import warnings
import numpy as np
import pandas as pd
import logging
from hum_subs import (get_hum, gamma)
from util_subs import *
from flux_subs import *
from cs_wl_subs import *
class S88:
def _wind_iterate(self, ind):
if self.gust[0] in range(1, 6):
self.wind[ind] = np.sqrt(np.power(np.copy(self.spd[ind]), 2) +
np.power(get_gust(
self.gust[1], self.gust[2],
self.gust[3], self.theta[ind],
self.usr[ind], self.tsrv[ind],
self.grav[ind]), 2))
if self.gust[0] in [3, 4]:
# self.GustFact[ind] = 1
# option to not remove GustFact
self.u10n[ind] = self.wind[ind]-self.usr[ind]/kappa*(
np.log(self.h_in[0, ind]/self.ref10)-self.psim[ind])
else:
self.GustFact = np.sqrt(self.wind/self.spd)
self.u10n[ind] = self.spd[ind]-self.usr[ind]/kappa / \
self.GustFact[ind]*(np.log(self.h_in[0, ind]/self.ref10) -
self.psim[ind])
else:
# initalisation of wind
self.wind[ind] = np.copy(self.spd[ind])
self.u10n[ind] = self.wind[ind]-self.usr[ind]/kappa*(
np.log(self.h_in[0, ind]/self.ref10)-self.psim[ind])
def get_heights(self, hin, hout=10):
self.hout = hout
self.hin = hin
self.h_in = get_heights(hin, len(self.spd))
self.h_out = get_heights(self.hout, 1)
def get_specHumidity(self, qmeth="Buck2"):
self.qair, self.qsea = get_hum(self.hum, self.T, self.SST, self.P,
qmeth)
if (np.all(np.isnan(self.qsea)) or np.all(np.isnan(self.qair))):
raise ValueError("qsea and qair cannot be nan")
self.dq_in = self.qair-self.qsea
self.dq_full = self.qair-self.qsea
# Set lapse rate and Potential Temperature (now we have humdity)
self.cp = 1004.67*(1+0.00084*self.qsea)
self.tlapse = gamma("dry", self.SST, self.T, self.qair/1000, self.cp)
self.theta = np.copy(self.T)+self.tlapse*self.h_in[1]
self.dt_in = self.theta-self.SST
self.dt_full = self.theta-self.SST
def _fix_coolskin_warmlayer(self, wl, cskin, skin, Rl, Rs):
skin = self.skin if skin is None else skin
assert wl in [0, 1], "wl not valid"
assert cskin in [0, 1], "cskin not valid"
assert skin in ["C35", "ecmwf", "Beljaars"], "Skin value not valid"
if ((cskin == 1 or wl == 1) and
(np.all(Rl == None) or np.all(np.isnan(Rl))) and
((np.all(Rs == None) or np.all(np.isnan(Rs))))):
print("Cool skin/warm layer is switched ON; "
"Radiation input should not be empty")
raise
self.wl = wl
self.cskin = cskin
self.skin = skin
self.Rs = np.full(self.spd.shape, np.nan) if Rs is None else Rs
self.Rl = np.full(self.spd.shape, np.nan) if Rl is None else Rl
def set_coolskin_warmlayer(self, wl=0, cskin=0, skin=None, Rl=None,
Rs=None):
wl = 0 if wl is None else wl
if hasattr(self, "skin") == False:
self.skin = "C35"
self._fix_coolskin_warmlayer(wl, cskin, skin, Rl, Rs)
def _update_coolskin_warmlayer(self, ind):
if self.cskin == 1:
self.dter[ind], self.tkt[ind] = cs(np.copy(
self.SST[ind]), np.copy(self.tkt[ind]), self.rho[ind],
self.Rs[ind], self.Rnl[ind], self.cp[ind], self.lv[ind],
self.usr[ind], self.tsr[ind], self.qsr[ind], self.grav[ind],
self.skin)
