"""
example of running AirSeaFluxCode with
1. R/V data (data_all.csv) or
2. one day era5 hourly data (era5_r360x180.nc)
compute fluxes
output NetCDF4
and statistics in stats.txt
@author: sbiri
"""
#%% import packages
import matplotlib.pyplot as plt
import netCDF4 as nc
import numpy as np
import pandas as pd
from AirSeaFluxCode import AirSeaFluxCode
import time
from tabulate import tabulate
#%%
def reject_outliers(data, m=2):
x = np.copy(data)
x = np.where(np.abs(x-np.nanmean(x)) < m*np.nanstd(x), x, np.nan)
return x
def toy_ASFC(inF, outF, gustIn, cskinIn, tolIn, meth):
"""
Example routine of how to run AirSeaFluxCode with the test data given
and save output either as .csv or NetCDF
Parameters
----------
inF : str
input filename either data_all.csv or era5_r360x180.nc
outF : str
output filename
gustIn : float
gustiness option e.g. [1, 1.2, 800]
cskinIn : int
cool skin option input 0 or 1
tolIn : float
tolerance input option e.g. ['all', 0.01, 0.01, 1e-05, 1e-3, 0.1, 0.1]
meth : str
parametrisation method option
Returns
-------
res : float
AirSeaFluxCode output
lon : float
longitude from input netCDF file
lat : float
latitude from input netCDF file
"""
if (inF == "data_all.csv"):
#%% load data_all
inDt = pd.read_csv("data_all.csv")
date = np.asarray(inDt["Date"])
lon = np.asarray(inDt["Longitude"])
lat = np.asarray(inDt["Latitude"])
spd = np.asarray(inDt["Wind speed"])
t = np.asarray(inDt["Air temperature"])
sst = np.asarray(inDt["SST"])
rh = np.asarray(inDt["RH"])
p = np.asarray(inDt["P"])
sw = np.asarray(inDt["Rs"])
hu = np.asarray(inDt["zu"])
ht = np.asarray(inDt["zt"])
hin = np.array([hu, ht, ht])
del hu, ht, inDt
#%% run AirSeaFluxCode
res = AirSeaFluxCode(spd, t, sst, lat=lat, hum=['rh', rh], P=p,
hin=hin, Rs=sw, tol=tolIn, gust=gustIn,
cskin=cskinIn, meth=meth, L="ecmwf", n=30)
elif (inF == 'era5_r360x180.nc'):
#%% load era5_r360x180.nc
fid = nc.Dataset(inF)
lon = np.array(fid.variables["lon"])
lat = np.array(fid.variables["lat"])
T = np.array(fid.variables["t2m"])
tim = np.array(fid.variables["time"])
Td = np.array(fid.variables["d2m"])
sst = np.array(fid.variables["sst"])
sst = np.where(sst < -100, np.nan, sst)
p = np.array(fid.variables["msl"])/100 # to set hPa
lw = np.array(fid.variables["strd"])/60/60
sw = np.array(fid.variables["ssrd"])/60/60
u = np.array(fid.variables["u10"])
v = np.array(fid.variables["v10"])
lsm = np.array(fid.variables["lsm"])
icon = np.array(fid.variables["siconc"])
fid.close()
spd = np.sqrt(np.power(u, 2)+np.power(v, 2))
del u, v, fid
lsm = np.where(lsm > 0, np.nan, 1) # reverse 0 on land 1 over ocean
icon = np.where(icon < 0, np.nan, 1)
msk = lsm*icon
hin = np.array([10, 2, 2])
latIn = np.tile(lat, (len(lon), 1)).T.reshape(len(lon)*len(lat))
date = np.copy(tim)
#%% run AirSeaFluxCode
res = np.zeros((len(tim),len(lon)*len(lat), 39))
flg = np.empty((len(tim),len(lon)*len(lat)), dtype="object")
# reshape input and run code
for x in range(len(tim)):
temp = AirSeaFluxCode(spd.reshape(len(tim), len(lon)*len(lat))[x, :],
