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Commit 848e57a9 authored by Richard Cornes's avatar Richard Cornes
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Added test file

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Code/AirSeaFluxCode_test.py 0 → 100644
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import importlib
import sys
sys.path.insert(1, '/Users/ricorne/projects/orchestra/Code')
import AirSeaFluxCode
importlib.reload(AirSeaFluxCode)
from AirSeaFluxCode import *
import warnings
import numpy as np
import pandas as pd
import logging
from get_init import get_init
from hum_subs import (get_hum, gamma)
from util_subs import *
from flux_subs import *
def AirSeaFluxCode_OLD(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)
# check input values and set defaults where appropriate
lat, hum, P, Rl, Rs, cskin, skin, wl, gust, tol, L, n = get_init(spd, T,
SST, lat,
hum, P,
Rl, Rs,
cskin,
skin,
wl, gust,
L, tol, n,
meth,
qmeth)
ref_ht = 10 # reference height
h_in = get_heights(hin, len(spd)) # heights of input measurements/fields
h_out = get_heights(hout, 1) # desired height of output variables
logging.info('method %s, inputs: lat: %s | P: %s | Rl: %s |'
' Rs: %s | gust: %s | cskin: %s | L : %s', meth,
np.nanmedian(lat), np.round(np.nanmedian(P), 2),
np.round(np.nanmedian(Rl), 2), np.round(np.nanmedian(Rs), 2),
gust, cskin, L)
# set up/calculate temperatures and specific humidities
sst = np.where(SST < 200, np.copy(SST)+CtoK, np.copy(SST))
qair, qsea = get_hum(hum, T, sst, P, qmeth)
# mask to preserve missing values when initialising variables
msk = np.empty(sst.shape)
msk = np.where(np.isnan(spd+T+SST), np.nan, 1)
Rb = np.empty(sst.shape)*msk
g = gc(lat)
# specific heat
cp = 1004.67*(1+0.00084*qsea)
th = np.where(T < 200, (np.copy(T)+CtoK) *
np.power(1000/P, 287.1/cp),
np.copy(T)*np.power(1000/P, 287.1/cp)) # potential T
# lapse rate
tlapse = gamma("dry", SST, T, qair/1000, cp)
Ta = np.where(T < 200, np.copy(T)+CtoK+tlapse*h_in[1],
np.copy(T)+tlapse*h_in[1]) # convert to Kelvin if needed
logging.info('method %s and q method %s | qsea:%s, qair:%s', meth, qmeth,
np.round(np.nanmedian(qsea), 7),
np.round(np.nanmedian(qair), 7))
if (np.all(np.isnan(qsea)) or np.all(np.isnan(qair))):
print("qsea and qair cannot be nan")
if ((hum[0] == 'rh') or (hum[0] == 'no')):
rh = hum[1]
elif (hum[0] == 'Td'):
Td = hum[1] # dew point temperature (K)
Td = np.where(Td < 200, np.copy(Td)+CtoK, np.copy(Td))
T = np.where(T < 200, np.copy(T)+CtoK, np.copy(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)))
rh = 100*esd/es
flag = np.empty(spd.shape, dtype="object")
flag[:] = "n"
if (hum[0] == 'no'):
if (cskin == 1):
