class Growth_Monod_Eppley_Steele:
resource = phydra.variable(foreign=True, flux='growth', negative=True)
consumer = phydra.variable(foreign=True, flux='growth', negative=False)
Temp = phydra.forcing(foreign=True, description='Temperature forcing')
Light = phydra.forcing(foreign=True, description='Light forcing')
MLD = phydra.forcing(foreign=True, description='Mixed Layer Depth forcing')
halfsat = phydra.parameter(description='monod half-saturation constant')
eppley = phydra.parameter(description='eppley exponent')
i_opt = phydra.parameter(description='Optimal irradiance of consumer')
µ_max = phydra.parameter(description='maximum growth rate')
kw = phydra.parameter(description='light attenuation coef for water')
kc = phydra.parameter(description='light attenuation coef for consumer')
@phydra.flux
def growth(self, resource, consumer, Temp, Light, MLD, i_opt, kw, kc, eppley, halfsat, µ_max):
temp_lim = self.m.exp(eppley * Temp)
monod_lim = resource / (resource + halfsat)
kPAR = kw + kc * consumer
light_lim = 1. / (kPAR * MLD) * (
-self.m.exp(1. - Light / i_opt) - (
-self.m.exp((1. - (Light * self.m.exp(-kPAR * MLD)) / i_opt))))
return µ_max * temp_lim * monod_lim * light_lim * consumer
class Mixing_K:
""" pre-computes K to be used in all mixing processes """
mld = phydra.forcing(foreign=True)
mld_deriv = phydra.forcing(foreign=True)
kappa = phydra.parameter(description='constant mixing coefficient')
@phydra.flux(group='mixing_K')
def mixing(self, mld, mld_deriv, kappa):
return (self.m.max(mld_deriv, 0) + kappa) / mld
class GlobalSlabClimatologyForcing:
forcing = phydra.forcing(foreign=False, setup_func='forcing_setup')
dataset = phydra.parameter(description="Options: 'n0x', 'mld', 'tmld', 'par'")
lat = phydra.parameter(description='constant value of forcing')
lon = phydra.parameter(description='constant value of forcing')
rbb = phydra.parameter(description='constant value of forcing')
smooth = phydra.parameter(description='smoothing conditions, larger values = stronger smoothing')
k = phydra.parameter(description='The degree of the spline fit')
deriv = phydra.parameter(description='order of derivative to store, for basic forcing pass 0')
def forcing_setup(self, dataset, lat, lon, rbb, smooth, k, deriv):
data = ClimatologyForcing(lat, lon, rbb, dataset).outForcing
dayspermonth = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
dpm = dayspermonth
dpm_cumsum = np.cumsum(dpm) - np.array(dpm) / 2
time = np.concatenate([[0], dpm_cumsum, [365]], axis=None)
boundary_int = [(data[0] + data[-1]) / 2]
dat = np.concatenate([boundary_int, data, boundary_int], axis=None)
spl = intrp.splrep(time, dat, per=True, k=k, s=smooth)
def forcing(time):
return intrp.splev(np.mod(time, 365), spl, der=deriv)
return forcing
class SlabSinking:
""" """
var = phydra.variable(foreign=True, flux='sinking', negative=True)
mld = phydra.forcing(foreign=True)
rate = phydra.parameter(description='sinking rate, units: m d^-1')
@phydra.flux
def sinking(self, var, rate, mld):
return var * rate / mld
class Eppley_ML:
""" """
temp = phydra.forcing(foreign=True, description='Temperature forcing')
eppley_exp = phydra.parameter(description='eppley exponent')
@phydra.flux(group='growth_lims')
def eppley_growth(self, temp, eppley_exp):
return self.m.exp(eppley_exp * temp)