T.reshape(len(tim), len(lon)*len(lat))[x, :],
sst.reshape(len(tim), len(lon)*len(lat))[x, :],
lat=latIn,
hum=['Td', Td.reshape(len(tim), len(lon)*len(lat))[x, :]],
P=p.reshape(len(tim), len(lon)*len(lat))[x, :],
hin=hin,
Rs=sw.reshape(len(tim), len(lon)*len(lat))[x, :],
Rl=lw.reshape(len(tim), len(lon)*len(lat))[x, :],
gust=gustIn, cskin=cskinIn, tol=tolIn, qmeth='WMO',
meth=meth, n=30, L="ecmwf")
a = temp.loc[:,"tau":"rh"]
a = a.to_numpy()
flg[x, :] = temp["flag"]
res[x, :, :] = a
del a, temp
n = np.shape(res)
res = np.asarray([res[:, :, i]*msk.reshape(n[0], n[1])
for i in range(39)])
res = np.moveaxis(res, 0, -1)
if (outF[-3:] == '.nc'):
if (inF == 'era5_r360x180.nc'):
#%% save NetCDF4
fid = nc.Dataset(outF,'w', format='NETCDF4')
fid.createDimension('lon', len(lon))
fid.createDimension('lat', len(lat))
fid.createDimension('time', None)
longitude = fid.createVariable('lon', 'f4', 'lon')
latitude = fid.createVariable('lat', 'f4', 'lat')
Date = fid.createVariable('Date', 'i4', 'time')
tau = fid.createVariable('tau', 'f4', ('time','lat','lon'))
sensible = fid.createVariable('shf', 'f4', ('time','lat','lon'))
latent = fid.createVariable('lhf', 'f4', ('time','lat','lon'))
monob = fid.createVariable('MO', 'f4', ('time','lat','lon'))
cd = fid.createVariable('cd', 'f4', ('time','lat','lon'))
cdn = fid.createVariable('cdn', 'f4', ('time','lat','lon'))
ct = fid.createVariable('ct', 'f4', ('time','lat','lon'))
ctn = fid.createVariable('ctn', 'f4', ('time','lat','lon'))
cq = fid.createVariable('cq', 'f4', ('time','lat','lon'))
cqn = fid.createVariable('cqn', 'f4', ('time','lat','lon'))
tsrv = fid.createVariable('tsrv', 'f4', ('time','lat','lon'))
tsr = fid.createVariable('tsr', 'f4', ('time','lat','lon'))
qsr = fid.createVariable('qsr', 'f4', ('time','lat','lon'))
usr = fid.createVariable('usr', 'f4', ('time','lat','lon'))
psim = fid.createVariable('psim', 'f4', ('time','lat','lon'))
psit = fid.createVariable('psit', 'f4', ('time','lat','lon'))
psiq = fid.createVariable('psiq', 'f4', ('time','lat','lon'))
u10n = fid.createVariable('u10n', 'f4', ('time','lat','lon'))
t10n = fid.createVariable('t10n', 'f4', ('time','lat','lon'))
tv10n = fid.createVariable('tv10n', 'f4', ('time','lat','lon'))
q10n = fid.createVariable('q10n', 'f4', ('time','lat','lon'))
zo = fid.createVariable('zo', 'f4', ('time','lat','lon'))
zot = fid.createVariable('zot', 'f4', ('time','lat','lon'))
zoq = fid.createVariable('zoq', 'f4', ('time','lat','lon'))
urefs = fid.createVariable('uref', 'f4', ('time','lat','lon'))
trefs = fid.createVariable('tref', 'f4', ('time','lat','lon'))
qrefs = fid.createVariable('qref', 'f4', ('time','lat','lon'))
itera = fid.createVariable('iter', 'i4', ('time','lat','lon'))
dter = fid.createVariable('dter', 'f4', ('time','lat','lon'))
dqer = fid.createVariable('dqer', 'f4', ('time','lat','lon'))
dtwl = fid.createVariable('dtwl', 'f4', ('time','lat','lon'))
qair = fid.createVariable('qair', 'f4', ('time','lat','lon'))
qsea = fid.createVariable('qsea', 'f4', ('time','lat','lon'))