self.dqer[ind] = get_dqer(self.dter[ind], self.SST[ind],
self.qsea[ind], self.lv[ind])
self.skt[ind] = np.copy(self.SST[ind])+self.dter[ind]
self.skq[ind] = np.copy(self.qsea[ind])+self.dqer[ind]
if self.wl == 1:
self.dtwl[ind] = wl_ecmwf(
self.rho[ind], self.Rs[ind], self.Rnl[ind], self.cp[ind],
self.lv[ind], self.usr[ind], self.tsr[ind], self.qsr[ind],
np.copy(self.SST[ind]), np.copy(self.skt[ind]),
np.copy(self.dter[ind]), self.grav[ind])
self.skt[ind] = (np.copy(self.SST[ind])+self.dter[ind] +
self.dtwl[ind])
self.dqer[ind] = get_dqer(self.dter[ind], self.skt[ind],
self.qsea[ind], self.lv[ind])
self.skq[ind] = np.copy(self.qsea[ind])+self.dqer[ind]
else:
self.dter[ind] = np.zeros(self.SST[ind].shape)
self.dqer[ind] = np.zeros(self.SST[ind].shape)
self.dtwl[ind] = np.zeros(self.SST[ind].shape)
self.tkt[ind] = np.zeros(self.SST[ind].shape)
def _first_guess(self):
# reference height
self.ref10 = 10
# first guesses
self.t10n, self.q10n = np.copy(self.theta), np.copy(self.qair)
self.rho = self.P*100/(287.1*self.t10n*(1+0.6077*self.q10n))
self.lv = (2.501-0.00237*(self.SST-CtoK))*1e6 # J/kg
# Zeng et al. 1998
self.tv = self.theta*(1+0.6077*self.qair) # virtual potential T
self.dtv = self.dt_in*(1+0.6077*self.qair)+0.6077*self.theta*self.dq_in
# Set the wind array
self.wind = np.sqrt(np.power(np.copy(self.spd), 2)+0.25)
self.GustFact = self.wind*0+1
# Rb eq. 11 Grachev & Fairall 1997, use air temp height
# use self.tv?? adjust wind to T-height?
Rb = self.grav*self.h_in[1]*self.dtv/(self.T*np.power(self.wind, 2))
# eq. 12 Grachev & Fairall 1997 # DO.THIS
self.monob = self.h_in[1]/12.0/Rb
# ------------
dummy_array = lambda val : np.full(self.T.shape, val)*self.msk
# def dummy_array(val): return np.full(self.T.shape, val)*self.msk
if self.cskin + self.wl > 0:
self.dter, self.tkt, self.dtwl = [
dummy_array(x) for x in (-0.3, 0.001, 0.3)]
self.dqer = get_dqer(self.dter, self.SST, self.qsea,
self.lv)
self.Rnl = 0.97*(self.Rl-5.67e-8*np.power(
self.SST-0.3*self.cskin, 4))
self.Qs = 0.945*self.Rs
else:
self.dter, self.dqer, self.dtwl = [
dummy_array(x) for x in (0.0, 0.0, 0.0)]
self.Rnl, self.Qs, self.tkt = [
np.empty(self.arr_shp)*self.msk for _ in range(3)]
self.skt = np.copy(self.SST)
self.skq = np.copy(self.qsea)
self.u10n = np.copy(self.wind)
self.usr = 0.035*self.u10n
self.cd10n, self.zo = cdn_calc(
self.u10n, self.usr, self.theta, self.grav, self.meth)
self.psim = psim_calc(self.h_in[0]/self.monob, self.meth)
self.cd = cd_calc(self.cd10n, self.h_in[0], self.ref10, self.psim)
self.usr =np.sqrt(self.cd*np.power(self.wind, 2))
self.zot, self.zoq, self.tsr, self.qsr = [
np.empty(self.arr_shp)*self.msk for _ in range(4)]
self.ct10n, self.cq10n, self.ct, self.cq = [
np.empty(self.arr_shp)*self.msk for _ in range(4)]
self.tv10n = self.zot
def iterate(self, maxiter=10, tol=None):
if maxiter < 5:
warnings.warn("Iteration number <5 - resetting to 5.")
