def test_density():
"""Test density calculation."""
p = 999. * units.mbar
t = 288. * units.kelvin
qv = .0016 * units.dimensionless # kg/kg
rho = density(p, t, qv).to(units.kilogram / units.meter ** 3)
assert_almost_equal(rho, 1.2072 * (units.kilogram / units.meter ** 3), 3)
def test_density():
"""Test density calculation."""
p = 999. * units.mbar
t = 288. * units.kelvin
qv = .0016 # kg/kg
rho = density(p, t, qv).to(units.kilogram / units.meter ** 3)
assert_almost_equal(rho, 1.2072 * (units.kilogram / units.meter ** 3), 3)
def calculateDensity(sonde):
#Formula rho=pres/RaT*(1+x)/(1+x*Rw/Ra)
sonde["play"].attrs['units'] = 'Pa'
sonde["tlay"].attrs['units'] = 'K'
rho = mpcalc.density(sonde["play"], sonde["tlay"], sonde["mr"])
sonde["rho"] = (["play"], rho.magnitude)
return sonde
def equation_of_state(p, t):
mixing = mpcalc.saturation_mixing_ratio(p * units.hPa, t *
units.kelvin).to('dimensionless')
#mpcalc.virtual_temperature(t,r, molecular_weight_ratio=0.6219800858985514)
density = mpcalc.density(p * units.hPa, t * units.kelvin, mixing)
return density
def get_density_vertical_velocity_and_omega(circle):
den_m = [None] * len(circle.sounding)
for n in range(len(circle.sounding)):
if len(circle.isel(sounding=n).sonde_id.values) > 1:
mr = mpcalc.mixing_ratio_from_specific_humidity(
circle.isel(sounding=n).q_sounding.values
)
den_m[n] = mpcalc.density(
circle.isel(sounding=n).p_sounding.values * units.Pa,
circle.isel(sounding=n).ta_sounding.values * units.kelvin,
mr,
).magnitude
else:
den_m[n] = np.nan
circle["density"] = (["sounding", "circle", "alt"], den_m)
circle["mean_density"] = (["circle", "alt"], np.nanmean(den_m, axis=0))
D = circle.D.values
mean_den = circle.mean_density
nan_ids = np.where(np.isnan(D) == True) # [0]
w_vel = np.full([len(circle["circle"]), len(circle.alt)], np.nan)
p_vel = np.full([len(circle["circle"]), len(circle.alt)], np.nan)
w_vel[:, 0] = 0
# last = 0
for cir in range(len(circle["circle"])):
last = 0
for m in range(1, len(circle.alt)):
if (
len(
np.intersect1d(
np.where(nan_ids[1] == m)[0], np.where(nan_ids[0] == cir)[0]
)
)
> 0
):
ids_for_nan_ids = np.intersect1d(
np.where(nan_ids[1] == m)[0], np.where(nan_ids[0] == cir)[0]
)
w_vel[nan_ids[0][ids_for_nan_ids], nan_ids[1][ids_for_nan_ids]] = np.nan
else:
w_vel[cir, m] = w_vel[cir, last] - circle.D.isel(circle=cir).isel(
alt=m
).values * 10 * (m - last)
last = m
for n in range(1, len(circle.alt)):
p_vel[cir, n] = (
-circle.mean_density.isel(circle=cir).isel(alt=n)
* 9.81
* w_vel[cir, n]
# * 60
# * 60
# / 100
)
circle["W"] = (["circle", "alt"], w_vel)
circle["omega"] = (["circle", "alt"], p_vel)
return print("Finished estimating density, W and omega ...")