Rl = fid.createVariable('Rl', 'f4', ('time','lat','lon'))
Rs = fid.createVariable('Rs', 'f4', ('time','lat','lon'))
Rnl = fid.createVariable('Rnl', 'f4', ('time','lat','lon'))
ug = fid.createVariable('ug', 'f4', ('time','lat','lon'))
Rib = fid.createVariable('Rib', 'f4', ('time','lat','lon'))
rh = fid.createVariable('rh', 'f4', ('time','lat','lon'))
flag = fid.createVariable('flag', 'S1', ('time','lat','lon'))
longitude[:] = lon
latitude[:] = lat
Date[:] = tim
tau[:] = res[:, :, 0].reshape((len(tim), len(lat), len(lon)))*msk
sensible[:] = res[:, :, 1].reshape((len(tim), len(lat), len(lon)))*msk
latent[:] = res[:, :, 2].reshape((len(tim), len(lat), len(lon)))*msk
monob[:] = res[:, :, 3].reshape((len(tim), len(lat), len(lon)))*msk
cd[:] = res[:, :, 4].reshape((len(tim), len(lat), len(lon)))*msk
cdn[:] = res[:, :, 5].reshape((len(tim), len(lat), len(lon)))*msk
ct[:] = res[:, :, 6].reshape((len(tim), len(lat), len(lon)))*msk
ctn[:] = res[:, :, 7].reshape((len(tim), len(lat), len(lon)))*msk
cq[:] = res[:, :, 8].reshape((len(tim), len(lat), len(lon)))*msk
cqn[:] = res[:, :, 9].reshape((len(tim), len(lat), len(lon)))*msk
tsrv[:] = res[:, :, 10].reshape((len(tim), len(lat), len(lon)))*msk
tsr[:] = res[:, :, 11].reshape((len(tim), len(lat), len(lon)))*msk
qsr[:] = res[:, :, 12].reshape((len(tim), len(lat), len(lon)))*msk
usr[:] = res[:, :, 13].reshape((len(tim), len(lat), len(lon)))*msk
psim[:] = res[:, :, 14].reshape((len(tim), len(lat), len(lon)))*msk
psit[:] = res[:, :, 15].reshape((len(tim), len(lat), len(lon)))*msk
psiq[:] = res[:, :, 16].reshape((len(tim), len(lat), len(lon)))*msk
u10n[:] = res[:, :, 17].reshape((len(tim), len(lat), len(lon)))*msk
t10n[:] = res[:, :, 18].reshape((len(tim), len(lat), len(lon)))*msk
tv10n[:] = res[:, :, 19].reshape((len(tim), len(lat), len(lon)))*msk
q10n[:] = res[:, :, 20].reshape((len(tim), len(lat), len(lon)))*msk
zo[:] = res[:, :, 21].reshape((len(tim), len(lat), len(lon)))*msk
zot[:] = res[:, :, 22].reshape((len(tim), len(lat), len(lon)))*msk
zoq[:] = res[:, :, 23].reshape((len(tim), len(lat), len(lon)))*msk
urefs[:] = res[:, :, 24].reshape((len(tim), len(lat), len(lon)))*msk
trefs[:] = res[:, :, 25].reshape((len(tim), len(lat), len(lon)))*msk
qrefs[:] = res[:, :, 26].reshape((len(tim), len(lat), len(lon)))*msk
itera[:] = res[:, :, 27].reshape((len(tim), len(lat), len(lon)))*msk
dter[:] = res[:, :, 28].reshape((len(tim), len(lat), len(lon)))*msk
dqer[:] = res[:, :, 29].reshape((len(tim), len(lat), len(lon)))*msk
dtwl[:] = res[:, :, 30].reshape((len(tim), len(lat), len(lon)))*msk
qair[:] = res[:, :, 31].reshape((len(tim), len(lat), len(lon)))*msk
qsea[:] = res[:, :, 32].reshape((len(tim), len(lat), len(lon)))*msk
Rl[:] = res[:, :, 33].reshape((len(tim), len(lat), len(lon)))*msk
Rs[:] = res[:, :, 34].reshape((len(tim), len(lat), len(lon)))*msk
Rnl[:] = res[:, :, 35].reshape((len(tim), len(lat), len(lon)))*msk
ug[:] = res[:, :, 36].reshape((len(tim), len(lat), len(lon)))*msk
Rib[:] = res[:, :, 37].reshape((len(tim), len(lat), len(lon)))*msk
