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 *
class S88:
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 = self.qair - self.qsea
# Set lapse rate and Potential Temperature (now we have humdity)
self._get_potentialT()
self._get_lapse()
def _get_potentialT(self):
self.cp = 1004.67*(1+0.00084*self.qsea)
self.th = np.where(self.T < 200, (np.copy(self.T)+CtoK) *
np.power(1000/self.P, 287.1/self.cp),
np.copy(self.T)*np.power(1000/self.P, 287.1/self.cp)) # potential T
def _get_lapse(self):
self.tlapse = gamma("dry", self.SST, self.T, self.qair/1000, self.cp)
self.Ta = np.where(self.T < 200, np.copy(self.T)+CtoK+self.tlapse*self.h_in[1],
np.copy(self.T)+self.tlapse*self.h_in[1]) # convert to Kelvin if needed
self.dt = self.Ta - self.SST
def _fix_coolskin_warmlayer(self, wl, cskin, skin, Rl, Rs):
assert wl in [0,1], "wl not valid"
assert cskin in [0,1], "cskin not valid"
assert skin in ["C35", "ecmwf" or "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.Rs = np.ones(self.spd.shape)*np.nan if Rs is None else Rs
self.Rl = np.ones(self.spd.shape)*np.nan if Rl is None else Rl
def set_coolskin_warmlayer(self, wl=0, cskin=0, skin="C35", Rl=None, Rs=None):
wl = 0 if wl is None else wl
self._fix_coolskin_warmlayer(wl, cskin, skin, Rl, Rs)
def _first_guess(self):
# reference height1
self.ref_ht = 10
# first guesses
self.t10n, self.q10n = np.copy(self.Ta), np.copy(self.qair)
self.tv10n = self.t10n*(1+0.6077*self.q10n)
# Zeng et al. 1998
tv = self.th*(1+0.6077*self.qair) # virtual potential T
self.dtv = self.dt*(1+0.6077*self.qair)+0.6077*self.th*self.dq
# Rb eq. 11 Grachev & Fairall 1997
Rb = self.g*10*(self.dtv)/(np.where(self.T < 200, np.copy(self.T)+CtoK, np.copy(self.T)) * np.power(self.wind, 2))
self.monob = 1/Rb # eq. 12 Grachev & Fairall 1997
# ------------
self.rho = self.P*100/(287.1*self.tv10n)
self.lv = (2.501-0.00237*(self.SST-CtoK))*1e6 # J/kg
self.dter = np.full(self.T.shape, -0.3)*self.msk
self.tkt = np.full(self.T.shape, 0.001)*self.msk
self.dqer = self.dter*0.622*self.lv*self.qsea/(287.1*np.power(self.SST, 2))
self.Rnl = 0.97*(self.Rl-5.67e-8*np.power(self.SST-0.3*self.cskin, 4))
self.Qs = 0.945*self.Rs
self.dtwl = np.full(self.T.shape,0.3)*self.msk
self.skt = np.copy(self.SST)
# Apply the gustiness adjustment if defined for this class
try:
self._adjust_gust()
except AttributeError:
pass
self.u10n = self.wind*np.log(10/1e-4)/np.log(self.hin[0]/1e-4)
self.usr = 0.035*self.u10n
self.cd10n = cdn_calc(self.u10n, self.usr, self.Ta, self.lat, self.meth)
self.cd = cd_calc(self.cd10n, self.h_in[0], self.ref_ht, self.psim)
self.usr = np.sqrt(self.cd*np.power(self.wind, 2))
self.zo = np.full(self.arr_shp,1e-4)*self.msk
self.zot, self.zoq = np.copy(self.zo), np.copy(self.zo)
self.ct10n = np.power(kappa, 2)/(np.log(self.h_in[0]/self.zo)*np.log(self.h_in[1]/self.zot))
self.cq10n = np.power(kappa, 2)/(np.log(self.h_in[0]/self.zo)*np.log(self.h_in[2]/self.zoq))
self.ct = np.power(kappa, 2)/((np.log(self.h_in[0]/self.zo)-self.psim) * (np.log(self.h_in[1]/self.zot)-self.psit))
self.cq = np.power(kappa, 2)/((np.log(self.h_in[0]/self.zo)-self.psim) * (np.log(self.h_in[2]/self.zoq)-self.psiq))
self.tsr = (self.dt-self.dter*self.cskin-self.dtwl*self.wl)*kappa/(np.log(self.h_in[1]/self.zot) - psit_calc(self.h_in[1]/self.monob, self.meth))
self.qsr = (self.dq-self.dqer*self.cskin)*kappa/(np.log(self.h_in[2]/self.zoq) - psit_calc(self.h_in[2]/self.monob, self.meth))