def main():
load_start = dt.datetime.now()
#Try parsing arguments using argparse
parser = argparse.ArgumentParser(
description='wrf non-parallel convective diagnostics processer')
parser.add_argument("-m", help="Model name", required=True)
parser.add_argument("-r",
help="Region name (default is aus)",
default="aus")
parser.add_argument("-t1", help="Time start YYYYMMDDHH", required=True)
parser.add_argument("-t2", help="Time end YYYYMMDDHH", required=True)
parser.add_argument(
"-e",
help=
"CMIP5 experiment name (not required if using era5, erai or barra)",
default="")
parser.add_argument(
"--barpa_forcing_mdl",
help="BARPA forcing model (erai or ACCESS1-0). Default erai.",
default="erai")
parser.add_argument(
"--ens",
help="CMIP5 ensemble name (not required if using era5, erai or barra)",
default="r1i1p1")
parser.add_argument("--group",
help="CMIP6 modelling group name",
default="")
parser.add_argument("--project",
help="CMIP6 modelling intercomparison project",
default="CMIP")
parser.add_argument("--ver6hr",
help="Version on al33 for 6hr data",
default="")
parser.add_argument("--ver3hr",
help="Version on al33 for 3hr data",
default="")
parser.add_argument("--issave",
help="Save output (True or False, default is False)",
default="False")
parser.add_argument(
"--ub4",
help=
"Where to get era5 data. Default True for ub4 project, otherwise rt52",
default="True")
parser.add_argument(
"--outname",
help=
"Name of saved output. In the form *outname*_*t1*_*t2*.nc. Default behaviour is the model name",
default=None)
parser.add_argument(
"--is_dcape",
help="Should DCAPE be calculated? (1 or 0. Default is 1)",
default=1)
parser.add_argument(
"--al33",
help=
"Should data be gathered from al33? Default is False, and data is gathered from r87. If True, then group is required",
default="False")
parser.add_argument(
"--delta_t",
help=
"Time step spacing for ERA5 data, in hours. Default is one the minimum spacing (1 hour)",
default="1")
parser.add_argument(
"--era5_interp",
help=
"Horizontally interpolate model data before calculating convective parameters",
default="False")
args = parser.parse_args()
#Parse arguments from cmd line and set up inputs (date region model)
model = args.m
region = args.r
t1 = args.t1
t2 = args.t2
issave = args.issave
ub4 = args.ub4
al33 = args.al33
if args.outname == None:
out_name = model
else:
out_name = args.outname
is_dcape = args.is_dcape
barpa_forcing_mdl = args.barpa_forcing_mdl
experiment = args.e
ensemble = args.ens
group = args.group
project = args.project
ver6hr = args.ver6hr
ver3hr = args.ver3hr
delta_t = int(args.delta_t)
era5_interp = args.era5_interp
if region == "sa_small":
start_lat = -38
end_lat = -26
start_lon = 132
end_lon = 142
elif region == "aus":
start_lat = -44.525
end_lat = -9.975
start_lon = 111.975
end_lon = 156.275
elif region == "global":
start_lat = -70
end_lat = 70
start_lon = -180
end_lon = 179.75
else:
raise ValueError("INVALID REGION\n")
domain = [start_lat, end_lat, start_lon, end_lon]
try:
time = [
dt.datetime.strptime(t1, "%Y%m%d%H"),
dt.datetime.strptime(t2, "%Y%m%d%H")
]
except:
raise ValueError("INVALID START OR END TIME. SHOULD BE YYYYMMDDHH\n")
if era5_interp == "True":
era5_interp = True
elif era5_interp == "False":
era5_interp = False
else:
raise ValueError("\n INVALID era5_interp...SHOULD BE True OR False")
if ub4 == "True":
ub4 = True
elif ub4 == "False":
ub4 = False
else:
raise ValueError("\n INVALID ub4...SHOULD BE True OR False")
if issave == "True":
issave = True
elif issave == "False":
issave = False
else:
raise ValueError("\n INVALID ISSAVE...SHOULD BE True OR False")
if al33 == "True":
al33 = True
elif al33 == "False":
al33 = False
else:
raise ValueError("\n INVALID al33...SHOULD BE True OR False")
#Load data
print("LOADING DATA...")