class SlabUpwelling:
""" """
n = phydra.variable(foreign=True,
flux='mixing',
description='nutrient mixed into system')
n_0 = phydra.forcing(
foreign=True,
description='nutrient concentration below mixed layer depth')
mld = phydra.forcing(foreign=True)
mld_deriv = phydra.forcing(foreign=True)
kappa = phydra.parameter(description='constant mixing coefficient')
@phydra.flux
def mixing(self, n, n_0, mld, mld_deriv, kappa):
""" compute function of on_demand xarray variable
specific flux needs to be implemented in BaseFlux """
return (n_0 - n) * (self.m.max(mld_deriv, 0) + kappa) / mld
class Steele_ML:
""" """
pigment_biomass = phydra.variable(foreign=True)
i_0 = phydra.forcing(foreign=True, description='Light forcing')
mld = phydra.forcing(foreign=True, description='Mixed Layer Depth forcing')
i_opt = phydra.parameter(description='Optimal irradiance of consumer')
kw = phydra.parameter(description='light attenuation coef for water')
kc = phydra.parameter(
description='light attenuation coef for pigment biomass')
@phydra.flux(group='growth_lims')
def steele_light_lim(self, i_0, mld, pigment_biomass, i_opt, kw, kc):
kPAR = kw + kc * pigment_biomass
light_lim = 1. / (kPAR *
mld) * (-self.m.exp(1. - i_0 / i_opt) - (-self.m.exp(
(1. - (i_0 * self.m.exp(-kPAR * mld)) / i_opt))))
return light_lim
class SlabMixing:
""" """
vars_sink = phydra.variable(foreign=True,
negative=True,
flux='mixing',
list_input=True,
dims='sinking_vars',
description='list of variables affected')
mld = phydra.forcing(foreign=True)
mld_deriv = phydra.forcing(foreign=True)
kappa = phydra.parameter(description='constant mixing coefficient')
@phydra.flux(dims='sinking_vars_full')
def mixing(self, vars_sink, mld, mld_deriv, kappa):
""" compute function of on_demand xarray variable
specific flux needs to be implemented in BaseFlux """
return vars_sink * (self.m.max(mld_deriv, 0) + kappa) / mld
class EMPOWER_Eppley_ML:
""" """
temp = phydra.forcing(foreign=True, description='Temperature forcing')
VpMax = phydra.parameter(
description='Maximum photosynthetic rate at 0 degrees celcius')
@phydra.flux(group='VpT')
def temp_dependence(self, temp, VpMax):
return VpMax * 1.066**temp
class IrradianceFromNoonPAR:
""" Calculate I0 at surface from noon PAR
TODO: DOESNT WORK WITH FOREIGN FORCING YET! NEED TO FIX IN XARRAY SIMLAB ODE """
NoonPAR = phydra.forcing(foreign=True)
I0 = phydra.forcing(setup_func='calculate_I0', description='calculated irradiance for latitude',
attrs={'unit': 'W m^-2'})
station = phydra.parameter(description="name of station, options: 'india', 'biotrans', 'kerfix', 'papa'")
def calculate_I0(self, station, NoonPAR):
def day_length_calc(jday, latradians):
""" Function to calculate day length for location """
declin = 23.45 * np.sin(2 * np.pi * (284 + jday) * 0.00274) * np.pi / 180 # solar declination angle
daylnow = 2 * np.arccos(-1 * np.tan(latradians) * np.tan(declin)) * 12 / np.pi # day length
return daylnow
if station == 'india':
latitude = 60.0 # latitude, degrees
elif station == 'biotrans':
latitude = 47.0
elif station == 'kerfix':
latitude = -50.67
elif station == 'papa':
latitude = 50.0
else:
raise ValueError("station label not found, options: 'india', 'biotrans', 'kerfix', 'papa'")
latradians = latitude * np.pi / 180.