def _zo_calc(self, ref_ht, cd10n):
zo = ref_ht/np.exp(kappa/np.sqrt(cd10n))
return zo
def iterate(self,n=10, tol=None):
n = 5 if n < 5 else n
# 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":["utmp","t10n","q10n"]}
new_vars["all"] = new_vars["ref"] + new_vars["flux"]
new_vars = new_vars[tol[0]]
ind = np.where(self.spd > 0)
it = 0
# Setup empty arrays
self.itera = np.full(self.arr_shp,-1)*self.msk
self.tsrv = np.zeros(self.arr_shp)*self.msk
self.psim, self.psit, self.psiq = np.copy(self.tsrv), np.copy(self.tsrv), np.copy(self.tsrv)
self.tau = np.full(self.arr_shp,0.05)*self.msk
self.sensible = np.full(self.arr_shp,-5)*self.msk
self.latent = np.full(self.arr_shp,-65)*self.msk
# Generate the first guess values
self._first_guess()
# iteration loop
ii = True
while ii:
it += 1
if it > n: break
# 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] = cdn_calc(self.u10n[ind], self.usr[ind], self.Ta[ind], self.lat[ind], self.meth)
if (np.all(np.isnan(self.cd10n))):
break
logging.info('break %s at iteration %s cd10n<0', meth, it)
self.zo[ind] = self.ref_ht/np.exp(kappa/np.sqrt(self.cd10n[ind]))
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.ref_ht, self.psim[ind])
self.ct10n[ind], self.cq10n[ind] = ctcqn_calc(self.h_in[1, ind]/self.monob[ind], self.cd10n[ind],
self.usr[ind], self.zo[ind], self.Ta[ind], self.meth)
self.zot[ind] = self.ref_ht/(np.exp(np.power(kappa, 2) / (self.ct10n[ind]*np.log(self.ref_ht/self.zo[ind]))))
self.zoq[ind] = self.ref_ht/(np.exp(np.power(kappa, 2) / (self.cq10n[ind]*np.log(self.ref_ht/self.zo[ind]))))
self.psit[ind] = psit_calc(self.h_in[1, ind]/self.monob[ind], self.meth)
self.psiq[ind] = psit_calc(self.h_in[2, ind]/self.monob[ind], self.meth)
self.ct[ind], self.cq[ind] = ctcq_calc(self.cd10n[ind], self.cd[ind], self.ct10n[ind],self.cq10n[ind], self.h_in[:, ind],
[self.ref_ht, self.ref_ht, self.ref_ht], self.psit[ind], self.psiq[ind])
# Some parameterizations set a minimum on parameters
try:
self._minimum_params()
except AttributeError:
pass
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[ind], self.dq[ind],
self.dter[ind], self.dqer[ind],
self.dtwl[ind], self.ct[ind], self.cq[ind],
self.cskin, self.wl, self.meth)
self.dter[ind] = 0
self.dqer[ind] = 0
self.tkt[ind] = 0.001
# 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))
self.Rnl[ind] = 0.97*(self.Rl[ind]-5.67e-8*np.power(self.SST[ind] + self.dter[ind]*self.cskin, 4))
self.t10n[ind] = (self.Ta[ind] - self.tsr[ind]/kappa*(np.log(self.h_in[1, ind]/self.ref_ht)-self.psit[ind]))
self.q10n[ind] = (self.qair[ind] - self.qsr[ind]/kappa*(np.log(self.h_in[2, ind]/self.ref_ht)-self.psiq[ind]))
self.tv10n[ind] = self.t10n[ind]*(1+0.6077*self.q10n[ind])
self.tsrv[ind], self.monob[ind], self.Rb[ind] = get_L(self.L, self.lat[ind], self.usr[ind], self.tsr[ind], self.qsr[ind], self.h_in[:, ind], self.Ta[ind],
(self.SST[ind]+self.dter[ind]*self.cskin + self.dtwl[ind]*self.wl), self.qair[ind], self.qsea[ind], self.wind[ind],
np.copy(self.monob[ind]), self.zo[ind], self.zot[ind], self.psim[ind], self.meth)
self.psim[ind] = psim_calc(self.h_in[0, ind]/self.monob[ind], self.meth)
self.psit[ind] = psit_calc(self.h_in[1, ind]/self.monob[ind], self.meth)
self.psiq[ind] = psit_calc(self.h_in[2, ind]/self.monob[ind], self.meth)
if (self.gust[0] == 1):
self.wind[ind] = np.sqrt(np.power(np.copy(self.spd[ind]), 2) + np.power(get_gust(self.gust[1], self.Ta[ind], self.usr[ind],