if model == "erai":
ta,temp1,hur,hgt,terrain,p,ps,wap,ua,va,uas,vas,tas,ta2d,\
cp,tp,wg10,mod_cape,lon,lat,date_list = \
read_erai(domain,time)
cp = cp.astype("float32", order="C")
tp = tp.astype("float32", order="C")
mod_cape = mod_cape.astype("float32", order="C")
elif model == "era5":
if ub4:
ta,temp1,hur,hgt,terrain,p,ps,ua,va,uas,vas,tas,ta2d,\
cp,wg10,mod_cape,lon,lat,date_list = \
read_era5(domain,time,delta_t=delta_t)
else:
ta,temp1,hur,hgt,terrain,p,ps,ua,va,uas,vas,tas,ta2d,\
cp,tp,wg10,mod_cape,lon,lat,date_list = \
read_era5_rt52(domain,time,delta_t=delta_t)
cp = cp.astype("float32", order="C")
tp = tp.astype("float32", order="C")
mod_cape = mod_cape.astype("float32", order="C")
wap = np.zeros(hgt.shape)
elif model == "barra":
ta,temp1,hur,hgt,terrain,p,ps,wap,ua,va,uas,vas,tas,ta2d,wg10,lon,lat,date_list = \
read_barra(domain,time)
elif model == "barra_fc":
ta,temp1,hur,hgt,terrain,p,ps,wap,ua,va,uas,vas,tas,ta2d,wg10,lon,lat,date_list = \
read_barra_fc(domain,time)
elif model == "barpa":
ta,hur,hgt,terrain,p,ps,ua,va,uas,vas,tas,ta2d,wg10,lon,lat,date_list = \
read_barpa(domain, time, experiment, barpa_forcing_mdl, ensemble)
wap = np.zeros(hgt.shape)
temp1 = None
elif model == "barra_ad":
wg10,temp2,ta,temp1,hur,hgt,terrain,p,ps,wap,ua,va,uas,vas,tas,ta2d,lon,lat,date_list = \
read_barra_ad(domain, time, False)
elif model in ["ACCESS1-0","ACCESS1-3","GFDL-CM3","GFDL-ESM2M","CNRM-CM5","MIROC5",\
"MRI-CGCM3","IPSL-CM5A-LR","IPSL-CM5A-MR","GFDL-ESM2G","bcc-csm1-1","MIROC-ESM",\
"BNU-ESM"]:
#Check that t1 and t2 are in the same year
year = np.arange(int(t1[0:4]), int(t2[0:4]) + 1)
ta, hur, hgt, terrain, p_3d, ps, ua, va, uas, vas, tas, ta2d, tp, lon, lat, \
date_list = read_cmip(model, experiment, \
ensemble, year, domain, cmip_ver=5, al33=al33, group=group, ver6hr=ver6hr, ver3hr=ver3hr)
wap = np.zeros(hgt.shape)
wg10 = np.zeros(ps.shape)
mod_cape = np.zeros(ps.shape)
p = np.zeros(p_3d[0, :, 0, 0].shape)
#date_list = pd.to_datetime(date_list).to_pydatetime()
temp1 = None
tp = tp.astype("float32", order="C")
elif model in ["ACCESS-ESM1-5", "ACCESS-CM2"]:
year = np.arange(int(t1[0:4]), int(t2[0:4]) + 1)
ta, hur, hgt, terrain, p_3d, ps, ua, va, uas, vas, tas, ta2d, lon, lat, \
date_list = read_cmip(model, experiment,\
ensemble, year, domain, cmip_ver=6, group=group, project=project)
wap = np.zeros(hgt.shape)
wg10 = np.zeros(ps.shape)
p = np.zeros(p_3d[0, :, 0, 0].shape)
#date_list = pd.to_datetime(date_list).to_pydatetime()
temp1 = None
else:
raise ValueError("Model not recognised")
del temp1
ta = ta.astype("float32", order="C")
hur = hur.astype("float32", order="C")
hgt = hgt.astype("float32", order="C")
terrain = terrain.astype("float32", order="C")
p = p.astype("float32", order="C")
ps = ps.astype("float32", order="C")
wap = wap.astype("float32", order="C")
ua = ua.astype("float32", order="C")
