def swin(self, pob, sob, tob, stat, dates):
""" toposcale surface pressure using hypsometric equation - move to own
class
EDITS Feb 20 2020:
-See https://www.evernote.com/Home.action?login=true#n=78e92684-4d64-4802-ab6c-2cb90b277e44&s=s500&ses=1&sh=5&sds=5&x=&
-
EDITS Jul 11 2019:
- removed elevation scaling as this degrade results - at least in WFJ test- Kris?
- reimplemnt original additative ele method - better than beers, or at least less damaging
- removed mask, why is this causing problems?
- dotprod method (corripio) does not seem to work - I dont think it ever did as we used SWTopo ==FALSE
- use Dozier cos corrction method (as in paper right)
- So ... we have ele, illumination angle, self shading and svf correction. We do not have horizon correction (partly in self shading of course)
"""
## Specific humidity routine.
# mrvf=0.622.*vpf./(psf-vpf); #Mixing ratio for water vapor at subgrid.
# qf=mrvf./(1+mrvf); # Specific humidity at subgrid [kg/kg].
# fout.q(:,:,n)=qf;
""" Maybe follow Dubayah's approach (as in Rittger and Girotto) instead
for the shortwave downscaling, other than terrain effects. """
""" Height of the "grid" (coarse scale)"""
Zc = sob.z
""" toa """
SWtoa = sob.tisr
""" Downwelling shortwave flux of the "grid" using nearest neighbor."""
SWc = sob.ssrd
"""Calculate the clearness index."""
kt = SWc / SWtoa
#kt[is.na(kt)==T]<-0 # make sure 0/0 =0
#kt[is.infinite(kt)==T]<-0 # make sure 0/0 =0
kt[kt < 0] = 0
kt[kt > 1] = 0.8 #upper limit of kt
self.kt = kt
"""
Calculate the diffuse fraction following the regression of Ruiz-Arias 2010
"""
kd = 0.952 - 1.041 * np.exp(-1 * np.exp(2.3 - 4.702 * kt))
self.kd = kd
""" Use this to calculate the downwelling diffuse and direct shortwave radiation at grid. """
SWcdiff = kd * SWc
SWcdir = (1 - kd) * SWc
self.SWcdiff = SWcdiff
self.SWcdir = SWcdir
""" Use the above with the sky-view fraction to calculate the
downwelling diffuse shortwave radiation at subgrid. """
self.SWfdiff = SWcdiff #* stat.svf
self.SWfdiff.set_fill_value(0)
self.SWfdiff = self.SWfdiff.filled()
""" Direct shortwave routine, modified from Joel.
Get surface pressure at "grid" (coarse scale). Can remove this
part once surface pressure field is downloaded, or just check
for existance. """
#T1=Ttemp(thisp)
#z1=ztemp(thisp)
#p1=pob.levels(thisp)*1e2 #Convert to Pa.
#Tbar=mean([T0 T1])
"""compute julian dates"""
jd = sg.to_jd(dates)
"""
Calculates a unit vector in the direction of the sun from the observer
position.
"""
sunv = sg.sunvector(jd=jd,
latitude=stat.lat,
longitude=stat.lon,
timezone=stat.tz)
"""
Computes azimuth , zenith and sun elevation
for each timestamp
"""
sp = sg.sunpos(sunv)
self.sp = sp
# Cosine of the zenith angle.
sp.zen = sp.zen
#sp.zen=sp.zen*(sp.zen>0) # Sun might be below the horizon.
muz = np.cos(sp.zen)
self.muz = muz
# NB! psc must be in Pa (NOT hPA!).
#if np.max(psc<1.5e3): # Obviously not in Pa
#psc=psc*1e2
# Compute air pressure at fine grid
ztemp = pob.z
Ttemp = pob.t
statz = stat.ele * self.g
dz = ztemp.transpose(
) - statz # transpose not needed but now consistent with CGC surface pressure equations
# compute air pressure at fine grid (could remove if download air pressure variable)
self.psf = []
# loop through timesteps
for i in range(0, dates.size):
# # find overlying layer
thisp = dz[:, i] == np.min(dz[:, i][dz[:, i] > 0])
# booleen indexing
T1 = Ttemp[i, thisp]
z1 = ztemp[i, thisp]
p1 = pob.levels[thisp] * 1e2 #Convert to Pa.