self.tsrv[ind], self.gust[2], self.lat[ind]), 2))
elif (self.gust[0] == 0):
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]/10) - self.psim[ind])
# make sure you allow small negative values convergence
if (it < 4): self.u10n = np.where(self.u10n < 0, 0.5, self.u10n)
self.utmp = np.copy(self.u10n)
self.utmp = np.where(self.utmp < 0, np.nan, self.utmp)
self.itera[ind] = np.ones(1)*it
self.tau = self.rho*np.power(self.usr, 2)*(self.spd/self.wind)
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 two iterations
d = np.abs(new-old)
if (tol[0] == 'flux'):
ind = np.where((d[0, :] > tol[1])+(d[1, :] > tol[2]) + (d[2, :] > tol[3]))
elif (tol[0] == 'ref'):
ind = np.where((d[0, :] > tol[1])+(d[1, :] > tol[2]) + (d[2, :] > tol[3]))
elif (tol[0] == 'all'):
ind = np.where((d[0, :] > tol[1])+(d[1, :] > tol[2]) + (d[2, :] > tol[3])+(d[3, :] > tol[4]) +
(d[4, :] > tol[5])+(d[5, :] > tol[6]))
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 > n, -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):
"RH only used for flagging purposes"
if ((self.hum[0] == 'rh') or (self.hum[0] == '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))
#T = 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
def _flag(self,out=0):
"Set the general flags"
flag = np.full(self.arr_shp, "n",dtype="object")
if (self.hum[0] == 'no'):
self.flag = np.where(np.isnan(self.spd+self.T+self.SST+self.P), "m", flag)
else:
flag = np.where(np.isnan(self.spd+self.T+self.SST+self.hum[1]+self.P), "m", flag)
flag = np.where(self.rh > 100, "r", flag)
flag = np.where((self.u10n < 0) & (flag == "n"), "u",
np.where((self.u10n < 0) &
(np.char.find(flag.astype(str), 'u') == -1),
flag+[","]+["u"], flag))
flag = np.where((self.q10n < 0) & (flag == "n"), "q",
np.where((self.q10n < 0) & (flag != "n"), flag+[","]+["q"],
flag))
flag = np.where(((self.Rb < -0.5) | (self.Rb > 0.2) | ((self.hin[0]/self.monob) > 1000)) &
(flag == "n"), "l",
np.where(((self.Rb < -0.5) | (self.Rb > 0.2) |
((self.hin[0]/self.monob) > 1000)) &
(flag != "n"), flag+[","]+["l"], flag))
if (out == 1):
flag = np.where((self.itera == -1) & (flag == "n"), "i",
np.where((self.itera == -1) &
((flag != "n") &
(np.char.find(flag.astype(str), 'm') == -1)),
flag+[","]+["i"], flag))
else:
flag = np.where((self.itera == -1) & (flag == "n"), "i",
np.where((self.itera == -1) &
((flag != "n") &
(np.char.find(flag.astype(str), 'm') == -1) &
(np.char.find(flag.astype(str), 'u') == -1)),
flag+[","]+["i"], flag))
self.flag = flag
def get_output(self,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
# calculate output parameters
rho = (0.34838*self.P)/(self.tv10n)
self.t10n = self.t10n-(273.16+self.tlapse*self.ref_ht)
# solve for zo from cd10n
zo = self.ref_ht/np.exp(kappa/np.sqrt(self.cd10n))
# adjust neutral cdn at any output height
self.cdn = np.power(kappa/np.log(self.hout/zo), 2)
self.cd = cd_calc(self.cdn, self.h_out[0], self.h_out[0], self.psim)
# solve for zot, zoq from ct10n, cq10n
zot = self.ref_ht/(np.exp(kappa**2/(self.ct10n*np.log(self.ref_ht/zo))))
zoq = self.ref_ht/(np.exp(kappa**2/(self.cq10n*np.log(self.ref_ht/zo))))
# adjust neutral ctn, cqn at any output height
self.ctn = np.power(kappa, 2)/(np.log(self.h_out[0]/zo)*np.log(self.h_out[1]/zot))
self.cqn = np.power(kappa, 2)/(np.log(self.h_out[0]/zo)*np.log(self.h_out[2]/zoq))