va = va.astype("float32", order="C")
uas = uas.astype("float32", order="C")
vas = vas.astype("float32", order="C")
tas = tas.astype("float32", order="C")
ta2d = ta2d.astype("float32", order="C")
wg10 = wg10.astype("float32", order="C")
lon = lon.astype("float32", order="C")
lat = lat.astype("float32", order="C")
gc.collect()
param = np.array([
"mu_cape", "mu_cin", "muq", "s06", "s0500", "lr700_500", "mhgt",
"ta500", "tp"
])
if model in ["erai", "era5"]:
param = np.concatenate([param, ["mod_cape"]])
#Option to interpolate to the ERA5 grid
if era5_interp:
#Interpolate model data to the ERA5 grid
from era5_read import get_lat_lon_rt52 as get_era5_lat_lon
era5_lon, era5_lat = get_era5_lat_lon()
era5_lon_ind = np.where((era5_lon >= domain[2])
& (era5_lon <= domain[3]))[0]
era5_lat_ind = np.where((era5_lat >= domain[0])
& (era5_lat <= domain[1]))[0]
era5_lon = era5_lon[era5_lon_ind]
era5_lat = era5_lat[era5_lat_ind]
terrain = interp_era5(terrain, lon, lat, era5_lon, era5_lat, d3=False)
#Set output array
output_data = np.zeros(
(ps.shape[0], era5_lat.shape[0], era5_lon.shape[0], len(param)))
else:
output_data = np.zeros(
(ps.shape[0], ps.shape[1], ps.shape[2], len(param)))
#Assign p levels to a 3d array, with same dimensions as input variables (ta, hgt, etc.)
#If the 3d p-lvl array already exists, then declare the variable "mdl_lvl" as true.
try:
p_3d
mdl_lvl = True
full_p3d = p_3d
except:
mdl_lvl = False
if era5_interp:
p_3d = np.moveaxis(np.tile(p,[ta.shape[2],ta.shape[3],1]),[0,1,2],[1,2,0]).\
astype(np.float32)
else:
p_3d = np.moveaxis(np.tile(p,[era5_lat.shape[0],era5_lon.shape[0],1]),[0,1,2],[1,2,0]).\
astype(np.float32)
print("LOAD TIME..." + str(dt.datetime.now() - load_start))
tot_start = dt.datetime.now()
for t in np.arange(0, ta.shape[0]):
cape_start = dt.datetime.now()
if era5_interp:
ta_t = interp_era5(ta[t], lon, lat, era5_lon, era5_lat, d3=True)
hur_t = interp_era5(hur[t], lon, lat, era5_lon, era5_lat, d3=True)
hgt_t = interp_era5(hgt[t], lon, lat, era5_lon, era5_lat, d3=True)
ps_t = interp_era5(ps[t], lon, lat, era5_lon, era5_lat, d3=False)
wap_t = interp_era5(wap[t], lon, lat, era5_lon, era5_lat, d3=True)
ua_t = interp_era5(ua[t], lon, lat, era5_lon, era5_lat, d3=True)
va_t = interp_era5(va[t], lon, lat, era5_lon, era5_lat, d3=True)
uas_t = interp_era5(uas[t], lon, lat, era5_lon, era5_lat, d3=False)
vas_t = interp_era5(vas[t], lon, lat, era5_lon, era5_lat, d3=False)
tas_t = interp_era5(tas[t], lon, lat, era5_lon, era5_lat, d3=False)
ta2d_t = interp_era5(ta2d[t],
lon,
lat,
era5_lon,
era5_lat,
d3=False)
tp_t = interp_era5(tp[t], lon, lat, era5_lon, era5_lat, d3=False)
mod_cape_t = interp_era5(mod_cape[t],
lon,
lat,
era5_lon,
era5_lat,
d3=False)
else:
ta_t = ta[t]
hur_t = hur[t]
hgt_t = hgt[t]
ps_t = ps[t]
wap_t = wap[t]
ua_t = ua[t]
va_t = va[t]
uas_t = uas[t]
vas_t = vas[t]
tas_t = tas[t]
ta2d_t = ta2d[t]
tp_t = tp[t]