Tbar = np.mean([T1, tob.t[i]], axis=0)
""" Hypsometric equation."""
self.psf.append(p1 * np.exp(
(z1 - statz) * (self.g / (Tbar * self.R))))
self.psf = np.array(self.psf).squeeze()
# Method 1 BEERS LAW
if (BEERS == 'TRUE'):
# compute air pressure at coarse grid
dz = ztemp.transpose() - sob.z
self.psc = []
for i in range(0, dz.shape[1]):
#thisp.append(np.argmin(dz[:,i][dz[:,i]>0]))
thisp = dz[:, i] == np.min(dz[:, i][dz[:, i] > 0])
z0 = sob.z[i]
T0 = sob.t2m[i]
T1 = Ttemp[i, thisp]
z1 = ztemp[i, thisp]
p1 = pob.levels[thisp] * 1e2 #Convert to Pa.
Tbar = np.mean([T0, T1], axis=0)
""" Hypsometric equation."""
self.psc.append(p1 * np.exp(
(z1 - z0) * (self.g / (Tbar * self.R))))
self.psc = np.array(self.psc).squeeze()
# Calculate the "broadband" absorption coefficient. Elevation correction
# from Kris
ka = (self.g * muz / (self.psc)) * np.log(SWtoa / SWcdir)
#ka.set_fill_value(0)
#ka = ka.filled()
# set inf (from SWtoa/SWcdir at nigh, zero division) to 0 (night)
ka[ka == -np.inf] = 0
ka[ka == np.inf] = 0
# Note this equation is obtained by inverting Beer's law, i.e. use
#I_0=I_inf x exp[(-ka/mu) int_z0**inf rho dz]
# Along with hydrostatic equation to convert to pressure coordinates then
# solve for ka using p(z=inf)=0.
# Method 1 Beers law
# Now you can (finally) find the direct component at subgrid.
self.SWfdir = SWtoa * np.exp(-ka * self.psf / (self.g * muz))
#self.SWfdir=SWcdir
# Method 2 ORIGINAL ELEVATION Correction
# https://hal.archives-ouvertes.fr/hal-00470155/document
"""ele diff in km"""
coarseZ = Zc[0] / 9.9
dz = (stat.ele - coarseZ) / 1000 # fine - coase in knm
s = SWtoa
b = SWcdir
zen = sp.zen
thetaz = (np.pi / 180) * zen #radians
m = 1 / np.cos(thetaz)
k = -np.log(b / s) / m
k.set_fill_value(0)
k = k.filled()
#k[is.na(k)==T]<-0 #correct b=0/s=0 problem
t = np.exp(1)**(-k * dz * np.cos(thetaz))
t[t > 1] < -1.1 #to constrain to reasonable values
t[t < 0.8] < -0.9 #to constrain to reasonable values
db = (1 - t) * SWcdir
self.SWfdir = SWcdir + db #additative correction
#======================================
""" Then perform the terrain correction. [Corripio 2003 / rpackage insol port]."""
"""compute mean horizon elevation - why negative hor.el possible??? Have tested seesm OK """
# In [485]: (((np.arccos(np.sqrt(0.9))*180.)/np.pi)*2)-20
# Out[485]: 16.869897645844034
# In [486]: (((np.arccos(np.sqrt(1))*180.)/np.pi)*2)-0
# Out[486]: 0.0
# In [487]: (((np.arccos(np.sqrt(0.9))*180.)/np.pi)*2)-0
# Out[487]: 36.869897645844034
# In [488]: (((np.arccos(np.sqrt(0.9))*180.)/np.pi)*2)-10
# Out[488]: 26.869897645844034
# In [489]: (((np.arccos(np.sqrt(0.95))*180.)/np.pi)*2)-10
# Out[489]: 15.841932763167158
horel = (((np.arccos(np.sqrt(stat.svf)) * 180) / np.pi) * 2) - stat.slp
if horel < 0:
horel = 0
self.meanhorel = horel
"""
normal vector - Calculates a unit vector normal to a surface defined by
slope inclination and slope orientation.