rh[:] = res[:, :, 38].reshape((len(tim), len(lat), len(lon)))*msk
flag[:] = flg.reshape((len(tim), len(lat), len(lon)))
longitude.long_name = 'Longitude'
longitude.units = 'degrees East'
latitude.long_name = 'Latitude'
latitude.units = 'degrees North'
Date.long_name = "gregorian"
Date.units = "hours since 1900-01-01 00:00:00.0"
tau.long_name = 'Wind stress'
tau.units = 'N/m^2'
sensible.long_name = 'Sensible heat fluxe'
sensible.units = 'W/m^2'
latent.long_name = 'Latent heat flux'
latent.units = 'W/m^2'
monob.long_name = 'Monin-Obukhov length'
monob.units = 'm'
cd.long_name = 'Drag coefficient'
cd.units = ''
cdn.long_name = 'Neutral Drag coefficient'
cdn.units = ''
ct.long_name = 'Heat exchange coefficient'
ct.units = ''
ctn.long_name = 'Neutral Heat exchange coefficient'
ctn.units = ''
cq.long_name = 'Moisture exchange coefficient'
cq.units = ''
cqn.long_name = 'Neutral Moisture exchange coefficient'
cqn.units = ''
tsrv.long_name = 'star virtual temperature'
tsrv.units = 'degrees Celsius'
tsr.long_name = 'star temperature'
tsr.units = 'degrees Celsius'
qsr.long_name = 'star specific humidity'
qsr.units = 'gr/kgr'
usr.long_name = 'friction velocity'
usr.units = 'm/s'
psim.long_name = 'Momentum stability function'
psit.long_name = 'Heat stability function'
u10n.long_name = '10m neutral wind speed'
u10n.units = 'm/s'
t10n.long_name = '10m neutral temperature'
t10n.units = 'degrees Celsius'
tv10n.long_name = '10m neutral virtual temperature'
tv10n.units = 'degrees Celsius'
q10n.long_name = '10m neutral specific humidity'
q10n.units = 'kgr/kgr'
zo.long_name = 'momentum roughness length'
zo.units = 'm'
zot.long_name = 'temperature roughness length'
zot.units = 'm'
zoq.long_name = 'moisture roughness length'
zoq.units = 'm'
urefs.long_name = 'wind speed at ref height'
urefs.units = 'm/s'
trefs.long_name = 'temperature at ref height'
trefs.units = 'degrees Celsius'
qrefs.long_name = 'specific humidity at ref height'
qrefs.units = 'kgr/kgr'
qair.long_name = 'specific humidity of air'
qair.units = 'kgr/kgr'
qsea.long_name = 'specific humidity over water'
qsea.units = 'kgr/kgr'
itera.long_name = 'number of iterations'
Rl.long_name = 'downward longwave radiation'
Rl.units = 'W/m^2'
Rs.long_name = 'downward shortwave radiation'
Rs.units = 'W/m^2'
Rnl.long_name = 'downward net longwave radiation'
Rnl.units = 'W/m^2'
ug.long_name = 'gust wind speed'
ug.units = 'm/s'
Rib.long_name = 'bulk Richardson number'
rh.long_name = 'relative humidity'
rh.units = '%'
flag.long_name = ('flag "n" normal, "u": u10n < 0, "q": q10n < 0,'
'"l": zol<0.01, "m": missing, "i": points that'
'have not converged')
fid.close()
#%% delete variables
del longitude, latitude, Date, tau, sensible, latent, monob, cd, cdn
del ct, ctn, cq, cqn, tsrv, tsr, qsr, usr, psim, psit, psiq, u10n, t10n
del tv10n, q10n, zo, zot, zoq, urefs, trefs, qrefs, itera, dter, dqer
del qair, qsea, Rl, Rs, Rnl, dtwl
del tim, T, Td, p, lw, sw, lsm, spd, hin, latIn, icon, msk
else:
#%% save NetCDF4
fid = nc.Dataset(outF,'w', format='NETCDF4')