self.ct, self.cq = ctcq_calc(self.cdn, self.cd, self.ctn, self.cqn, self.h_out, self.h_out, self.psit, self.psiq)
self.uref = (self.spd-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)))
tref = (self.Ta-self.tsr/kappa*(np.log(self.h_in[1]/self.h_out[1])-self.psit + psit_calc(self.h_out[0]/self.monob, self.meth)))
self.tref = tref-(CtoK+self.tlapse*self.h_out[1])
self.qref = (self.qair-self.qsr/kappa*(np.log(self.h_in[2]/self.h_out[2]) - self.psit+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
if (self.hum[0] == 'no'):
self.latent = np.ones(self.SST.shape)*np.nan
self.qsr = np.copy(self.latent)
self.q10n = np.copy(self.latent)
self.qref = np.copy(self.latent)
self.qair = np.copy(self.latent)
self.rh = np.copy(self.latent)
# Set the final wind speed values
self.wind_spd = np.sqrt(np.power(self.wind, 2)-np.power(self.spd, 2))
# Get class specific flags
try:
self._class_flag()
except AttributeError:
pass
# Combine all output variables into a pandas array
res_vars = ("tau","sensible","latent","monob","cd","cdn","ct","ctn","cq","cqn","tsrv","tsr","qsr","usr","psim","psit",
"psiq","u10n","t10n","tv10n","q10n","zo","zot","zoq","uref","tref","qref","itera","dter","dqer","dtwl",
"qair","qsea","Rl","Rs","Rnl","wind_spd","Rb","rh","tkt","lv")
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))]) # FIXME: why 41?
res = np.asarray([np.where(self.u10n < 0, 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)
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 = 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.g = gc(self.lat)
self.P = np.full(n, 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
self.dtwl = np.full(T.shape,0.3)*self.msk
# Set the wind array
if (self.gust[0] == 1):
self.wind = np.sqrt(np.power(np.copy(self.spd), 2)+np.power(0.5, 2))
elif (self.gust[0] == 0):
self.wind = np.copy(spd)
def add_gust(self,gust=None):
if np.all(gust == None):
try:
gust = self.default_gust
except AttributeError:
gust = [1,1.2,800]
elif ((np.size(gust) < 3) and (gust == 0)):
gust = [0, 0, 0]
assert np.size(gust) == 3, "gust input must be a 3x1 array"
self.gust = gust
def __init__(self):
self.meth = "S88"
class S80(S88):
def _class_flag(self):
"A flag specific to this class"
self.flag = np.where(((self.utmp < 6) | (self.utmp > 22)) & (self.flag == "n"), "o",
np.where(((self.utmp < 6) | (self.utmp > 22)) &
((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 = "S80"
class YT96(S88):
def _class_flag(self):
self.flag = np.where(((self.utmp < 0) | (self.utmp > 26)) & (self.flag == "n"), "o",
np.where(((self.utmp < 3) | (self.utmp > 26)) &
((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 = "YT96"
class LP82(S88):
def _class_flag(self):
self.flag = np.where(((self.utmp < 3) | (self.utmp > 25)) & (self.flag == "n"), "o",
np.where(((self.utmp < 3) | (self.utmp > 25)) &
((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 = "LP82"
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 _class_flag(self):
self.flag = np.where((self.utmp < 0.5) & (self.flag == "n"), "o",
np.where((self.utmp < 0.5) &
((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 _zo_calc(self, ref_ht, cd10n):
"Special z0 calculation for NCAR"
zo = ref_ht/np.exp(kappa/np.sqrt(cd10n))
zo = np.minimum(np.copy(zo), 0.0025)
return zo
def __init__(self):
self.meth = "NCAR"
class UA(S88):
def _class_flag(self):
self.flag = np.where((self.utmp > 18) & (self.flag == "n"), "o",
np.where((self.utmp > 18) &