maxiter = 5
# Decide which variables to use in tolerances based on tolerance
# specification
tol = ['all', 0.01, 0.01, 1e-05, 1e-3,
0.1, 0.1] if tol is None else tol
assert tol[0] in ['flux', 'ref', 'all'], "unknown tolerance input"
old_vars = {"flux": ["tau", "sensible", "latent"],
"ref": ["u10n", "t10n", "q10n"]}
old_vars["all"] = old_vars["ref"] + old_vars["flux"]
old_vars = old_vars[tol[0]]
new_vars = {"flux": ["tau", "sensible", "latent"],
"ref": ["u10n", "t10n", "q10n"]}
new_vars["all"] = new_vars["ref"] + new_vars["flux"]
new_vars = new_vars[tol[0]]
# extract tolerance values by deleting flag from tol
tvals = np.delete(np.copy(tol), 0)
tol_vals = list([float(tt) for tt in tvals])
ind = np.where(self.spd > 0)
it = 0
# Setup empty arrays
self.tsrv, self.psim, self.psit, self.psiq = [
np.zeros(self.arr_shp)*self.msk for _ in range(4)]
# extreme values for first comparison
dummy_array = lambda val : np.full(self.T.shape, val)*self.msk
# you can use def instead of lambda
# def dummy_array(val): return np.full(self.arr_shp, val)*self.msk
self.itera, self.tau, self.sensible, self.latent = [
dummy_array(x) for x in (-1, 1e+99, 1e+99, 1e+99)]
# Generate the first guess values
self._first_guess()
# iteration loop
ii = True
while ii & (it < maxiter):
it += 1
# Set the old variables (for comparison against "new")
old = np.array([np.copy(getattr(self, i)) for i in old_vars])
# Calculate cdn
self.cd10n[ind], self.zo[ind] = cdn_calc(
self.u10n[ind], self.usr[ind], self.theta[ind], self.grav[ind],
self.meth)
if np.all(np.isnan(self.cd10n)):
logging.info('break %s at iteration %s cd10n<0', meth, it)
break
self.psim[ind] = psim_calc(
self.h_in[0, ind]/self.monob[ind], self.meth)
self.cd[ind] = cd_calc(
self.cd10n[ind], self.h_in[0, ind], self.ref10, self.psim[ind])
# Update the wind values
self._wind_iterate(ind)
# temperature
self.ct10n[ind], self.zot[ind] = ctqn_calc(
"ct", self.h_in[1, ind]/self.monob[ind], self.cd10n[ind],
self.usr[ind], self.zo[ind], self.theta[ind], self.meth)
self.psit[ind] = psit_calc(
self.h_in[1, ind]/self.monob[ind], self.meth)
self.ct[ind] = ctq_calc(
self.cd10n[ind], self.cd[ind], self.ct10n[ind],
self.h_in[1, ind], self.ref10, self.psit[ind])
# humidity
self.cq10n[ind], self.zoq[ind] = ctqn_calc(
"cq", self.h_in[2, ind]/self.monob[ind], self.cd10n[ind],
self.usr[ind], self.zo[ind], self.theta[ind], self.meth)
self.psiq[ind] = psit_calc(
self.h_in[2, ind]/self.monob[ind], self.meth)
self.cq[ind] = ctq_calc(
self.cd10n[ind], self.cd[ind], self.cq10n[ind],
self.h_in[2, ind], self.ref10, self.psiq[ind])
# Some parameterizations set a minimum on parameters
try:
self._minimum_params()
except AttributeError:
pass
self.dt_full[ind] = self.dt_in[ind] - \
self.dter[ind]*self.cskin - self.dtwl[ind]*self.wl
self.dq_full[ind] = self.dq_in[ind] - self.dqer[ind]*self.cskin
self.usr[ind], self.tsr[ind], self.qsr[ind] = get_strs(
self.h_in[:, ind], self.monob[ind], self.wind[ind],
self.zo[ind], self.zot[ind], self.zoq[ind], self.dt_full[ind],
self.dq_full[ind], self.cd[ind], self.ct[ind], self.cq[ind],
self.meth)
# Update CS/WL parameters
self._update_coolskin_warmlayer(ind)
# Logging output
log_vars = {"dter": 2, "dqer": 7, "tkt": 2,
"Rnl": 2, "usr": 3, "tsr": 4, "qsr": 7}
log_vars = [np.round(np.nanmedian(getattr(self, V)), R)
for V, R in log_vars.items()]
log_vars.insert(0, self.meth)
logging.info(
'method {} | dter = {} | dqer = {} | tkt = {} | Rnl = {} |'
' usr = {} | tsr = {} | qsr = {}'.format(*log_vars))
if self.cskin + self.wl > 0:
self.Rnl[ind] = 0.97*(self.Rl[ind]-5.67e-8 *
np.power(self.SST[ind] +
self.dter[ind]*self.cskin, 4))