mod_cape_t = mod_cape[t]
print(date_list[t])
output = np.zeros((1, ps_t.shape[0], ps_t.shape[1], len(param)))
if mdl_lvl:
if era5_interp:
p_3d = interp_era5(full_p3d[t],
lon,
lat,
era5_lon,
era5_lat,
d3=True)
else:
p_3d = full_p3d[t]
dp = get_dp(hur=hur_t, ta=ta_t, dp_mask=False)
#Insert surface arrays, creating new arrays with "sfc" prefix
sfc_ta = np.insert(ta_t, 0, tas_t, axis=0)
sfc_hgt = np.insert(hgt_t, 0, terrain, axis=0)
sfc_dp = np.insert(dp, 0, ta2d_t, axis=0)
sfc_p_3d = np.insert(p_3d, 0, ps_t, axis=0)
sfc_ua = np.insert(ua_t, 0, uas_t, axis=0)
sfc_va = np.insert(va_t, 0, vas_t, axis=0)
sfc_wap = np.insert(wap_t, 0, np.zeros(vas_t.shape), axis=0)
#Sort by ascending p
a,temp1,temp2 = np.meshgrid(np.arange(sfc_p_3d.shape[0]) , np.arange(sfc_p_3d.shape[1]),\
np.arange(sfc_p_3d.shape[2]))
sort_inds = np.flip(np.lexsort([np.swapaxes(a, 1, 0), sfc_p_3d],
axis=0),
axis=0)
sfc_hgt = np.take_along_axis(sfc_hgt, sort_inds, axis=0)
sfc_dp = np.take_along_axis(sfc_dp, sort_inds, axis=0)
sfc_p_3d = np.take_along_axis(sfc_p_3d, sort_inds, axis=0)
sfc_ua = np.take_along_axis(sfc_ua, sort_inds, axis=0)
sfc_va = np.take_along_axis(sfc_va, sort_inds, axis=0)
sfc_ta = np.take_along_axis(sfc_ta, sort_inds, axis=0)
#Calculate q and wet bulb for pressure level arrays with surface values
sfc_ta_unit = units.units.degC * sfc_ta
sfc_dp_unit = units.units.degC * sfc_dp
sfc_p_unit = units.units.hectopascals * sfc_p_3d
hur_unit = mpcalc.relative_humidity_from_dewpoint(ta_t*units.units.degC, dp*units.units.degC)*\
100*units.units.percent
q_unit = mpcalc.mixing_ratio_from_relative_humidity(hur_unit,\
ta_t*units.units.degC,np.array(p_3d)*units.units.hectopascals)
sfc_hur_unit = mpcalc.relative_humidity_from_dewpoint(sfc_ta_unit, sfc_dp_unit)*\
100*units.units.percent
sfc_q_unit = mpcalc.mixing_ratio_from_relative_humidity(sfc_hur_unit,\
sfc_ta_unit,sfc_p_unit)
sfc_theta_unit = mpcalc.potential_temperature(sfc_p_unit, sfc_ta_unit)
sfc_thetae_unit = mpcalc.equivalent_potential_temperature(
sfc_p_unit, sfc_ta_unit, sfc_dp_unit)
sfc_thetae = np.array(mpcalc.equivalent_potential_temperature(ps_t*units.units.hectopascals,tas_t*units.units.degC,\
ta2d_t*units.units.degC))
sfc_q = np.array(sfc_q_unit)
sfc_hur = np.array(sfc_hur_unit)
#sfc_wb = np.array(wrf.wetbulb( sfc_p_3d*100, sfc_ta+273.15, sfc_q, units="degC"))
#Use getcape.f90
#cape_gb_mu1, cape_gb_mu4 = getcape_driver(sfc_p_3d, sfc_ta, sfc_dp, ps_t)
#Now get most-unstable CAPE (max CAPE in vertical, ensuring parcels used are AGL)
cape3d = wrf.cape_3d(sfc_p_3d,sfc_ta+273.15,\
sfc_q,sfc_hgt,\
terrain,ps_t,\
True,meta=False, missing=0)
cape = cape3d.data[0]
cin = cape3d.data[1]
lfc = cape3d.data[2]
lcl = cape3d.data[3]
el = cape3d.data[4]
#Mask values which are below the surface and above 350 hPa AGL
cape[(sfc_p_3d > ps_t) | (sfc_p_3d < (ps_t - 350))] = np.nan
cin[(sfc_p_3d > ps_t) | (sfc_p_3d < (ps_t - 350))] = np.nan