fid.createDimension('lon', len(lon))
fid.createDimension('lat', len(lat))
fid.createDimension('time', None)
longitude = fid.createVariable('lon', 'f4', 'lon')
latitude = fid.createVariable('lat', 'f4', 'lat')
Date = fid.createVariable('Date', 'i4', 'time')
tau = fid.createVariable('tau', 'f4', 'time')
sensible = fid.createVariable('shf', 'f4', 'time')
latent = fid.createVariable('lhf', 'f4', 'time')
monob = fid.createVariable('MO', 'f4', 'time')
cd = fid.createVariable('cd', 'f4', 'time')
cdn = fid.createVariable('cdn', 'f4', 'time')
ct = fid.createVariable('ct', 'f4', 'time')
ctn = fid.createVariable('ctn', 'f4', 'time')
cq = fid.createVariable('cq', 'f4', 'time')
cqn = fid.createVariable('cqn', 'f4', 'time')
tsrv = fid.createVariable('tsrv', 'f4', 'time')
tsr = fid.createVariable('tsr', 'f4', 'time')
qsr = fid.createVariable('qsr', 'f4', 'time')
usr = fid.createVariable('usr', 'f4', 'time')
psim = fid.createVariable('psim', 'f4', 'time')
psit = fid.createVariable('psit', 'f4', 'time')
psiq = fid.createVariable('psiq', 'f4', 'time')
u10n = fid.createVariable('u10n', 'f4', 'time')
t10n = fid.createVariable('t10n', 'f4', 'time')
tv10n = fid.createVariable('tv10n', 'f4', 'time')
q10n = fid.createVariable('q10n', 'f4', 'time')
zo = fid.createVariable('zo', 'f4', 'time')
zot = fid.createVariable('zot', 'f4', 'time')
zoq = fid.createVariable('zoq', 'f4', 'time')
urefs = fid.createVariable('uref', 'f4', 'time')
trefs = fid.createVariable('tref', 'f4', 'time')
qrefs = fid.createVariable('qref', 'f4', 'time')
itera = fid.createVariable('iter', 'i4', 'time')
dter = fid.createVariable('dter', 'f4', 'time')
dqer = fid.createVariable('dqer', 'f4', 'time')
dtwl = fid.createVariable('dtwl', 'f4', 'time')
qair = fid.createVariable('qair', 'f4', 'time')
qsea = fid.createVariable('qsea', 'f4', 'time')
Rl = fid.createVariable('Rl', 'f4', 'time')
Rs = fid.createVariable('Rs', 'f4', 'time')
Rnl = fid.createVariable('Rnl', 'f4', 'time')
ug = fid.createVariable('ug', 'f4', 'time')
Rib = fid.createVariable('Rib', 'f4', 'time')
rh = fid.createVariable('rh', 'f4', 'time')
flag = fid.createVariable('flag', 'S1', 'time')
longitude[:] = lon
latitude[:] = lat
Date[:] = date
tau[:] = res["tau"]
sensible[:] = res["shf"]
latent[:] = res["lhf"]
monob[:] = res["L"]
cd[:] = res["cd"]
cdn[:] = res["cdn"]
ct[:] = res["ct"]
ctn[:] = res["ctn"]
cq[:] = res["cq"]
cqn[:] = res["cqn"]
tsrv[:] = res["tsrv"]
tsr[:] = res["tsr"]
qsr[:] = res["qsr"]
usr[:] = res["usr"]
psim[:] = res["psim"]
psit[:] = res["psit"]
psiq[:] = res["psiq"]
u10n[:] = res["u10n"]
t10n[:] = res["t10n"]
tv10n[:] = res["tv10n"]
q10n[:] = res["q10n"]
zo[:] = res["zo"]
zot[:] = res["zot"]
zoq[:] = res["zoq"]
urefs[:] = res["uref"]
trefs[:] = res["tref"]
qrefs[:] = res["qref"]
itera[:] = res["iteration"]
dter[:] = res["dter"]
dqer[:] = res["dqer"]
dtwl[:] = res["dtwl"]
qair[:] = res["qair"]
qsea[:] = res["qsea"]
Rl[:] = res["Rl"]
Rs[:] = res["Rs"]
Rnl[:] = res["Rnl"]
ug[:] = res["ug"]
Rib[:] = res["Rib"]
rh[:] = res["rh"]
flag[:] = res["flag"]