flag = np.where(np.isnan(spd+T+SST+P+Rs+Rl), "m", flag)
else:
flag = np.where(np.isnan(spd+T+SST+P), "m", flag)
else:
if (cskin == 1):
flag = np.where(np.isnan(spd+T+SST+hum[1]+P+Rs+Rl), "m", flag)
else:
flag = np.where(np.isnan(spd+T+SST+hum[1]+P), "m", flag)
flag = np.where(rh > 100, "r", flag)
dt = Ta - sst
dq = qair - qsea
# first guesses
t10n, q10n = np.copy(Ta), np.copy(qair)
tv10n = t10n*(1+0.6077*q10n)
# Zeng et al. 1998
tv = th*(1+0.6077*qair) # virtual potential T
dtv = dt*(1+0.6077*qair)+0.6077*th*dq
# ------------
rho = P*100/(287.1*tv10n)
lv = (2.501-0.00237*(sst-CtoK))*1e6 # J/kg
# cskin parameters
tkt = 0.001*np.ones(T.shape)*msk
dter = -0.3*np.ones(T.shape)*msk
dqer = dter*0.622*lv*qsea/(287.1*np.power(sst, 2))
Rnl = 0.97*(Rl-5.67e-8*np.power(sst-0.3*cskin, 4))
Qs = 0.945*Rs
dtwl = np.ones(T.shape)*0.3*msk
skt = np.copy(sst)
# gustiness adjustment
if (gust[0] == 1 and meth == "UA"):
wind = np.where(dtv >= 0, np.where(spd > 0.1, spd, 0.1),
np.sqrt(np.power(np.copy(spd), 2)+np.power(0.5, 2)))
elif (gust[0] == 1):
wind = np.sqrt(np.power(np.copy(spd), 2)+np.power(0.5, 2))
elif (gust[0] == 0):
wind = np.copy(spd)
u10n = wind*np.log(10/1e-4)/np.log(hin[0]/1e-4)
usr = 0.035*u10n
cd10n = cdn_calc(u10n, usr, Ta, lat, meth)
psim, psit, psiq = (np.zeros(spd.shape)*msk, np.zeros(spd.shape)*msk,
np.zeros(spd.shape)*msk)
cd = cd_calc(cd10n, h_in[0], ref_ht, psim)
tsr, tsrv, qsr = (np.zeros(spd.shape)*msk, np.zeros(spd.shape)*msk,
np.zeros(spd.shape)*msk)
usr = np.sqrt(cd*np.power(wind, 2))
zo, zot, zoq = (1e-4*np.ones(spd.shape)*msk, 1e-4*np.ones(spd.shape)*msk,
1e-4*np.ones(spd.shape)*msk)
ct10n = np.power(kappa, 2)/(np.log(h_in[0]/zo)*np.log(h_in[1]/zot))
cq10n = np.power(kappa, 2)/(np.log(h_in[0]/zo)*np.log(h_in[2]/zoq))
ct = np.power(kappa, 2)/((np.log(h_in[0]/zo)-psim) *
(np.log(h_in[1]/zot)-psit))
cq = np.power(kappa, 2)/((np.log(h_in[0]/zo)-psim) *
(np.log(h_in[2]/zoq)-psiq))
Rb = g*10*(dtv)/(np.where(T < 200, np.copy(T)+CtoK, np.copy(T)) *
np.power(wind, 2)) # Rb eq. 11 Grachev & Fairall 1997
monob = 1/Rb # eq. 12 Grachev & Fairall 1997
tsr = (dt-dter*cskin-dtwl*wl)*kappa/(np.log(h_in[1]/zot) -
psit_calc(h_in[1]/monob, meth))
qsr = (dq-dqer*cskin)*kappa/(np.log(h_in[2]/zoq) -
psit_calc(h_in[2]/monob, meth))
it, ind = 0, np.where(spd > 0)
ii, itera = True, -1*np.ones(spd.shape)*msk
tau = 0.05*np.ones(spd.shape)*msk
sensible = -5*np.ones(spd.shape)*msk
latent = -65*np.ones(spd.shape)*msk
# iteration loop
while np.any(ii):
it += 1
if it > n:
break
if (tol[0] == 'flux'):
old = np.array([np.copy(tau), np.copy(sensible), np.copy(latent)])
elif (tol[0] == 'ref'):
old = np.array([np.copy(u10n), np.copy(t10n), np.copy(q10n)])
elif (tol[0] == 'all'):