((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 _adjust_gust(self):
# gustiness adjustment
if (self.gust[0] == 1):
self.wind = np.where(self.dtv >= 0, np.where(self.spd > 0.1, self.spd, 0.1),
np.sqrt(np.power(np.copy(self.spd), 2)+np.power(0.5, 2)))
def __init__(self):
self.meth = "UA"
self.default_gust = [1,1,1000]
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]
class C35(C30):
def __init__(self):
self.meth = "C35"
self.default_gust = [1,1.2,600]
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 __init__(self):
self.meth = "ecmwf"
self.default_gust = [1,1,1000]
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 = [1,1,1000]
def AirSeaFluxCode(spd, T, SST, lat=None, hum=None, P=None, hin=18, hout=10,
Rl=None, Rs=None, cskin=None, skin="C35", wl=0, gust=None,
meth="S88", qmeth="Buck2", tol=None, n=10, out=0, L=None):
"""
Calculates turbulent surface fluxes using different parameterizations
Calculates 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)
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 1
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
3x1 [x, beta, zi] x=1 to include the effect of gustiness, else 0
beta gustiness parameter, beta=1 for UA, beta=1.2 for COARE
zi PBL height (m) 600 for COARE, 1000 for UA and ecmwf, 800 default
default for COARE [1, 1.2, 600]
default for UA, ecmwf [1, 1, 1000]
default else [1, 1.2, 800]
meth : str
"S80", "S88", "LP82", "YT96", "UA", "NCAR", "C30", "C35",
"ecmwf", "Beljaars"
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]
n : int
number of iterations (defautl = 10)
out : int
set 0 to set points that have not converged, negative values of
u10n and q10n to missing (default)
set 1 to keep points
L : str
Monin-Obukhov length definition options
"tsrv" : default for "S80", "S88", "LP82", "YT96", "UA", "NCAR",
"C30", "C35"
"Rb" : following ecmwf (IFS Documentation cy46r1), default for
"ecmwf", "Beljaars"
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 (cdn)
7. heat exchange coefficient (ct)
8. neutral heat exchange coefficient (ctn)
9. moisture exhange coefficient (cq)
10. neutral moisture exchange coefficient (cqn)
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. 10m neutral wind speed (u10n)
19. 10m neutral temperature (t10n)
20. 10m neutral virtual temperature (tv10n)
21. 10m neutral specific humidity (q10n)
22. surface roughness length (zo)
23. heat roughness length (zot)
24. moisture roughness length (zoq)
25. wind speed at reference height (uref)
26. temperature at reference height (tref)
27. specific humidity at reference height (qref)
28. number of iterations until convergence
29. cool-skin temperature depression (dter)
30. cool-skin humidity depression (dqer)
31. warm layer correction (dtwl)
32. specific humidity of air (qair)
33. specific humidity at sea surface (qsea)
34. downward longwave radiation (Rl)
35. downward shortwave radiation (Rs)
36. downward net longwave radiation (Rnl)
37. gust wind speed (ug)
38. Bulk Richardson number (Rib)
39. relative humidity (rh)
40. thickness of the viscous layer (delta)
41. lv latent heat of vaporization (Jkg−1)
42. flag ("n": normal, "o": out of nominal range,
"u": u10n<0, "q":q10n<0
"m": missing,
"l": Rib<-0.5 or Rib>0.2 or z/L>1000,
"r" : rh>100%,
"i": convergence fail at n)
2021 / Author S. Biri
"""
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)
resAll = iclass.get_output(out=out)
return resAll