longitude.long_name = 'Longitude'
longitude.units = 'degrees East'
latitude.long_name = 'Latitude'
latitude.units = 'degrees North'
Date.long_name = "calendar date"
Date.units = "YYYYMMDD UTC"
tau.long_name = 'Wind stress'
tau.units = 'N/m^2'
sensible.long_name = 'Sensible heat fluxe'
sensible.units = 'W/m^2'
latent.long_name = 'Latent heat flux'
latent.units = 'W/m^2'
monob.long_name = 'Monin-Obukhov length'
monob.units = 'm'
cd.long_name = 'Drag coefficient'
cd.units = ''
cdn.long_name = 'Neutral Drag coefficient'
cdn.units = ''
ct.long_name = 'Heat exchange coefficient'
ct.units = ''
ctn.long_name = 'Neutral Heat exchange coefficient'
ctn.units = ''
cq.long_name = 'Moisture exchange coefficient'
cq.units = ''
cqn.long_name = 'Neutral Moisture exchange coefficient'
cqn.units = ''
tsrv.long_name = 'star virtual temperature'
tsrv.units = 'degrees Celsius'
tsr.long_name = 'star temperature'
tsr.units = 'degrees Celsius'
qsr.long_name = 'star specific humidity'
qsr.units = 'gr/kgr'
usr.long_name = 'friction velocity'
usr.units = 'm/s'
psim.long_name = 'Momentum stability function'
psit.long_name = 'Heat stability function'
u10n.long_name = '10m neutral wind speed'
u10n.units = 'm/s'
t10n.long_name = '10m neutral temperature'
t10n.units = 'degrees Celsius'
tv10n.long_name = '10m neutral virtual temperature'
tv10n.units = 'degrees Celsius'
q10n.long_name = '10m neutral specific humidity'
q10n.units = 'kgr/kgr'
zo.long_name = 'momentum roughness length'
zo.units = 'm'
zot.long_name = 'temperature roughness length'
zot.units = 'm'
zoq.long_name = 'moisture roughness length'
zoq.units = 'm'
urefs.long_name = 'wind speed at ref height'
urefs.units = 'm/s'
trefs.long_name = 'temperature at ref height'
trefs.units = 'degrees Celsius'
qrefs.long_name = 'specific humidity at ref height'
qrefs.units = 'kgr/kgr'
qair.long_name = 'specific humidity of air'
qair.units = 'kgr/kgr'
qsea.long_name = 'specific humidity over water'
qsea.units = 'kgr/kgr'
itera.long_name = 'number of iterations'
Rl.long_name = 'downward longwave radiation'
Rl.units = 'W/m^2'
Rs.long_name = 'downward shortwave radiation'
Rs.units = 'W/m^2'
Rnl.long_name = 'downward net longwave radiation'
Rnl.units = 'W/m^2'
ug.long_name = 'gust wind speed'
ug.units = 'm/s'
Rib.long_name = 'bulk Richardson number'
rh.long_name = 'relative humidity'
rh.units = '%'
flag.long_name = ('flag "n" normal, "u": u10n < 0, "q": q10n < 0,'
'"l": zol<0.01, "m": missing, "i": points that'
'have not converged')
fid.close()
#%% delete variables
del longitude, latitude, Date, tau, sensible, latent, monob, cd, cdn
del ct, ctn, cq, cqn, tsrv, tsr, qsr, usr, psim, psit, psiq, u10n, t10n
del tv10n, q10n, zo, zot, zoq, urefs, trefs, qrefs, itera, dter, dqer
del qair, qsea, Rl, Rs, Rnl, ug, rh, Rib
del t, date, p, sw, spd, hin, sst
else:
#%% save as .csv
res.insert(loc=0, column='date', value=date)
res.insert(loc=1, column='lon', value=lon)
res.insert(loc=2, column='lat', value=lat)
res.to_csv(outF)
return res, lon, lat
#%% run function