# not sure how to handle lapse/potemp
# well-mixed in potential temperature ...
self.t10n[ind] = self.theta[ind]-self.tlapse[ind]*self.ref10 - \
self.tsr[ind]/kappa * \
(np.log(self.h_in[1, ind]/self.ref10)-self.psit[ind])
self.q10n[ind] = self.qair[ind]-self.qsr[ind]/kappa * \
(np.log(self.h_in[2, ind]/self.ref10)-self.psiq[ind])
# update stability info
self.tsrv[ind] = get_tsrv(
self.tsr[ind], self.qsr[ind], self.theta[ind], self.qair[ind])
self.Rb[ind] = get_Rb(
self.grav[ind], self.usr[ind], self.h_in[0, ind],
self.h_in[1, ind], self.tv[ind], self.dtv[ind], self.wind[ind],
self.monob[ind], self.meth)
if self.L == "tsrv":
self.monob[ind] = get_Ltsrv(
self.tsrv[ind], self.grav[ind], self.tv[ind],
self.usr[ind])
else:
self.monob[ind] = get_LRb(
self.Rb[ind], self.h_in[1, ind], self.monob[ind],
self.zo[ind], self.zot[ind], self.meth)
# Update the wind values
self._wind_iterate(ind)
# make sure you allow small negative values convergence
if it == 1:
self.u10n = np.where(self.u10n < 0, 0.5, self.u10n)
self.itera[ind] = np.full(1, it)
self.tau = self.rho*np.power(self.usr, 2)
self.sensible = self.rho*self.cp*self.usr*self.tsr
self.latent = self.rho*self.lv*self.usr*self.qsr
# Set the new variables (for comparison against "old")
new = np.array([np.copy(getattr(self, i)) for i in new_vars])
if it > 2: # force at least three iterations
d = np.abs(new-old) # change over this iteration
for ii in range(0, len(tol_vals)):
d[ii, ] = d[ii, ]/tol_vals[ii] # ratio to tolerance
# identifies non-convergence
ind = np.where(d.max(axis=0) >= 1)
self.ind = np.copy(ind)
ii = False if (ind[0].size == 0) else True
# End of iteration loop
self.itera[ind] = -1
self.itera = np.where(self.itera > maxiter, -1, self.itera)
logging.info('method %s | # of iterations:%s', self.meth, it)
logging.info('method %s | # of points that did not converge :%s \n',
self.meth, self.ind[0].size)
def _get_humidity(self):
"""Calculate RH used for flagging purposes & output."""