old = np.array([np.copy(u10n), np.copy(t10n), np.copy(q10n),
np.copy(tau), np.copy(sensible), np.copy(latent)])
cd10n[ind] = cdn_calc(u10n[ind], usr[ind], Ta[ind], lat[ind], meth)
if (np.all(np.isnan(cd10n))):
break
logging.info('break %s at iteration %s cd10n<0', meth, it)
zo[ind] = ref_ht/np.exp(kappa/np.sqrt(cd10n[ind]))
psim[ind] = psim_calc(h_in[0, ind]/monob[ind], meth)
cd[ind] = cd_calc(cd10n[ind], h_in[0, ind], ref_ht, psim[ind])
ct10n[ind], cq10n[ind] = ctcqn_calc(h_in[1, ind]/monob[ind],
cd10n[ind], usr[ind], zo[ind],
Ta[ind], meth)
zot[ind] = ref_ht/(np.exp(np.power(kappa, 2) /
(ct10n[ind]*np.log(ref_ht/zo[ind]))))
zoq[ind] = ref_ht/(np.exp(np.power(kappa, 2) /
(cq10n[ind]*np.log(ref_ht/zo[ind]))))
psit[ind] = psit_calc(h_in[1, ind]/monob[ind], meth)
psiq[ind] = psit_calc(h_in[2, ind]/monob[ind], meth)
ct[ind], cq[ind] = ctcq_calc(cd10n[ind], cd[ind], ct10n[ind],
cq10n[ind], h_in[:, ind],
[ref_ht, ref_ht, ref_ht], psit[ind],
psiq[ind])
if (meth == "NCAR"):
cd = np.maximum(np.copy(cd), 1e-4)
ct = np.maximum(np.copy(ct), 1e-4)
cq = np.maximum(np.copy(cq), 1e-4)
zo = np.minimum(np.copy(zo), 0.0025)
usr[ind], tsr[ind], qsr[ind] = get_strs(h_in[:, ind], monob[ind],
wind[ind], zo[ind], zot[ind],
zoq[ind], dt[ind], dq[ind],
dter[ind], dqer[ind],
dtwl[ind], ct[ind], cq[ind],
cskin, wl, meth)
if ((cskin == 1) and (wl == 0)):
if (skin == "C35"):
dter[ind], dqer[ind], tkt[ind] = cs_C35(np.copy(sst[ind]),
qsea[ind],
rho[ind], Rs[ind],
Rnl[ind],
cp[ind], lv[ind],
np.copy(tkt[ind]),
usr[ind], tsr[ind],
qsr[ind], lat[ind])
elif (skin == "ecmwf"):
dter[ind] = cs_ecmwf(rho[ind], Rs[ind], Rnl[ind], cp[ind],
lv[ind], usr[ind], tsr[ind], qsr[ind],
np.copy(sst[ind]), lat[ind])
dqer[ind] = (dter[ind]*0.622*lv[ind]*qsea[ind] /
(287.1*np.power(sst[ind], 2)))
elif (skin == "Beljaars"):
Qs[ind], dter[ind] = cs_Beljaars(rho[ind], Rs[ind], Rnl[ind],
cp[ind], lv[ind], usr[ind],
tsr[ind], qsr[ind], lat[ind],
np.copy(Qs[ind]))
dqer = dter*0.622*lv*qsea/(287.1*np.power(sst, 2))
skt = np.copy(sst)+dter
elif ((cskin == 1) and (wl == 1)):
if (skin == "C35"):
dter[ind], dqer[ind], tkt[ind] = cs_C35(sst[ind], qsea[ind],
rho[ind], Rs[ind],
Rnl[ind],
cp[ind], lv[ind],
np.copy(tkt[ind]),
usr[ind], tsr[ind],
qsr[ind], lat[ind])
dtwl[ind] = wl_ecmwf(rho[ind], Rs[ind], Rnl[ind], cp[ind],
lv[ind], usr[ind], tsr[ind], qsr[ind],
np.copy(sst[ind]), np.copy(skt[ind]),
np.copy(dter[ind]), lat[ind])
skt = np.copy(sst)+dter+dtwl
elif (skin == "ecmwf"):
dter[ind] = cs_ecmwf(rho[ind], Rs[ind], Rnl[ind], cp[ind],
lv[ind], usr[ind], tsr[ind], qsr[ind],
sst[ind], lat[ind])
dtwl[ind] = wl_ecmwf(rho[ind], Rs[ind], Rnl[ind], cp[ind],
lv[ind], usr[ind], tsr[ind], qsr[ind],