start_time = time.perf_counter()
#------------------------------------------------------------------------------
inF = input("Give input file name (data_all.csv or era5_r360x180.nc): \n")
meth = input("Give prefered method: \n")
while meth not in ["S80", "S88", "LP82", "YT96", "UA", "LY04", "C30", "C35",
"C40", "ecmwf","Beljaars"]:
print("method unknown")
meth = input("Give prefered method: \n")
else:
meth = meth #[meth]
ext = meth+"_"
#------------------------------------------------------------------------------
gustIn = input("Give gustiness option (to use default press enter): \n")
if (gustIn == ''):
gustIn = None
ext = ext+'gust_'
else:
gustIn = np.asarray(eval(gustIn), dtype=float)
if ((np.all(gustIn) == 0)):
ext = ext+'nogust_'
else:
ext = ext+'gust_'
#------------------------------------------------------------------------------
cskinIn = input("Give cool skin option (to use default press enter): \n")
if (cskinIn == ''):
cskinIn = None
if ((cskinIn == None) and (meth == "S80" or meth == "S88" or meth == "LP82"
or meth == "YT96" or meth == "UA" or
meth == "LY04")):
cskinIn = 0
ext = ext+'noskin_'
elif ((cskinIn == None) and (meth == "C30" or meth == "C35" or meth == "C40"
or meth == "ecmwf" or meth == "Beljaars")):
cskinIn = 1
ext = ext+'skin_'
else:
cskinIn = int(cskinIn)
if (cskinIn == 0):
ext = ext+'noskin_'
elif (cskinIn == 1):
ext = ext+'skin_'
#------------------------------------------------------------------------------
tolIn = input("Give tolerance option (to use default press enter): \n")
if (tolIn == ''):
tolIn = ['flux', 1e-3, 0.1, 0.1]
else:
tolIn = eval(tolIn)
ext = ext+'tol'+tolIn[0]
#------------------------------------------------------------------------------
outF = input("Give path and output file name: \n")
if ((outF == '') and (inF == "data_all.csv")):
outF = "out_"+inF[:-4]+"_"+ext+".csv"
elif ((outF == '') and (inF == "era5_r360x180.nc")):
outF = "out_"+inF[:-3]+"_"+ext+".nc"
elif ((outF[-4:] == '.csv') and (inF == 'era5_r360x180.nc')):
outF = outF[:-4]+".nc"
elif ((outF[-3:] != '.nc') and (outF[-4:] != '.csv')):
if (inF == "data_all.csv"):
outF = outF+".csv"
else:
outF = outF+".nc"
else:
outF = outF
#------------------------------------------------------------------------------
print("\n run_ASFC.py, started for method "+meth)
res, lon, lat = toy_ASFC(inF, outF, gustIn, cskinIn, tolIn, meth)
print("run_ASFC.py took ", np.round((time.perf_counter()-start_time)/60, 2),
"minutes to run")
#%% generate flux plots
if (inF == 'era5_r360x180.nc'):
cm = plt.cm.get_cmap('RdYlBu')
ttl = ["tau (Nm$^{-2}$)", "shf (Wm$^{-2}$)", "lhf (Wm$^{-2}$)"]
for i in range(3):
plt.figure()
plt.contourf(lon, lat,
np.nanmean(res[:, :, i], axis=0).reshape(len(lat),
len(lon)),
100, cmap=cm)
plt.colorbar()
plt.tight_layout()
plt.xlabel("Longitude")
plt.ylabel("Latitude")
plt.title(meth+', '+ttl[i])
plt.savefig('./'+ttl[i][:3]+'_'+ext+'.png', dpi=300, bbox_inches='tight')
elif (inF == "data_all.csv"):
ttl = ["tau (Nm$^{-2}$)", "shf (Wm$^{-2}$)", "lhf (Wm$^{-2}$)"]
for i in range(3):