"""
nv = sg.normalvector(slope=stat.slp, aspect=stat.asp)
"""
Method 1: Computes the intensity according to the position of the sun (sunv) and
dotproduct normal vector to slope.
From corripio r package THIS IS GOOD
# consider october 31 29018 at 46/9 UTC=0
dt= datetime.datetime(2018, 10, 31, 12, 0)
In [471]: sg.to_jd(dt)
Out[471]: 2458423.0
sunv=sg.sunvector(jd=2458423 , latitude=46, longitude=9, timezone=0)
sp=sg.sunpos(sunv)
- sun is in the south:
In [456]: sp.azi
Out[456]: array([192.82694139])
- at quite low ele
In [455]: sp.sel
Out[455]: array([19.94907576])
# FLAT CASE
In [449]: nv = sg.normalvector(slope=0, aspect=0)
In [450]: np.dot(sunv ,np.transpose(nv))
Out[450]: array([[0.34118481]])
# SOOUTH SLOPE CASE = enhanced rad wrt. flat case
nv = sg.normalvector(slope=30, aspect=180)
In [446]: np.dot(sunv ,np.transpose(nv))
Out[446]: array([[0.75374406]])
# NORTH SLOPE CASE = self shaded
In [447]: nv = sg.normalvector(slope=30, aspect=0)
In [448]: np.dot(sunv ,np.transpose(nv))
Out[448]: array([[-0.16279463]])
"""
dotprod = np.dot(sunv, np.transpose(nv))
dprod = dotprod.squeeze()
dprod[dprod < 0] = 0 #negative indicates selfshading
self.dprod = dprod
"""Method 2: Illumination angles. Dozier and self shading""" # THIS IS WRONG
# eg consider south facting 30 degree slope on 31 oct 2018 at midday at 46,9 utc=0
# In [420]: sp.azi
# Out[420]: array([192.82694139])
# In [421]: stat.slp
# Out[421]: 30
# In [422]: stat.asp
# Out[422]: 180
# In [423]: sp.azi
# Out[423]: array([192.82694139])
# In [424]: sp.sel
# Out[424]: array([19.94907576])
# In [425]: ^I^I saz=sp.azi
# ...: ^I^I cosis=muz*np.cos(stat.slp)+np.sin(sp.zen)*np.sin(stat.slp)*np.cos(sp.azi-stat.asp)# cosine of illumination angle at subgrid.
# ...: ^I^I cosic=muz # cosine of illumination angle at grid (slope=0).
# ...: ^I^I cosi = (cosis/cosic)
# ...:
# In [427]: cosi
# Out[427]: array([-1.14169945])
# negative cosi shows sun below horizon!
# saz=sp.azi
# cosis=muz*np.cos(stat.slp)+np.sin(sp.zen)*np.sin(stat.slp)*np.cos(sp.azi-stat.asp)# cosine of illumination angle at subgrid.
# cosic=muz # cosine of illumination angle at grid (slope=0).
# cosi = (cosis/cosic)
# #If ratio of illumination angle subgrid/ illum angle grid is negative the point is selfshaded
# cosi[cosi<0]=0 #WRONG!!!
"""
CAST SHADOWS: SUN ELEVATION below hor.el set to 0 - binary mask
"""
selMask = sp.sel
selMask[selMask < horel] = 0
selMask[selMask > 0] = 1
self.selMask = selMask
"""
derive incident radiation on slope accounting for self shading and cast
shadow and solar geometry
BOTH formulations seem to be broken
"""
#self.SWfdirCor=selMask*(cosis/cosic)*self.SWfdir # this is really wrong!
#self.SWfdirCor=(cosis/cosic)*self.SWfdir
self.SWfdirCor = dprod * self.SWfdir * selMask # this is bad WHYYY?