lfc[(sfc_p_3d > ps_t) | (sfc_p_3d < (ps_t - 350))] = np.nan
lcl[(sfc_p_3d > ps_t) | (sfc_p_3d < (ps_t - 350))] = np.nan
el[(sfc_p_3d > ps_t) | (sfc_p_3d < (ps_t - 350))] = np.nan
#Get maximum (in the vertical), and get cin, lfc, lcl for the same parcel
mu_cape_inds = np.tile(np.nanargmax(cape, axis=0),
(cape.shape[0], 1, 1))
mu_cape = np.take_along_axis(cape, mu_cape_inds, 0)[0]
mu_cin = np.take_along_axis(cin, mu_cape_inds, 0)[0]
mu_lfc = np.take_along_axis(lfc, mu_cape_inds, 0)[0]
mu_lcl = np.take_along_axis(lcl, mu_cape_inds, 0)[0]
mu_el = np.take_along_axis(el, mu_cape_inds, 0)[0]
muq = np.take_along_axis(sfc_q, mu_cape_inds, 0)[0] * 1000
#Calculate other parameters
#Thermo
thermo_start = dt.datetime.now()
lr700_500 = get_lr_p(ta_t, p_3d, hgt_t, 700, 500)
melting_hgt = get_t_hgt(sfc_ta, np.copy(sfc_hgt), 0, terrain)
melting_hgt = np.where((melting_hgt < 0) | (np.isnan(melting_hgt)), 0,
melting_hgt)
ta500 = get_var_p_lvl(np.copy(sfc_ta), sfc_p_3d, 500)
ta925 = get_var_p_lvl(np.copy(sfc_ta), sfc_p_3d, 925)
ta850 = get_var_p_lvl(np.copy(sfc_ta), sfc_p_3d, 850)
ta700 = get_var_p_lvl(np.copy(sfc_ta), sfc_p_3d, 700)
rho = mpcalc.density(
np.array(sfc_p_3d) * (units.units.hectopascal),
sfc_ta * units.units.degC, sfc_q_unit)
rho925 = np.array(get_var_p_lvl(np.array(rho), sfc_p_3d, 925))
rho850 = np.array(get_var_p_lvl(np.array(rho), sfc_p_3d, 850))
rho700 = np.array(get_var_p_lvl(np.array(rho), sfc_p_3d, 700))
#Winds
winds_start = dt.datetime.now()
s06 = get_shear_hgt(sfc_ua, sfc_va, np.copy(sfc_hgt), 0, 6000, terrain)
s0500 = get_shear_p(ua_t,
va_t,
p_3d,
"sfc",
np.array([500]),
p_3d,
uas=uas_t,
vas=vas_t)[0]
#WAP
if model in ["erai", "era5"]:
sfc_w = mpcalc.vertical_velocity( wap_t * (units.units.pascal / units.units.second),\
np.array(p_3d) * (units.units.hectopascal), \
ta_t * units.units.degC, q_unit)
w925 = np.array(get_var_p_lvl(np.array(sfc_w), p_3d, 925))
w850 = np.array(get_var_p_lvl(np.array(sfc_w), p_3d, 850))
w700 = np.array(get_var_p_lvl(np.array(sfc_w), p_3d, 700))
#Convergence
if era5_interp:
x, y = np.meshgrid(era5_lon, era5_lat)
else:
x, y = np.meshgrid(lon, lat)
dx, dy = mpcalc.lat_lon_grid_deltas(x, y)
u925 = np.array(get_var_p_lvl(np.copy(sfc_ua), sfc_p_3d, 925))
u850 = np.array(get_var_p_lvl(np.copy(sfc_ua), sfc_p_3d, 850))
u700 = np.array(get_var_p_lvl(np.copy(sfc_ua), sfc_p_3d, 700))
v925 = np.array(get_var_p_lvl(np.copy(sfc_va), sfc_p_3d, 925))
v850 = np.array(get_var_p_lvl(np.copy(sfc_va), sfc_p_3d, 850))
v700 = np.array(get_var_p_lvl(np.copy(sfc_va), sfc_p_3d, 700))
conv925 = -1e5 * np.array(
mpcalc.divergence(u925 * (units.units.meter / units.units.second),
v925 * (units.units.meter / units.units.second),
dx, dy))
conv850 = -1e5 * np.array(
mpcalc.divergence(u850 * (units.units.meter / units.units.second),
v850 * (units.units.meter / units.units.second),
dx, dy))
conv700 = -1e5 * np.array(