plt.figure()
plt.plot(res[ttl[i][:3]],'.c', markersize=1)
plt.title(meth)
plt.xlabel("points")
plt.ylabel(ttl[i])
plt.savefig('./'+ttl[i][:3]+'_'+ext+'.png', dpi=300, bbox_inches='tight')
#%% generate txt file with statistic
if ((cskinIn == None) and (meth == "S80" or meth == "S88" or meth == "LP82"
or meth == "YT96" or meth == "UA" or
meth == "LY04")):
cskinIn = 0
elif ((cskinIn == None) and (meth == "C30" or meth == "C35" or meth == "C40"
or meth == "ecmwf" or meth == "Beljaars")):
cskinIn = 1
if (np.all(gustIn == None) and (meth == "C30" or meth == "C35" or meth == "C40")):
gustIn = [1, 1.2, 600]
elif (np.all(gustIn == None) and (meth == "UA" or meth == "ecmwf")):
gustIn = [1, 1, 1000]
elif np.all(gustIn == None):
gustIn = [1, 1.2, 800]
elif ((np.size(gustIn) < 3) and (gustIn == 0)):
gust = [0, 0, 0]
if (tolIn == None):
tolIn = ['flux', 0.01, 1, 1]
print("Input summary", file=open('./stats.txt', 'a'))
print('input file name: {}, \n method: {}, \n gustiness: {}, \n cskin: {},'
' \n tolerance: {}'.format(inF, meth, gustIn, cskinIn, tolIn),
file=open('./stats.txt', 'a'))
ttl = np.asarray(["tau ", "shf ", "lhf ", "L ", "cd ", "cdn ",
"ct ", "ctn ", "cq ", "cqn ", "tsrv ", "tsr ",
"qsr ", "usr ", "psim ", "psit ", "psiq ", "u10n ",
"t10n ", "tv10n", "q10n ", "zo ", "zot ", "zoq ",
"urefs", "trefs", "qrefs", "itera", "dter ", "dqer ",
"dtwl ", "qair ", "qsea ", "Rl ", "Rs ", "Rnl ",
"ug ", "Rib ", "rh "])
header = ["var", "mean", "median", "min", "max", "5%", "95%"]
n = np.shape(res)
stats = np.copy(ttl)
if (inF == 'era5_r360x180.nc'):
stats = np.c_[stats, np.nanmean(res.reshape(n[0]*n[1], n[2]), axis=0)]
stats = np.c_[stats, np.nanmedian(res.reshape(n[0]*n[1], n[2]), axis=0)]
stats = np.c_[stats, np.nanmin(res.reshape(n[0]*n[1], n[2]), axis=0)]
stats = np.c_[stats, np.nanmax(res.reshape(n[0]*n[1], n[2]), axis=0)]
stats = np.c_[stats, np.nanpercentile(res.reshape(n[0]*n[1], n[2]), 5,
axis=0)]
stats = np.c_[stats, np.nanpercentile(res.reshape(n[0]*n[1], n[2]), 95,
axis=0)]
print(tabulate(stats, headers=header, tablefmt="github", numalign="left",
floatfmt=("s", "2.2e", "2.2e", "2.2e", "2.2e", "2.2e",
"2.2e")), file=open('./stats.txt', 'a'))
print('-'*79+'\n', file=open('./stats.txt', 'a'))
elif (inF == "data_all.csv"):
a = res.loc[:,"tau":"rh"].to_numpy(dtype="float64").T
stats = np.c_[stats, np.nanmean(a, axis=1)]
stats = np.c_[stats, np.nanmedian(a, axis=1)]
stats = np.c_[stats, np.nanmin(a, axis=1)]
stats = np.c_[stats, np.nanmax(a, axis=1)]
stats = np.c_[stats, np.nanpercentile(a, 5, axis=1)]
stats = np.c_[stats, np.nanpercentile(a, 95, axis=1)]
print(tabulate(stats, headers=header, tablefmt="github", numalign="left",
floatfmt=("s", "2.2e", "2.2e", "2.2e", "2.2e", "2.2e",
"2.2e")), file=open('./stats.txt', 'a'))
print('-'*79+'\n', file=open('./stats.txt', 'a'))
del a
print('input file name: {}, \n method: {}, \n gustiness: {}, \n cskin: {},'
' \n tolerance: {}, \n output is written in: {}'.format(inF, meth,
gustIn, cskinIn,
tolIn, outF),
file=open('./readme.txt', 'w'))