if self.hum[0] in ('rh', 'no'):
self.rh = self.hum[1]
elif self.hum[0] == 'Td':
Td = self.hum[1] # dew point temperature (K)
Td = np.where(Td < 200, np.copy(Td)+CtoK, np.copy(Td))
T = np.where(self.T < 200, np.copy(self.T)+CtoK, np.copy(self.T))
esd = 611.21*np.exp(17.502*((Td-CtoK)/(Td-32.19)))
es = 611.21*np.exp(17.502*((T-CtoK)/(T-32.19)))
self.rh = 100*esd/es
elif self.hum[0] == "q":
es = 611.21*np.exp(17.502*((self.T-CtoK)/(self.T-32.19)))
e = self.qair*self.P/(0.378*self.qair+0.622)
self.rh = 100*e/es
def _flag(self, out=0):
miss = np.copy(self.msk) # define missing input points
if self.cskin == 1:
miss = np.where(np.isnan(self.msk+self.P+self.Rs+self.Rl),
np.nan, 1)
else:
miss = np.where(np.isnan(self.msk+self.P), np.nan, 1)
flag = set_flag(miss, self.rh, self.u10n, self.q10n, self.t10n,
self.Rb, self.hin, self.monob, self.itera, out=out)
self.flag = flag
def get_output(self, out_var=None, out=0):
assert out in [0, 1], "out must be either 0 or 1"
self._get_humidity() # Get the Relative humidity
self._flag(out=out) # Get flags
self.GustFact = apply_GF(self.gust, self.spd, self.wind, "TSF")
self.tau = self.rho*np.power(self.usr, 2)/self.GustFact[0]
self.sensible = self.rho*self.cp*(self.usr/self.GustFact[1])*self.tsr
self.latent = self.rho*self.lv*(self.usr/self.GustFact[2])*self.qsr
self.GustFact = apply_GF(self.gust, self.spd, self.wind, "u")
if self.gust[0] in [3, 4]:
self.u10n = self.wind-self.usr/kappa*(
np.log(self.h_in[0]/self.ref10)-self.psim)
self.uref = self.wind-self.usr/kappa*(
np.log(self.h_in[0]/self.h_out[0])-self.psim +
psim_calc(self.h_out[0]/self.monob, self.meth))
else:
self.u10n = self.spd-self.usr/kappa/self.GustFact*(
np.log(self.h_in[0]/self.ref10)-self.psim)
self.uref = self.spd-self.usr/kappa/self.GustFact * \
(np.log(self.h_in[0]/self.h_out[0])-self.psim +
psim_calc(self.h_out[0]/self.monob, self.meth))
self.usrGF = self.usr/self.GustFact
# include lapse rate adjustment as theta is well-mixed
self.tref = self.theta-self.tlapse*self.h_out[1]-self.tsr/kappa * \
(np.log(self.h_in[1]/self.h_out[1])-self.psit +
psit_calc(self.h_out[1]/self.monob, self.meth))
self.qref = self.qair-self.qsr/kappa * \
(np.log(self.h_in[2]/self.h_out[2])-self.psiq +
psit_calc(self.h_out[2]/self.monob, self.meth))
self.psim_ref = psim_calc(self.h_out[0]/self.monob, self.meth)
self.psit_ref = psit_calc(self.h_out[1]/self.monob, self.meth)
self.psiq_ref = psit_calc(self.h_out[2]/self.monob, self.meth)
if self.wl == 0:
self.dtwl = np.zeros(self.T.shape)*self.msk
# reset to zero if not used
# Do not calculate lhf if a measure of humidity is not input
# This gets filled into a pd dataframe and so no need to specify
# dimension of array
if self.hum[0] == 'no':
self.latent, self.qsr, self.q10n = np.empty(3)
self.qref, self.qair, self.rh = np.empty(3)
# Set the final wind speed values
# this seems to be gust (was wind_speed)
self.ug = np.sqrt(np.power(self.wind, 2)-np.power(self.spd, 2))
# Get class specific flags (will only work if self.u_hi and self.u_lo
# have been set in the class)
try:
self._class_flag()
except AttributeError:
pass
# Combine all output variables into a pandas array
res_vars = get_outvars(out_var, self.cskin, self.gust)
res = np.zeros((len(res_vars), len(self.spd)))
for i, value in enumerate(res_vars):
res[i][:] = getattr(self, value)
if out == 0:
res[:, self.ind] = np.nan
# set missing values where data have non acceptable values
if self.hum[0] != 'no':
res = np.asarray([