#self.SWfdirCor=dprod*self.SWfdir
self.SWfglob = self.SWfdiff + self.SWfdirCor
#self.SWfglob = self.SWfdiff+ self.SWfdir
"""
def swin1D(pob, sob, tob, stat, dates, index):
# many arrays transposed
""" toposcale surface pressure using hypsometric equation - move to own
class
index: index of station array (numeric)
"""
g = 9.81
R = 287.05 # Gas constant for dry air.
tz = 0 # ERA5 is always utc0, used to compute sunvector
ztemp = pob.z[:, index, :].T
Ttemp = pob.t[:, index, :].T
statz = stat.ele[index] * g
dz = ztemp.T - statz # transpose not needed but now consistent with CGC surface pressure equations
psf = []
# loop through timesteps
#for i in range(starti,endi):
for i in range(0, dates.size):
# # find overlying layer
thisp = dz[:, i] == np.min(dz[:, i][dz[:, i] > 0])
# booleen indexing
T1 = Ttemp[i, thisp]
z1 = ztemp[i, thisp]
p1 = pob.levels[thisp] * 1e2 #Convert to Pa.
Tbar = np.mean([T1, tob.t[i, index]], axis=0)
""" Hypsometric equation."""
psf.append(p1 * np.exp((z1 - statz) * (g / (Tbar * R))))
psf = np.array(psf).squeeze()
## Specific humidity routine.
# mrvf=0.622.*vpf./(psf-vpf); #Mixing ratio for water vapor at subgrid.
# qf=mrvf./(1+mrvf); # Specific humidity at subgrid [kg/kg].
# fout.q(:,:,n)=qf;
""" Maybe follow Dubayah's approach (as in Rittger and Girotto) instead
for the shortwave downscaling, other than terrain effects. """
""" Height of the "grid" (coarse scale)"""
Zc = sob.z[:, index] # should this be a single value or vector?
""" toa """
SWtoa = sob.tisr[:, index]
""" Downwelling shortwave flux of the "grid" using nearest neighbor."""
SWc = sob.ssrd[:, index]
"""Calculate the clearness index."""
kt = SWc / SWtoa
#kt[is.na(kt)==T]<-0 # make sure 0/0 =0
#kt[is.infinite(kt)==T]<-0 # make sure 0/0 =0
kt[kt < 0] = 0
kt[kt > 1] = 0.8 #upper limit of kt
kt = kt
"""
Calculate the diffuse fraction following the regression of Ruiz-Arias 2010
"""
kd = 0.952 - 1.041 * np.exp(-1 * np.exp(2.3 - 4.702 * kt))
kd = kd
""" Use this to calculate the downwelling diffuse and direct shortwave radiation at grid. """
SWcdiff = kd * SWc
SWcdir = (1 - kd) * SWc
SWcdiff = SWcdiff
SWcdir = SWcdir
""" Use the above with the sky-view fraction to calculate the
downwelling diffuse shortwave radiation at subgrid. """
SWfdiff = stat.svf[index] * SWcdiff
SWfdiff = np.nan_to_num(SWfdiff) # convert nans (night) to 0
""" Direct shortwave routine, modified from Joel.
Get surface pressure at "grid" (coarse scale). Can remove this
part once surface pressure field is downloaded, or just check
for existance. """
ztemp = pob.z[:, index, :].T
Ttemp = pob.t[:, index, :].T
dz = ztemp.transpose() - sob.z[:, index]
psc = []
for i in range(0, dz.shape[1]):
#thisp.append(np.argmin(dz[:,i][dz[:,i]>0]))
thisp = dz[:, i] == np.min(dz[:, i][dz[:, i] > 0])
z0 = sob.z[i, index]
T0 = sob.t2m[i, index]
T1 = Ttemp[i, thisp]
z1 = ztemp[i, thisp]
p1 = pob.levels[thisp] * 1e2 #Convert to Pa.
Tbar = np.mean([T0, T1], axis=0)
""" Hypsometric equation."""
psc.append(p1 * np.exp((z1 - z0) * (g / (Tbar * R))))
psc = np.array(psc).squeeze()
#T1=Ttemp(thisp)
#z1=ztemp(thisp)
#p1=pob.levels(thisp)*1e2 #Convert to Pa.