def return_PAR_forcing(time):
day_length = day_length_calc(time, latradians)
print("day_len", day_length)
noonpar = NoonPAR
print("NOON PAR", noonpar)
return noonpar * day_length * np.sin(2 / np.pi) # sinusoidal integration
# return noonpar * day_length / 2 # trapezoidal integration
return return_PAR_forcing
class SinusoidalForcing:
forcing = phydra.forcing(foreign=False, setup_func='forcing_setup')
period = phydra.parameter(description='period of sinusoidal forcing')
def forcing_setup(self, period):
cwd = os.getcwd()
print("forcing function is in directory:", cwd)
@np.vectorize
def forcing(time):
return np.cos(time / period * 2 * np.pi) + 1
return forcing
class LinearForcingInput:
var = phydra.variable(foreign=True,
flux='input',
negative=False,
description='variable affected by flux')
forcing = phydra.forcing(foreign=True,
description='forcing affecting flux')
rate = phydra.parameter(description='linear rate of change')
@phydra.flux
def input(self, var, forcing, rate):
""" """
return forcing * rate
class NoonPARfromLat:
""" Component that calculates Photosynthetically Active Radiation (PAR) from Latitude"""
NoonPAR = phydra.forcing(setup_func='calcNoonPAR', description='calculated PAR from Irradiance',
attrs={'unit': 'W m^-2'})
station = phydra.parameter(description="name of station, options: 'india', 'biotrans', 'kerfix', 'papa'")
def calcNoonPAR(self, station):
""" Function adapted from EMPOWER model (Anderson et al. 2015)"""
def noon_PAR_calc(jday, latradians, clouds, e0):
albedo = 0.04 # albedo
solarconst = 1368.0 # solar constant, w m-2
parrac = 0.43 # PAR fraction
declin = 23.45 * np.sin(2 * np.pi * (284 + jday) * 0.00274) * np.pi / 180 # solar declination angle
coszen = np.sin(latradians) * np.sin(declin) + np.cos(latradians) * np.cos(declin) # cosine of zenith angle
zen = np.arccos(coszen) * 180 / np.pi # zenith angle, degrees
Rvector = 1 / np.sqrt(1 + 0.033 * np.cos(2 * np.pi * jday * 0.00274)) # Earth's radius vector
Iclear = solarconst * coszen ** 2 / (Rvector ** 2) / (
1.2 * coszen + e0 * (1.0 + coszen) * 0.001 + 0.0455) # irradiance at ocean surface, clear sky
cfac = (1 - 0.62 * clouds * 0.125 + 0.0019 * (90 - zen)) # cloud factor (atmospheric transmission)
Inoon = Iclear * cfac * (1 - albedo) # noon irradiance: total solar
noonparnow = parrac * Inoon
return noonparnow
if station == 'india':
latitude = 60.0 # latitude, degrees
clouds = 6.0 # cloud fraction, oktas
e0 = 12.0 # atmospheric vapour pressure
elif station == 'biotrans':
latitude = 47.0
clouds = 6.0
e0 = 12.0
elif station == 'kerfix':
latitude = -50.67
clouds = 6.0
e0 = 12.0
elif station == 'papa':
latitude = 50.0
clouds = 6.0
e0 = 12.0
else:
raise ValueError("station label not found, options: 'india', 'biotrans', 'kerfix', 'papa'")
latradians = latitude * np.pi / 180.
def return_noonPAR(time):
return noon_PAR_calc(time, latradians, clouds, e0)
return return_noonPAR
class EMPOWER_Smith_ML:
""" """
pigment_biomass = phydra.variable(foreign=True)
i_0 = phydra.forcing(foreign=True, description='Light forcing')
mld = phydra.forcing(foreign=True, description='Mixed Layer Depth forcing')
alpha = phydra.parameter(description='initial slop of PI curve')
CtoChl = phydra.parameter(description='chlorophyll to carbon ratio')
kw = phydra.parameter(description='light attenuation coef for water')
kc = phydra.parameter(
description='light attenuation coef for pigment biomass')
@phydra.flux(group_to_arg='VpT', group='growth_lims')
def smith_light_lim(self, i_0, mld, pigment_biomass, alpha, VpT, kw, kc,
CtoChl):
kPAR = kw + kc * pigment_biomass