np.where(self.q10n < 0, np.nan, res[i][:])
for i in range(len(res_vars))])
# len(res_vars)-1 instead of len(res_vars) in order to keep
# itera= -1 for no convergence
res = np.asarray([
np.where(self.u10n < 0, np.nan, res[i][:])
for i in range(len(res_vars))])
res = np.asarray([
np.where(((self.t10n < 173) | (self.t10n > 373)), np.nan,
res[i][:])
for i in range(len(res_vars))])
else:
warnings.warn("Warning: the output will contain values for points"
" that have not converged and negative values "
"(if any) for u10n/q10n")
resAll = pd.DataFrame(data=res.T, index=range(self.nlen),
columns=res_vars)
if "itera" in res_vars:
resAll["itera"] = self.itera # restore itera
resAll["flag"] = self.flag
return resAll
def add_variables(self, spd, T, SST, lat=None, hum=None, P=None, L=None):
# Add the mandatory variables
assert type(spd) == type(T) == type(
SST) == np.ndarray, "input type of spd, T and SST should be"
" numpy.ndarray"
self.L = "tsrv" if L is None else L
self.arr_shp = spd.shape
self.nlen = len(spd)
self.spd = spd
self.T = np.where(T < 200, np.copy(T)+CtoK, np.copy(T))
self.hum = ['no', np.full(SST.shape, 80)] if hum is None else hum
self.SST = np.where(SST < 200, np.copy(SST)+CtoK, np.copy(SST))
self.lat = np.full(self.arr_shp, 45) if lat is None else lat
self.grav = gc(self.lat)
self.P = np.full(self.nlen, 1013) if P is None else P
# mask to preserve missing values when initialising variables
self.msk = np.empty(SST.shape)
self.msk = np.where(np.isnan(spd+T+SST), np.nan, 1)
self.Rb = np.empty(SST.shape)*self.msk
def add_gust(self, gust=None):
if np.all(gust is None):
try:
gust = self.default_gust
except AttributeError:
gust = [0, 0, 0, 0] # gustiness OFF
# gust = [1, 1.2, 800]
elif ((np.size(gust) < 3) and (gust == 0)):
gust = [0, 0, 0, 0]
assert np.size(gust) == 4, "gust input must be a 4x1 array"
assert gust[0] in range(6), "gust at position 0 must be 0 to 5"
self.gust = gust
def _class_flag(self):
"""A flag specific to this class - only used for certain classes where
u_lo and u_hi are defined"""
self.flag = np.where(((self.u10n < self.u_lo[0]) |
(self.u10n > self.u_hi[0])) &
(self.flag == "n"), "o",
np.where(((self.u10n < self.u_lo[1]) |
(self.u10n > self.u_hi[1])) &
((self.flag != "n") & (np.char.find(
self.flag.astype(str), 'u') == -1) &
(np.char.find(
self.flag.astype(str), 'q') == -1)),
self.flag+[","]+["o"], self.flag))
def __init__(self):
self.meth = "S88"
class S80(S88):
def __init__(self):
self.meth = "S80"
self.u_lo = [6, 6]
self.u_hi = [22, 22]
class YT96(S88):
def __init__(self):
self.meth = "YT96"
# no limits to u range as we use eq. 21 for cdn
# self.u_lo = [0, 3]
# self.u_hi = [26, 26]
class LP82(S88):
def __init__(self):
self.meth = "LP82"
self.u_lo = [3, 3]
self.u_hi = [25, 25]
class NCAR(S88):
def _minimum_params(self):
self.cd = np.maximum(np.copy(self.cd), 1e-4)
self.ct = np.maximum(np.copy(self.ct), 1e-4)
self.cq = np.maximum(np.copy(self.cq), 1e-4)
# self.zo = np.minimum(np.copy(self.zo), 0.0025)
def __init__(self):
self.meth = "NCAR"
self.u_lo = [0.5, 0.5]
self.u_hi = [999, 999]
class UA(S88):
def __init__(self):
self.meth = "UA"
self.default_gust = [1, 1.2, 600, 0.01]
self.u_lo = [-999, -999]
self.u_hi = [18, 18]
class C30(S88):
# def set_coolskin_warmlayer(self, wl=0, cskin=1, skin="C35", Rl=None, Rs=None):
# self._fix_coolskin_warmlayer(wl, cskin, skin, Rl, Rs)
def __init__(self):
self.meth = "C30"
self.default_gust = [1, 1.2, 600, 0.01]
self.skin = "C35"
class C35(C30):