#Tbar=mean([T0 T1])
"""compute julian dates"""
jd = sg.to_jd(dates)
"""
Calculates a unit vector in the direction of the sun from the observer
position.
"""
sunv = sg.sunvector(jd=jd,
latitude=stat.lat[index],
longitude=stat.lon[index],
timezone=tz)
"""
Computes azimuth , zenith and sun elevation
for each timestamp
"""
sp = sg.sunpos(sunv)
sp = sp
# Cosine of the zenith angle.
sp.zen = sp.zen
#sp.zen=sp.zen*(sp.zen>0) # Sun might be below the horizon.
muz = np.cos(sp.zen)
muz = muz
# NB! psc must be in Pa (NOT hPA!).
#if np.max(psc<1.5e3): # Obviously not in Pa
#psc=psc*1e2
# Calculate the "broadband" absorption coefficient. Elevation correction
# from Kris
ka = (g * muz / (psc)) * np.log(SWtoa / SWcdir)
#ka.set_fill_value(0)
#ka = ka.filled()
# set inf (from SWtoa/SWcdir at nigh, zero division) to 0 (night)
ka[ka == -np.inf] = 0
ka[ka == np.inf] = 0
# Note this equation is obtained by inverting Beer's law, i.e. use
#I_0=I_inf x exp[(-ka/mu) int_z0**inf rho dz]
# Along with hydrostatic equation to convert to pressure coordinates then
# solve for ka using p(z=inf)=0.
# Now you can (finally) find the direct component at subgrid.
SWfdir = SWtoa * np.exp(-ka * psf / (g * muz))
""" Then perform the terrain correction. [Corripio 2003 / rpackage insol port]."""
"""compute mean horizon elevation - why negative hor.el possible??? """
horel = (((np.arccos(np.sqrt(stat.svf[index])) * 180) / np.pi) *
2) - stat.slp[index]
if horel < 0:
horel = 0
meanhorel = horel
"""
normal vector - Calculates a unit vector normal to a surface defined by
slope inclination and slope orientation.
"""
nv = sg.normalvector(slope=stat.slp[index], aspect=stat.asp[index])
"""
Method 1: Computes the intensity according to the position of the sun (sunv) and
dotproduct normal vector to slope.
From corripio r package
"""
dotprod = np.dot(sunv, np.transpose(nv))
dprod = dotprod.squeeze()
dprod[dprod < 0] = 0 #negative indicates selfshading
dprod = dprod
"""Method 2: Illumination angles. Dozier"""
saz = sp.azi
cosis = muz * np.cos(stat.slp[index]) + np.sin(
sp.zen) * np.sin(stat.slp[index]) * np.cos(
sp.azi - stat.asp[index]
) # cosine of illumination angle at subgrid.
cosic = muz # cosine of illumination angle at grid (slope=0).
"""
SUN ELEVATION below hor.el set to 0 - binary mask
"""
selMask = sp.sel
selMask[selMask < horel] = 0
selMask[selMask > 0] = 1
selMask = selMask
"""
derive incident radiation on slope accounting for self shading and cast
shadow and solar geometry
BOTH formulations seem to be broken
"""
#SWfdirCor=selMask*(cosis/cosic)*SWfdir
SWfdirCor = selMask * dprod * SWfdir
SWfglob = SWfdiff + SWfdirCor
return SWfglob
"""
def swin(self, pob, sob, tob, stat, dates):
""" toposcale surface pressure using hypsometric equation - move to own
class
EDITS Jul 11 2019:
- removed elevation scaling as this degrade results - at least in WFJ test- Kris?
- reimplemnt original additative ele method - better than beers, or at least less damaging
- removed mask, why is this causing problems?
- dotprod method (corripio) does not seem to work - I dont think it ever did as we used SWTopo ==FALSE
- use Dozier cos corrction method (as in paper right)
- So ... we have ele, illumination angle, self shading and svf correction. We do not have horizon correction (partly in self shading of course)
"""
ztemp = pob.z
Ttemp = pob.t
statz = stat.ele * self.g
dz1 = ztemp - statz # transpose not needed but now consistent with CGC surface pressure equations
dz = dz1.transpose()
self.psf = []
# loop through timesteps
for i in range(0, dates.size):
# # find overlying layer
thisp = dz[:, i] == np.min(dz[:, i][dz[:, i] > 0])
# booleen indexing
T1 = Ttemp[i, thisp]
z1 = ztemp[i, thisp]
p1 = pob.levels[thisp] * 1e2 #Convert to Pa.