np.copy(sst[ind]), np.copy(skt[ind]),
np.copy(dter[ind]), lat[ind])
skt = np.copy(sst)+dter+dtwl
dqer[ind] = (dter[ind]*0.622*lv[ind]*qsea[ind] /
(287.1*np.power(skt[ind], 2)))
elif (skin == "Beljaars"):
Qs[ind], dter[ind] = cs_Beljaars(rho[ind], Rs[ind], Rnl[ind],
cp[ind], lv[ind], usr[ind],
tsr[ind], qsr[ind], lat[ind],
np.copy(Qs[ind]))
dtwl[ind] = wl_ecmwf(rho[ind], Rs[ind], Rnl[ind], cp[ind],
lv[ind], usr[ind], tsr[ind], qsr[ind],
np.copy(sst[ind]), np.copy(skt[ind]),
np.copy(dter[ind]), lat[ind])
skt = np.copy(sst)+dter+dtwl
dqer = dter*0.622*lv*qsea/(287.1*np.power(skt, 2))
else:
dter[ind] = np.zeros(sst[ind].shape)
dqer[ind] = np.zeros(sst[ind].shape)
tkt[ind] = 0.001*np.ones(T[ind].shape)
logging.info('method %s | dter = %s | dqer = %s | tkt = %s | Rnl = %s '
'| usr = %s | tsr = %s | qsr = %s', meth,
np.round(np.nanmedian(dter), 2),
np.round(np.nanmedian(dqer), 7),
np.round(np.nanmedian(tkt), 2),
np.round(np.nanmedian(Rnl), 2),
np.round(np.nanmedian(usr), 3),
np.round(np.nanmedian(tsr), 4),
np.round(np.nanmedian(qsr), 7))
Rnl[ind] = 0.97*(Rl[ind]-5.67e-8*np.power(sst[ind] +
dter[ind]*cskin, 4))
t10n[ind] = (Ta[ind] -
tsr[ind]/kappa*(np.log(h_in[1, ind]/ref_ht)-psit[ind]))
q10n[ind] = (qair[ind] -
qsr[ind]/kappa*(np.log(h_in[2, ind]/ref_ht)-psiq[ind]))
tv10n[ind] = t10n[ind]*(1+0.6077*q10n[ind])
tsrv[ind], monob[ind], Rb[ind] = get_L(L, lat[ind], usr[ind], tsr[ind],
qsr[ind], h_in[:, ind], Ta[ind],
(sst[ind]+dter[ind]*cskin +
dtwl[ind]*wl), qair[ind],
qsea[ind], wind[ind],
np.copy(monob[ind]), zo[ind],
zot[ind], psim[ind], meth)
psim[ind] = psim_calc(h_in[0, ind]/monob[ind], meth)
psit[ind] = psit_calc(h_in[1, ind]/monob[ind], meth)
psiq[ind] = psit_calc(h_in[2, ind]/monob[ind], meth)
if (gust[0] == 1 and meth == "UA"):
wind[ind] = np.where(dtv[ind] >= 0, np.where(spd[ind] > 0.1,
spd[ind], 0.1),
np.sqrt(np.power(np.copy(spd[ind]), 2) +
np.power(get_gust(gust[1], tv[ind],
usr[ind], tsrv[ind],
gust[2], lat[ind]),
2))) # Zeng et al. 1998 (20)
elif (gust[0] == 1):
wind[ind] = np.sqrt(np.power(np.copy(spd[ind]), 2) +
np.power(get_gust(gust[1], Ta[ind], usr[ind],
tsrv[ind], gust[2],
lat[ind]), 2))
elif (gust[0] == 0):
wind[ind] = np.copy(spd[ind])
u10n[ind] = wind[ind]-usr[ind]/kappa*(np.log(h_in[0, ind]/10) -
psim[ind])
if (it < 4): # make sure you allow small negative values convergence
u10n = np.where(u10n < 0, 0.5, u10n)
utmp = np.copy(u10n)
utmp = np.where(utmp < 0, np.nan, utmp)
itera[ind] = np.ones(1)*it
tau = rho*np.power(usr, 2)*(spd/wind)
sensible = rho*cp*usr*tsr
latent = rho*lv*usr*qsr
if (tol[0] == 'flux'):
new = np.array([np.copy(tau), np.copy(sensible), np.copy(latent)])
elif (tol[0] == 'ref'):
new = np.array([np.copy(utmp), np.copy(t10n), np.copy(q10n)])