mpcalc.divergence(u700 * (units.units.meter / units.units.second),
v700 * (units.units.meter / units.units.second),
dx, dy))
#CS6
mucs6 = mu_cape * np.power(s06, 1.67)
#Fill output
output = fill_output(output, t, param, ps, "mu_cape", mu_cape)
output = fill_output(output, t, param, ps, "mu_cin", mu_cin)
output = fill_output(output, t, param, ps, "muq", muq)
output = fill_output(output, t, param, ps, "s06", s06)
output = fill_output(output, t, param, ps, "s0500", s0500)
output = fill_output(output, t, param, ps, "lr700_500", lr700_500)
output = fill_output(output, t, param, ps, "ta500", ta500)
output = fill_output(output, t, param, ps, "mhgt", melting_hgt)
output = fill_output(output, t, param, ps, "tp", tp_t)
if (model == "erai") | (model == "era5"):
output = fill_output(output, t, param, ps, "mod_cape", mod_cape_t)
output_data[t] = output
print("SAVING DATA...")
param_out = []
for param_name in param:
temp_data = output_data[:, :, :, np.where(param == param_name)[0][0]]
param_out.append(temp_data)
#If the mhgt variable is zero everywhere, then it is likely that data has not been read.
#In this case, all values are missing, set to zero.
for t in np.arange(param_out[0].shape[0]):
if param_out[np.where(param == "mhgt")[0][0]][t].max() == 0:
for p in np.arange(len(param_out)):
param_out[p][t] = np.nan
if issave:
if era5_interp:
save_netcdf(region, model, out_name, date_list, era5_lat, era5_lon, param, param_out, \
out_dtype = "f4", compress=True)
else:
save_netcdf(region, model, out_name, date_list, lat, lon, param, param_out, \
out_dtype = "f4", compress=True)
print(dt.datetime.now() - tot_start)
def gradient_fluxes(df): # This method is very sensitive to input data quality
"""Returns Sensible Heat Flux and Latent Heat Flux based on Steffen & DeMaria (1996) method"""
g = 9.81 # m/s**2
cp = 1005 # J/kg/K
k = 0.4 # von Karman
Lv = 2.50e6 # J/kg
fillvalue = common.fillvalue_float
ht_low, ht_high, ta_low, ta_high, wspd_low, wspd_high, rh_low, rh_high, phi_m, phi_h = ([] for _ in range(10))
# Average temp from both sensors for height1 and height2
ta1 = df.loc[:, ("ta_tc1", "ta_cs1")]
ta2 = df.loc[:, ("ta_tc2", "ta_cs2")]
df['ta1'] = ta1.mean(axis=1)
df['ta2'] = ta2.mean(axis=1)
# Assign low and high depending on height of sensors
idx = 0
while idx < len(df):
if df['wind_sensor_height_1'][idx] == fillvalue or df['wind_sensor_height_2'][idx] == fillvalue:
ht_low.append(np.nan)
ht_high.append(np.nan)
ta_low.append(df['ta1'][idx])
ta_high.append(df['ta2'][idx])
wspd_low.append(df['wspd1'][idx])
wspd_high.append(df['wspd2'][idx])
rh_low.append(df['rh1'][idx])
rh_high.append(df['rh2'][idx])
elif df['wind_sensor_height_1'][idx] > df['wind_sensor_height_2'][idx]:
ht_low.append(df['wind_sensor_height_2'][idx])
ht_high.append(df['wind_sensor_height_1'][idx])
ta_low.append(df['ta2'][idx])
ta_high.append(df['ta1'][idx])