def __init__(self):
self.meth = "C35"
self.default_gust = [1, 1.2, 600, 0.01]
self.skin = "C35"
class ecmwf(C30):
# def set_coolskin_warmlayer(self, wl=0, cskin=1, skin="ecmwf", Rl=None,
# Rs=None):
# self._fix_coolskin_warmlayer(wl, cskin, skin, Rl, Rs)
# def _minimum_params(self):
# self.cdn = np.maximum(np.copy(self.cdn), 0.1e-3)
# self.ctn = np.maximum(np.copy(self.ctn), 0.1e-3)
# self.cqn = np.maximum(np.copy(self.cqn), 0.1e-3)
# self.wind = np.maximum(np.copy(self.wind), 0.2)
def __init__(self):
self.meth = "ecmwf"
self.default_gust = [1, 1.2, 600, 0.01]
self.skin = "ecmwf"
class Beljaars(C30):
# def set_coolskin_warmlayer(self, wl=0, cskin=1, skin="Beljaars", Rl=None,
# Rs=None):
# self._fix_coolskin_warmlayer(wl, cskin, skin, Rl, Rs)
def __init__(self):
self.meth = "Beljaars"
self.default_gust = [1, 1.2, 600, 0.01]
self.skin = "ecmwf"
# self.skin = "Beljaars"
def AirSeaFluxCode(spd, T, SST, meth, lat=None, hum=None, P=None, hin=18,
hout=10, Rl=None, Rs=None, cskin=0, skin=None, wl=0,
gust=None, qmeth="Buck2", tol=None, maxiter=10, out=0,
out_var=None, L=None):
"""
Calculate turbulent surface fluxes using different parameterizations.
Calculate height adjusted values for spd, T, q
Parameters
----------
spd : float
relative wind speed in m/s (is assumed as magnitude difference
between wind and surface current vectors)
T : float
air temperature in K (will convert if < 200)
SST : float
sea surface temperature in K (will convert if < 200)
meth : str
"S80", "S88", "LP82", "YT96", "UA", "NCAR", "C30", "C35",
"ecmwf", "Beljaars"
lat : float
latitude (deg), default 45deg
hum : float
humidity input switch 2x1 [x, values] default is relative humidity
x='rh' : relative humidity in %
x='q' : specific humidity (g/kg)
x='Td' : dew point temperature (K)
P : float
air pressure (hPa), default 1013hPa
hin : float
sensor heights in m (array 3x1 or 3xn), default 18m
hout : float
output height, default is 10m
Rl : float
downward longwave radiation (W/m^2)
Rs : float
downward shortwave radiation (W/m^2)
cskin : int
0 switch cool skin adjustment off, else 1
default is 0
skin : str
cool skin method option "C35", "ecmwf" or "Beljaars"
wl : int
warm layer correction default is 0, to switch on set to 1
gust : int
4x1 [x, beta, zi, ustb] x=0 gustiness is OFF, x=1-5 gustiness is ON
and use gustiness factor: 1. Fairall et al. 2003, 2. GF is removed
from TSFs u10n, uref, 3. GF=1, 4. following Zeng et al. 1998 or
Brodeau et al. 2006, 5. following C35 matlab code;
beta gustiness parameter, default is 1.2,
zi PBL height (m) default is 600,
min is the value for gust speed in stable conditions,
default is 0.01ms^{-1}
qmeth : str
is the saturation evaporation method to use amongst
"HylandWexler","Hardy","Preining","Wexler","GoffGratch","WMO",
"MagnusTetens","Buck","Buck2","WMO2018","Sonntag","Bolton",
"IAPWS","MurphyKoop"]
default is Buck2
tol : float
4x1 or 7x1 [option, lim1-3 or lim1-6]
option : 'flux' to set tolerance limits for fluxes only lim1-3
option : 'ref' to set tolerance limits for height adjustment lim-1-3
option : 'all' to set tolerance limits for both fluxes and height
adjustment lim1-6
default is tol=['all', 0.01, 0.01, 1e-05, 1e-3, 0.1, 0.1]
maxiter : int
number of iterations (default = 10)
out : int
set 0 to set points that have not converged, negative values of
u10n, q10n or T10n out of limits to missing (default)
set 1 to keep points
out_var : str
optional. user can define pandas array of variables to be output.