elif (tol[0] == 'all'):
new = np.array([np.copy(utmp), np.copy(t10n), np.copy(q10n),
np.copy(tau), np.copy(sensible), np.copy(latent)])
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]))
if (ind[0].size == 0):
ii = False
else:
ii = True
# END ITERATION LOOP
itera[ind] = -1
itera = np.where(itera > n, -1, itera)
logging.info('method %s | # of iterations:%s', meth, it)
logging.info('method %s | # of points that did not converge :%s \n', meth,
ind[0].size)
# calculate output parameters
rho = (0.34838*P)/(tv10n)
t10n = t10n-(273.16+tlapse*ref_ht)
# solve for zo from cd10n
zo = ref_ht/np.exp(kappa/np.sqrt(cd10n))
# adjust neutral cdn at any output height
cdn = np.power(kappa/np.log(hout/zo), 2)
cd = cd_calc(cdn, h_out[0], h_out[0], psim)
# solve for zot, zoq from ct10n, cq10n
zot = ref_ht/(np.exp(kappa**2/(ct10n*np.log(ref_ht/zo))))
zoq = ref_ht/(np.exp(kappa**2/(cq10n*np.log(ref_ht/zo))))
# adjust neutral ctn, cqn at any output height
ctn = np.power(kappa, 2)/(np.log(h_out[0]/zo)*np.log(h_out[1]/zot))
cqn = np.power(kappa, 2)/(np.log(h_out[0]/zo)*np.log(h_out[2]/zoq))
ct, cq = ctcq_calc(cdn, cd, ctn, cqn, h_out, h_out, psit, psiq)
uref = (spd-usr/kappa*(np.log(h_in[0]/h_out[0])-psim +
psim_calc(h_out[0]/monob, meth)))
tref = (Ta-tsr/kappa*(np.log(h_in[1]/h_out[1])-psit +
psit_calc(h_out[0]/monob, meth)))
tref = tref-(CtoK+tlapse*h_out[1])
qref = (qair-qsr/kappa*(np.log(h_in[2]/h_out[2]) -
psit+psit_calc(h_out[2]/monob, meth)))
if (wl == 0):
dtwl = np.zeros(T.shape)*msk # reset to zero if not used
flag = np.where((u10n < 0) & (flag == "n"), "u",
np.where((u10n < 0) &
(np.char.find(flag.astype(str), 'u') == -1),
flag+[","]+["u"], flag))
flag = np.where((q10n < 0) & (flag == "n"), "q",
np.where((q10n < 0) & (flag != "n"), flag+[","]+["q"],
flag))
flag = np.where(((Rb < -0.5) | (Rb > 0.2) | ((hin[0]/monob) > 1000)) &
(flag == "n"), "l",
np.where(((Rb < -0.5) | (Rb > 0.2) |
((hin[0]/monob) > 1000)) &
(flag != "n"), flag+[","]+["l"], flag))
if (out == 1):
flag = np.where((itera == -1) & (flag == "n"), "i",
np.where((itera == -1) &
((flag != "n") &
(np.char.find(flag.astype(str), 'm') == -1)),
flag+[","]+["i"], flag))
else:
flag = np.where((itera == -1) & (flag == "n"), "i",
np.where((itera == -1) &
((flag != "n") &
(np.char.find(flag.astype(str), 'm') == -1) &
(np.char.find(flag.astype(str), 'u') == -1)),
flag+[","]+["i"], flag))
if (meth == "S80"):
flag = np.where(((utmp < 6) | (utmp > 22)) & (flag == "n"), "o",
np.where(((utmp < 6) | (utmp > 22)) &
((flag != "n") &
(np.char.find(flag.astype(str), 'u') == -1) &
(np.char.find(flag.astype(str), 'q') == -1)),
flag+[","]+["o"], flag))
elif (meth == "LP82"):