Tbar = np.mean([T1, tob.t[i]], axis=0)
""" Hypsometric equation."""
self.psf.append(p1 * np.exp(
(z1 - statz) * (self.g / (Tbar * self.R))))
self.psf = np.array(self.psf).squeeze()
## Specific humidity routine.
# mrvf=0.622.*vpf./(psf-vpf); #Mixing ratio for water vapor at subgrid.
# qf=mrvf./(1+mrvf); # Specific humidity at subgrid [kg/kg].
# fout.q(:,:,n)=qf;
""" Maybe follow Dubayah's approach (as in Rittger and Girotto) instead
for the shortwave downscaling, other than terrain effects. """
""" Height of the "grid" (coarse scale)"""
Zc = sob.z
""" toa """
SWtoa = sob.tisr
""" Downwelling shortwave flux of the "grid" using nearest neighbor."""
SWc = sob.ssrd
"""Calculate the clearness index."""
kt = SWc / SWtoa
#kt[is.na(kt)==T]<-0 # make sure 0/0 =0
#kt[is.infinite(kt)==T]<-0 # make sure 0/0 =0
kt[kt < 0] = 0
kt[kt > 1] = 0.8 #upper limit of kt
self.kt = kt
"""
Calculate the diffuse fraction following the regression of Ruiz-Arias 2010
"""
kd = 0.952 - 1.041 * np.exp(-1 * np.exp(2.3 - 4.702 * kt))
self.kd = kd
""" Use this to calculate the downwelling diffuse and direct shortwave radiation at grid. """
SWcdiff = kd * SWc
SWcdir = (1 - kd) * SWc
self.SWcdiff = SWcdiff
self.SWcdir = SWcdir
""" Use the above with the sky-view fraction to calculate the
downwelling diffuse shortwave radiation at subgrid. """
self.SWfdiff = stat.svf * SWcdiff
self.SWfdiff.set_fill_value(0)
self.SWfdiff = self.SWfdiff.filled()
""" Direct shortwave routine, modified from Joel.
Get surface pressure at "grid" (coarse scale). Can remove this
part once surface pressure field is downloaded, or just check
for existance. """
ztemp = pob.z
Ttemp = pob.t
dz = ztemp.transpose() - sob.z
self.psc = []
for i in range(0, dz.shape[1]):
#thisp.append(np.argmin(dz[:,i][dz[:,i]>0]))
thisp = dz[:, i] == np.min(dz[:, i][dz[:, i] > 0])
z0 = sob.z[i]
T0 = sob.t2m[i]
T1 = Ttemp[i, thisp]
z1 = ztemp[i, thisp]
p1 = pob.levels[thisp] * 1e2 #Convert to Pa.
Tbar = np.mean([T0, T1], axis=0)
""" Hypsometric equation."""
self.psc.append(p1 * np.exp(
(z1 - z0) * (self.g / (Tbar * self.R))))
self.psc = np.array(self.psc).squeeze()
#T1=Ttemp(thisp)
#z1=ztemp(thisp)
#p1=pob.levels(thisp)*1e2 #Convert to Pa.
#Tbar=mean([T0 T1])
"""compute julian dates"""
jd = sg.to_jd(dates)
"""
Calculates a unit vector in the direction of the sun from the observer
position.