the default full pandas array is :
out_var = ("tau", "sensible", "latent", "monob", "cd", "cd10n",
"ct", "ct10n", "cq", "cq10n", "tsrv", "tsr", "qsr",
"usr", "psim", "psit", "psiq", "psim_ref", "psit_ref",
"psiq_ref", "u10n", "t10n", "q10n", "zo", "zot", "zoq",
"uref", "tref", "qref", "dter", "dqer", "dtwl", "tkt",
"qair", "qsea", "Rl", "Rs", "Rnl", "ug", "usrGF",
"GustFact", "Rb", "rh", "rho", "cp", "lv", "theta",
"itera")
the "limited" pandas array is:
out_var = ("tau", "sensible", "latent", "uref", "tref", "qref")
the user can define a custom pandas array of variables to output.
L : str
Monin-Obukhov length definition options
"tsrv" : default
"Rb" : following ecmwf (IFS Documentation cy46r1)
Returns
-------
res : array that contains
1. momentum flux (N/m^2)
2. sensible heat (W/m^2)
3. latent heat (W/m^2)
4. Monin-Obhukov length (m)
5. drag coefficient (cd)
6. neutral drag coefficient (cd10n)
7. heat exchange coefficient (ct)
8. neutral heat exchange coefficient (ct10n)
9. moisture exhange coefficient (cq)
10. neutral moisture exchange coefficient (cq10n)
11. star virtual temperatcure (tsrv)
12. star temperature (tsr)
13. star specific humidity (qsr)
14. star wind speed (usr)
15. momentum stability function (psim)
16. heat stability function (psit)
17. moisture stability function (psiq)
18. momentum stability function at hout (psim_ref)
19. heat stability function at hout (psit_ref)
20. moisture stability function at hout (psiq_ref)
21. 10m neutral wind speed (u10n)
22. 10m neutral temperature (t10n)
23. 10m neutral specific humidity (q10n)
24. surface roughness length (zo)
25. heat roughness length (zot)
26. moisture roughness length (zoq)
27. wind speed at reference height (uref)
28. temperature at reference height (tref)
29. specific humidity at reference height (qref)
30. cool-skin temperature depression (dter)
31. cool-skin humidity depression (dqer)
32. warm layer correction (dtwl)
33. thickness of the viscous layer (delta)
34. specific humidity of air (qair)
35. specific humidity at sea surface (qsea)
36. downward longwave radiation (Rl)
37. downward shortwave radiation (Rs)
38. downward net longwave radiation (Rnl)
39. gust wind speed (ug)
40. star wind speed/GustFact (usrGF)
41. Gustiness Factor (GustFact)
42. Bulk Richardson number (Rb)
43. relative humidity (rh)
44. air density (rho)
45. specific heat of moist air (cp)
46. lv latent heat of vaporization (Jkg−1)
47. potential temperature (theta)
48. number of iterations until convergence
49. flag ("n": normal, "o": out of nominal range,
"u": u10n<0, "q":q10n<0 or q>0.04
"m": missing,
"l": Rib<-0.5 or Rib>0.2 or z/L>1000,
"r" : rh>100%,
"t" : t10n<173K or t10n>373K
"i": convergence fail at n)
2021 / Author S. Biri
2021 / Restructured by R. Cornes
2021 / Simplified by E. Kent
"""
logging.basicConfig(filename='flux_calc.log', filemode="w",
format='%(asctime)s %(message)s', level=logging.INFO)
logging.captureWarnings(True)
iclass = globals()[meth]()
iclass.add_gust(gust=gust)
iclass.add_variables(spd, T, SST, lat=lat, hum=hum, P=P, L=L)
iclass.get_heights(hin, hout)
iclass.get_specHumidity(qmeth=qmeth)
iclass.set_coolskin_warmlayer(wl=wl, cskin=cskin, skin=skin, Rl=Rl, Rs=Rs)
iclass.iterate(tol=tol, maxiter=maxiter)
resAll = iclass.get_output(out_var=out_var, out=out)
return resAll