flag = np.where(((utmp < 3) | (utmp > 25)) & (flag == "n"), "o",
np.where(((utmp < 3) | (utmp > 25)) &
((flag != "n") &
(np.char.find(flag.astype(str), 'u') == -1) &
(np.char.find(flag.astype(str), 'q') == -1)),
flag+[","]+["o"], flag))
elif (meth == "YT96"):
flag = np.where(((utmp < 0) | (utmp > 26)) & (flag == "n"), "o",
np.where(((utmp < 3) | (utmp > 26)) &
((flag != "n") &
(np.char.find(flag.astype(str), 'u') == -1) &
(np.char.find(flag.astype(str), 'q') == -1)),
flag+[","]+["o"], flag))
elif (meth == "UA"):
flag = np.where((utmp > 18) & (flag == "n"), "o",
np.where((utmp > 18) &
((flag != "n") &
(np.char.find(flag.astype(str), 'u') == -1) &
(np.char.find(flag.astype(str), 'q') == -1)),
flag+[","]+["o"], flag))
elif (meth == "NCAR"):
flag = np.where((utmp < 0.5) & (flag == "n"), "o",
np.where((utmp < 0.5) &
((flag != "n") &
(np.char.find(flag.astype(str), 'u') == -1) &
(np.char.find(flag.astype(str), 'q') == -1)),
flag+[","]+["o"], flag))
# Do not calculate lhf if a measure of humidity is not input
if (hum[0] == 'no'):
latent = np.ones(sst.shape)*np.nan
qsr = np.ones(sst.shape)*np.nan
q10n = np.ones(sst.shape)*np.nan
qref = np.ones(sst.shape)*np.nan
qair = np.ones(sst.shape)*np.nan
rh = np.ones(sst.shape)*np.nan
wind_spd = np.sqrt(np.power(wind, 2)-np.power(spd, 2))
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(spd)))
for i, value in enumerate(res_vars):
res[i][:] = locals()[value]
if (out == 0):
res[:, ind] = np.nan
# set missing values where data have non acceptable values
if (hum[0] != 'no'):
res = np.asarray([np.where(q10n < 0, np.nan,
res[i][:]) for i in range(41)])
res = np.asarray([np.where(u10n < 0, np.nan,
res[i][:]) for i in range(41)])
elif (out == 1):
print("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(len(spd)), columns=res_vars)
resAll["flag"] = flag
return resAll
inDt = pd.read_csv("~/projects/orchestra/Test_Data/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])
Rs = np.asarray(inDt["Rs"])
del hu, ht, inDt
# run AirSeaFluxCode
res = AirSeaFluxCode_OLD(spd, t, sst, lat=lat, hin=hin, P=p, cskin=0,n=10,hum=None,
tol=['all', 0.01, 0.01, 1e-05, 1e-3, 0.1, 0.1], L="Rb",meth="UA", Rs=Rs,Rl=Rs,gust=None)
res1 = AirSeaFluxCode(spd, t, sst, lat=lat, hin=hin, P=p, cskin=0,n=10,hum=None,
tol=['all', 0.01, 0.01, 1e-05, 1e-3, 0.1, 0.1], L="Rb",meth="UA", Rs=Rs,Rl=Rs,gust=None)
print(res1)
print(res)
for i in res1.columns:
print(res1[i].equals(res[i]))
Code/flux_subs.py
View file @ 848e57a9
......@@ -3,7 +3,6 @@ from util_subs import (kappa, gc, visc_air)
# ---------------------------------------------------------------------
def cdn_calc(u10n, usr, Ta, lat, meth):
"""
Calculates neutral drag coefficient
