, t2_line, "interpline")
if interptype == "vinterp":
tk = getvar(infile, "temp", units="k")
interp_levels = [200, 300, 500, 1000]
field = vinterp(infile,
field=tk,
vert_coord="theta",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
log_p=True)
add_to_ncfile(outfile, field, "vinterp")
for latlonmeth in LATLON_METHS:
if latlonmeth == "xy":
# Hardcoded values from other unit tests
lats = [-55, -60, -65]
lons = [25, 30, 35]
xy = ll_to_xy(infile, lats[0], lons[0])
add_to_ncfile(outfile, xy, "xy")
else:
# Hardcoded from other unit tests
i_s = np.asarray([10, 100, 150], int) - 1
j_s = np.asarray([10, 100, 150], int) - 1
ll = xy_to_ll(infile, i_s[0], j_s[0])
add_to_ncfile(outfile, ll, "ll")
v_tmp = np.empty((times.shape[0], z.shape[0], v_int.shape[2], v_int.shape[3]))
w_tmp = np.empty((times.shape[0], zw.shape[0], y.shape[0], x.shape[0]))
qv_tmp = np.empty(
(times.shape[0], z.shape[0], qv_int.shape[2], qv_int.shape[3]))
pt_tmp = np.empty(
(times.shape[0], z.shape[0], pt_int.shape[2], pt_int.shape[3]))
pres_tmp = np.empty(
(times.shape[0], z.shape[0], pres_int.shape[2], pres_int.shape[3]))
tk_tmp = np.empty(
(times.shape[0], z.shape[0], tk_int.shape[2], tk_int.shape[3]))
for l_idx, l in tqdm(enumerate(z),
desc="Interpolating unstaggered vertical levels"):
for t in range(0, times.shape[0]):
qv_tmp[t,
int(l_idx), :, :] = interplevel(qv_int[t, :, :, :],
z_wrf_int[t, :, :, :], l).data
pt_tmp[t,
int(l_idx), :, :] = interplevel(pt_int[t, :, :, :],
z_wrf_int[t, :, :, :], l).data
u_tmp[t,
int(l_idx), :, :] = interplevel(u_int[t, :, :, :],
z_wrf_int_u[t, :, :, :], l).data
v_tmp[t,
int(l_idx), :, :] = interplevel(v_int[t, :, :, :],
z_wrf_int_v[t, :, :, :], l).data
pres_tmp[t,
int(l_idx), :, :] = interplevel(pres_int[t, :, :, :],
z_wrf_int[t, :, :, :], l).data
tk_tmp[t,
int(l_idx), :, :] = interplevel(tk_int[t, :, :, :],
z_wrf_int[t, :, :, :], l).data
timeneed = datelist(timeperiod1[tp], timeperiod2[tp])
os.chdir("/scratch/zhaohuiw/PWRF_non_staticseaice_ib/WRFV3/test/em_real")
x = np.zeros((264, 699, 699))
i = 0
for date in timeneed:
filename = 'wrfout_d01_' + date + '_00:00:00'
ncfile = Dataset(filename)
# Extract the Geopotential Height and Pressure (hPa) fields
z = getvar(ncfile, "z", units='dm',
timeidx=ALL_TIMES) #change the variable to what we need
p = getvar(ncfile, "pressure",
timeidx=ALL_TIMES) #change the variable to what we need
# Compute the 500 MB Geopotential Height
ht_500mb = interplevel(z, p, 500.)
x[24 * i:24 * (i + 1), :, :] = ht_500mb
i = i + 1
del i
os.chdir("/scratch/zhaohuiw/wrf_variable")
sio.savemat('ht_500mb_non_staticseaice_' + filename[11:21] + '.mat',
{'ht_500mb': x}) # give x the variable name
###################################
os.chdir("/scratch/zhaohuiw/PWRF_staticseaice_ib/WRFV3/test/em_real")
x = np.zeros((264, 699, 699))
i = 0
for date in timeneed:
filename = 'wrfout_d01_' + date + '_00:00:00'
ncfile = Dataset(filename)
start_point=startpoint,
end_point=endpoint,
latlon=True)
lonlist, latlist = [], []
plist = ua_vert.coords['vertical']
print(plist)
for i in range(len(ua_vert.coords['xy_loc'])):
s = str(ua_vert.coords['xy_loc'][i].values)
lonlist.append(float(s[s.find('lon=') + 4:s.find('lon=') + 12]))
latlist.append(float(s[s.find('lat=') + 4:s.find('lat=') + 12]))
print(lonlist)
print(latlist)
hlist = []
for i in range(1000, 700, -30):
hlist.append(float(np.max(interplevel(height2earth, p, i)).values))
hlist = np.array([int(i) for i in hlist])
print(hlist)
plist = to_np(plist)
fig = plt.figure(figsize=(12, 8), dpi=150)
axe_1 = plt.subplot(1, 1, 1) #这里可以设置多个子图，第一个参数表示多少行，第二个表示多少列，第三个表示第几个子图
axe_1.set_title(str(Readtime.get_ncfile_time(ncfile, timezone=8)[time]),
fontsize=12)
start_x, end_x = start_lon, end_lon
small_p, big_p = 700, 1000
big_interval_x, small_interval_x, big_interval_p, small_interval_p = 0.1, 0.1, 30, 30
#axe_1.set_xlim(start_x, end_x)
axe_1.set_ylim(65, 100) # 设置图的范围
axe_1.grid(color='gray', linestyle=':', linewidth=0.7)