wspd_low.append(df['wspd2'][idx])
wspd_high.append(df['wspd1'][idx])
rh_low.append(df['rh2'][idx])
rh_high.append(df['rh1'][idx])
else:
ht_low.append(df['wind_sensor_height_1'][idx])
ht_high.append(df['wind_sensor_height_2'][idx])
ta_low.append(df['ta1'][idx])
ta_high.append(df['ta2'][idx])
wspd_low.append(df['wspd1'][idx])
wspd_high.append(df['wspd2'][idx])
rh_low.append(df['rh1'][idx])
rh_high.append(df['rh2'][idx])
idx += 1
# Convert lists to arrays
ht_low = np.asarray(ht_low)
ht_high = np.asarray(ht_high)
ta_low = np.asarray(ta_low)
ta_high = np.asarray(ta_high)
wspd_low = np.asarray(wspd_low)
wspd_high = np.asarray(wspd_high)
rh_low = np.asarray(rh_low)
rh_high = np.asarray(rh_high)
ps = np.asarray(df['ps'].values)
# Potential Temperature
pot_tmp_low = potential_temperature(ps * units.pascal, ta_low * units.kelvin).magnitude
pot_tmp_high = potential_temperature(ps * units.pascal, ta_high * units.kelvin).magnitude
pot_tmp_avg = (pot_tmp_low + pot_tmp_high)/2
ta_avg = (ta_low + ta_high)/2
# Ri
du = wspd_high-wspd_low
du = np.asarray([fillvalue if i == 0 else i for i in du])
pot_tmp_avg = np.asarray([fillvalue if i == 0 else i for i in pot_tmp_avg])
ri = g*(pot_tmp_high - pot_tmp_low)*(ht_high - ht_low)/(pot_tmp_avg*du)
# Phi
for val in ri:
if val < -0.03:
phi = (1-18*val)**-0.25
phi_m.append(phi)
phi_h.append(phi/1.3)
elif -0.03 <= val < 0:
phi = (1-18*val)**-0.25
phi_m.append(phi)
phi_h.append(phi)
else:
phi = (1-5.2*val)**-1
phi_m.append(phi)
phi_h.append(phi)
phi_e = phi_h
# air density
rho = density(ps * units.pascal, ta_avg * units.kelvin, 0).magnitude # Use average temperature
# SH
ht_low = np.asarray([fillvalue if i == 0 else i for i in ht_low])
num = np.asarray([-a1 * cp * k**2 * (b1 - c1) * (d1 - e1) for a1, b1, c1, d1, e1 in
zip(rho, pot_tmp_high, pot_tmp_low, wspd_high, wspd_low)])
dnm = [a2 * b2 * np.log(c2 / d2)**2 for a2, b2, c2, d2 in
zip(phi_h, phi_m, ht_high, ht_low)]
dnm = np.asarray([fillvalue if i == 0 else i for i in dnm])
sh = num/dnm
sh = [fillvalue if abs(i) >= 100 else i for i in sh]
# Specific Humidity
mixing_ratio_low = mixing_ratio_from_relative_humidity(rh_low, ta_low * units.kelvin, ps * units.pascal)
mixing_ratio_high = mixing_ratio_from_relative_humidity(rh_high, ta_high * units.kelvin, ps * units.pascal)
q_low = specific_humidity_from_mixing_ratio(mixing_ratio_low).magnitude
q_high = specific_humidity_from_mixing_ratio(mixing_ratio_high).magnitude
q_low = q_low/100 # Divide by 100 to make it in range [0,1]
q_high = q_high/100
# LH
num = np.asarray([-a1 * Lv * k**2 * (b1 - c1) * (d1 - e1) for a1, b1, c1, d1, e1 in
zip(rho, q_high, q_low, wspd_high, wspd_low)])
dnm = [a2 * b2 * np.log(c2 / d2)**2 for a2, b2, c2, d2 in
zip(phi_e, phi_m, ht_high, ht_low)]
dnm = np.asarray([fillvalue if i == 0 else i for i in dnm])
lh = num/dnm
lh = [fillvalue if abs(i) >= 100 else i for i in lh]
return sh, lh