......@@ -720,22 +719,18 @@ def get_strs(hin, monob, wind, zo, zot, zoq, dt, dq, dter, dqer, dtwl, ct, cq,
"""
if (meth == "UA"):
usr = np.where(hin[0]/monob <= -1.574, kappa*wind /
(np.log(-1.574*monob/zo)-psim_calc(-1.574, meth) +
psim_calc(zo/monob, meth) +
1.14*(np.power(-hin[0]/monob, 1/3) -
np.power(1.574, 1/3))),
np.where(hin[0]/monob < 0, kappa*wind /
(np.log(hin[0]/zo) -
psim_calc(hin[0]/monob, meth) +
psim_calc(zo/monob, meth)),
np.where(hin[0]/monob <= 1, kappa*wind /
(np.log(hin[0]/zo) +
5*hin[0]/monob-5*zo/monob),
kappa*wind/(np.log(monob/zo)+5 -
5*zo/monob +
5*np.log(hin[0]/monob) +
hin[0]/monob-1))))
usr = np.where(hin[0]/monob <= -1.574, kappa*wind / (np.log(-1.574*monob/zo)-psim_calc(-1.574, meth) + psim_calc(zo/monob, meth) + 1.14*(np.power(-hin[0]/monob, 1/3) - np.power(1.574, 1/3))),
np.where(hin[0]/monob < 0, kappa*wind / (np.log(hin[0]/zo) - psim_calc(hin[0]/monob, meth) + psim_calc(zo/monob, meth)), np.where(hin[0]/monob <= 1, kappa*wind / (np.log(hin[0]/zo) +
5*hin[0]/monob-5*zo/monob), kappa*wind/(np.log(monob/zo)+5 - 5*zo/monob + 5*np.log(hin[0]/monob) + hin[0]/monob-1))))
usr = np.where(hin[0]/monob <= -1.574, kappa*wind / (np.log(-1.574*monob/zo)-psim_calc(-1.574, meth) + psim_calc(zo/monob, meth) + 1.14*(np.power(-hin[0]/monob, 1/3) - np.power(1.574, 1/3)))
usr = np.where(>= -1.574 hin[0]/monob < 0, kappa*wind / (np.log(hin[0]/zo) - psim_calc(hin[0]/monob, meth) + psim_calc(zo/monob, meth)
usr = np.where(hin[0]/monob <= 1, kappa*wind / (np.log(hin[0]/zo) + 5*hin[0]/monob-5*zo/monob)
usr = kappa*wind/(np.log(monob/zo)+5 - 5*zo/monob + 5*np.log(hin[0]/monob) + hin[0]/monob-1))))
# Zeng et al. 1998 (7-10)
tsr = np.where(hin[1]/monob < -0.465, kappa*(dt-dter*cskin-dtwl*wl) /
(np.log((-0.465*monob)/zot) -
......@@ -783,3 +778,153 @@ def get_strs(hin, monob, wind, zo, zot, zoq, dt, dq, dter, dqer, dtwl, ct, cq,
qsr = cq*wind*(dq-dqer*cskin)/usr
return usr, tsr, qsr
# ---------------------------------------------------------------------
# --------------------------------------------------------------------------------
# --------------------------------------------------------------------------------
# ---------------------------------------------------------------------
def get_tsrv(tsr, qsr, Ta, qair):
"""
calculates virtual star temperature
Parameters
----------
tsr : float
star temperature (K)
qsr : float
star specific humidity (g/kg)
Ta : float
air temperature (K)
qair : float
air specific humidity (g/kg)
Returns
-------
tsrv : float
virtual star temperature (K)
"""
# as in aerobulk One_on_L in mod_phymbl.f90
tsrv = tsr*(1+0.6077*qair)+0.6077*Ta*qsr
return tsrv
# ---------------------------------------------------------------------
def get_Rb(g, usr, hin, Ta, sst, qair, qsea, wind, monob, psim, meth):