"""
sunv = sg.sunvector(jd=jd,
latitude=stat.lat,
longitude=stat.lon,
timezone=stat.tz)
"""
Computes azimuth , zenith and sun elevation
for each timestamp
"""
sp = sg.sunpos(sunv)
self.sp = sp
# Cosine of the zenith angle.
sp.zen = sp.zen
#sp.zen=sp.zen*(sp.zen>0) # Sun might be below the horizon.
muz = np.cos(sp.zen)
self.muz = muz
# NB! psc must be in Pa (NOT hPA!).
#if np.max(psc<1.5e3): # Obviously not in Pa
#psc=psc*1e2
# Calculate the "broadband" absorption coefficient. Elevation correction
# from Kris
ka = (self.g * muz / (self.psc)) * np.log(SWtoa / SWcdir)
#ka.set_fill_value(0)
#ka = ka.filled()
# set inf (from SWtoa/SWcdir at nigh, zero division) to 0 (night)
ka[ka == -np.inf] = 0
ka[ka == np.inf] = 0
# Note this equation is obtained by inverting Beer's law, i.e. use
#I_0=I_inf x exp[(-ka/mu) int_z0**inf rho dz]
# Along with hydrostatic equation to convert to pressure coordinates then
# solve for ka using p(z=inf)=0.
# Method 1 Beers law
# Now you can (finally) find the direct component at subgrid.
self.SWfdir = SWtoa * np.exp(-ka * self.psf / (self.g * muz))
#self.SWfdir=SWcdir
# Method 2 ORIGINAL ELEVATION Correction
"""ele diff in km"""
coarseZ = Zc[0] / 9.9
dz = (stat.ele - coarseZ) / 1000 # fine - coase in knm
s = SWtoa
b = SWcdir
zen = sp.zen
thetaz = (np.pi / 180) * zen #radians
m = 1 / np.cos(thetaz)
k = -np.log(b / s) / m
k.set_fill_value(0)
k = k.filled()
#k[is.na(k)==T]<-0 #correct b=0/s=0 problem
t = np.exp(1)**(-k * dz * np.cos(thetaz))
t[t > 1] < -1.1 #to constrain to reasonable values
t[t < 0.8] < -0.9 #to constrain to reasonable values
db = (1 - t) * SWcdir
#self.SWfdir=SWcdir+db #additative correction
#======================================
""" Then perform the terrain correction. [Corripio 2003 / rpackage insol port]."""
"""compute mean horizon elevation - why negative hor.el possible??? """
horel = (((np.arccos(np.sqrt(stat.svf)) * 180) / np.pi) * 2) - stat.slp
if horel < 0:
horel = 0
self.meanhorel = horel
"""
normal vector - Calculates a unit vector normal to a surface defined by
slope inclination and slope orientation.
"""
nv = sg.normalvector(slope=stat.slp, aspect=stat.asp)
"""
Method 1: Computes the intensity according to the position of the sun (sunv) and
dotproduct normal vector to slope.
From corripio r package REMOVE THIS
"""
dotprod = np.dot(sunv, np.transpose(nv))
dprod = dotprod.squeeze()
dprod[dprod < 0] = 0 #negative indicates selfshading
self.dprod = dprod
"""Method 2: Illumination angles. Dozier and self shading"""
saz = sp.azi
cosis = muz * np.cos(
stat.slp) + np.sin(sp.zen) * np.sin(stat.slp) * np.cos(
sp.azi - stat.asp) # cosine of illumination angle at subgrid.
cosic = muz # cosine of illumination angle at grid (slope=0).
cosi = (cosis / cosic)
#If ratio of illumination angle subgrid/ illum angle grid is negative the point is selfshaded
cosi[cosi < 0] = 0
"""
CAST SHADOWS: SUN ELEVATION below hor.el set to 0 - binary mask
"""
selMask = sp.sel
selMask[selMask < horel] = 0
selMask[selMask > 0] = 1
self.selMask = selMask
"""
derive incident radiation on slope accounting for self shading and cast
shadow and solar geometry
BOTH formulations seem to be broken
"""
#self.SWfdirCor=selMask*(cosis/cosic)*self.SWfdir # this is really wrong!
self.SWfdirCor = (cosis / cosic) * self.SWfdir
self.SWfdirCor = selMask * dprod * self.SWfdir # this is bad
#self.SWfdirCor=dprod*self.SWfdir
self.SWfglob = self.SWfdiff + self.SWfdirCor
#self.SWfglob = self.SWfdiff+ self.SWfdir
"""