"""
calculates bulk Richardson number
Parameters
----------
g : float
acceleration due to gravity (m/s2)
usr : float
friction wind speed (m/s)
hin : float
sensor heights (m)
Ta : float
air temperature (K)
sst : float
sea surface temperature (K)
qair : float
air specific humidity (g/kg)
qsea : float
specific humidity at sea surface (g/kg)
wind : float
wind speed (m/s)
monob : float
Monin-Obukhov length from previous iteration step (m)
psim : float
momentum stability function
meth : str
bulk parameterisation method option: "S80", "S88", "LP82", "YT96",
"UA", "NCAR", "C30", "C35", "ecmwf", "Beljaars"
Returns
-------
Rb : float
Richardson number
"""
thvs = sst*(1+0.6077*qsea) # virtual SST
thv = Ta*(1+0.6077*qair) # virtual potential air temperature
dthv = thv - thvs # virtual air - sea temp. diff
tv = thv + 0.5*dthv # estimate tv within surface layer
# adjust wind to T sensor's height
uz = (wind-usr/kappa*(np.log(hin[0]/hin[1])-psim +
psim_calc(hin[1]/monob, meth)))
Rb = g*dthv*hin[1]/(tv*uz*uz)
return Rb
# ---------------------------------------------------------------------
def get_LRb(Rb, hin, monob, zo, zot, meth):
"""
calculates Monin-Obukhov length following ecmwf (IFS Documentation cy46r1)
default for methods ecmwf and Beljaars
Parameters
----------
Rb : float
Richardson number
hin : float
sensor heights (m)
monob : float
Monin-Obukhov length from previous iteration step (m)
zo : float
surface roughness (m)
zot : float
temperature roughness length (m)
meth : str
bulk parameterisation method option: "S80", "S88", "LP82", "YT96",
"UA", "NCAR", "C30", "C35", "ecmwf", "Beljaars"
Returns
-------
monob : float
M-O length (m)
"""
zol = (Rb*(np.power(np.log((hin[1]+zo)/zo)-psim_calc((hin[1]+zo) /
monob, meth) + psim_calc(zo/monob, meth), 2) /
(np.log((hin[1]+zo)/zot) -
psit_calc((hin[1]+zo)/monob, meth) +
psit_calc(zot/monob, meth))))
monob = 1/zol
return monob
# ---------------------------------------------------------------------
def get_Ltsrv(tsrv, g, tv, usr):
"""
calculates Monin-Obukhov length from tsrv
Parameters
----------
tsrv : float
virtual star temperature (K)
g : float
acceleration due to gravity (m/s2)
tv : float
virtual temperature (K)
usr : float
friction wind speed (m/s)
Returns
-------
monob : float
M-O length (m)
"""
if (L == "tsrv"):
#tmp = (g*kappa*tsrv /
# np.maximum(np.power(usr, 2)*Ta*(1+0.6077*qair), 1e-9))
#tmp = np.minimum(np.abs(tmp), 200)*np.sign(tmp)
#monob = 1/np.copy(tmp)
monob = (np.power(usr, 2)*tv)/(g*kappa*tsrv)
return monob
# ---------------------------------------------------------------------
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