i_0 = i_0 / 24 # from per day to per h
x_0 = alpha * i_0 # * self.m.exp(- kPAR * 0) # (== 1)
x_H = alpha * i_0 * self.m.exp(-kPAR * mld)
VpH = VpT / kPAR / mld * (self.m.log(x_0 + (VpT**2 + x_0**2)**0.5) -
self.m.log(x_H + (VpT**2 + x_H**2)**0.5))
return VpH * 24 / CtoChl
class SlabUpwelling_KfromGroup:
""" """
n = phydra.variable(foreign=True,
flux='mixing',
description='nutrient mixed into system')
n_0 = phydra.forcing(
foreign=True,
description='nutrient concentration below mixed layer depth')
@phydra.flux(group_to_arg='mixing_K')
def mixing(self, n, n_0, mixing_K):
""" compute function of on_demand xarray variable
specific flux needs to be implemented in BaseFlux """
return (n_0 - n) * mixing_K
class ConstantForcing:
forcing = phydra.forcing(foreign=False, setup_func='forcing_setup')
value = phydra.parameter(description='constant value of forcing')
def forcing_setup(self, value):
cwd = os.getcwd()
print("forcing function is in directory:", cwd)
print("forcing_val:", value)
@np.vectorize
def forcing(time):
return value
return forcing
class Smith_ML:
""" """
pigment_biomass = phydra.variable(foreign=True)
i_0 = phydra.forcing(foreign=True, description='Light forcing')
mld = phydra.forcing(foreign=True, description='Mixed Layer Depth forcing')
alpha = phydra.parameter(description='initial slop of PI curve')
VpMax = phydra.parameter(description='Maximum photosynthetic rate')
kw = phydra.parameter(description='light attenuation coef for water')
kc = phydra.parameter(
description='light attenuation coef for pigment biomass')
@phydra.flux(group='growth_lims')
def smith_light_lim(self, i_0, mld, pigment_biomass, alpha, VpMax, kw, kc):
kPAR = kw + kc * pigment_biomass
x_0 = alpha * i_0 # * self.m.exp(- kPAR * 0) # (== 1)
x_H = alpha * i_0 * self.m.exp(-kPAR * mld)
VpH = (VpMax / (kPAR * mld)) * \
self.m.log(
(x_0 + self.m.sqrt(VpMax ** 2 + x_0 ** 2)) /
(x_H + self.m.sqrt(VpMax ** 2 + x_H ** 2))
)
return VpH
class EMPOWER_Anderson_Light_ML:
""" """
pigment_biomass = phydra.variable(foreign=True)
i_0 = phydra.forcing(foreign=True, description='Light forcing')
mld = phydra.forcing(foreign=True, description='Mixed Layer Depth forcing')
alpha = phydra.parameter(description='initial slop of PI curve')
CtoChl = phydra.parameter(description='chlorophyll to carbon ratio')
kw = phydra.parameter(description='light attenuation coef for water')
kc = phydra.parameter(
description='light attenuation coef for pigment biomass')
@phydra.flux(group_to_arg='VpT', group='growth_lims')
def irradiance_out(self, i_0, mld, pigment_biomass, alpha, VpT, kw, kc,
CtoChl):
""" """
i_0 = i_0 / 24
chl = pigment_biomass * 6.625 * 12.0 / CtoChl # chlorophyll, mg m-3 (Redfield ratio of 6.625 mol C mol N-1 assumed for C:N of phytoplankton)
ss = chl**0.5 # square root of chlorophyll
kPAR_1 = 0.13096 + 0.030969 * ss + 0.042644 * ss**2 - 0.013738 * ss**3 + 0.0024617 * ss**4 - 0.00018059 * ss**5
kPAR_2 = 0.041025 + 0.036211 * ss + 0.062297 * ss**2 - 0.030098 * ss**3 + 0.0062597 * ss**4 - 0.00051944 * ss**5
kPAR_3 = 0.021517 + 0.050150 * ss + 0.058900 * ss**2 - 0.040539 * ss**3 + 0.0087586 * ss**4 - 0.00049476 * ss**5
kPAR = [kPAR_1, kPAR_2, kPAR_3]
if mld <= 5.0:
jnlay = 1
zbase = [0., mld]
zdep = [0., mld]
I_1 = i_0 * np.exp(-kPAR_1 * mld)
Ibase = [i_0, I_1]
elif mld > 5.0 and mld <= 23.0:
jnlay = 2
zbase = [0., 5.0, mld]
zdep = [0., 5.0, mld - 5.0]
mld_rest = mld - 5.0
I_1 = i_0 * np.exp(-kPAR_1 * 5.0)
I_2 = I_1 * np.exp(-kPAR_2 * mld - 5.0)
Ibase = [i_0, I_1, I_2]
elif mld > 23.0:
jnlay = 3
zbase = [0., 5.0, 23.0, mld]
zdep = [0., 5.0, 23.0 - 5.0, mld - 23.0]
I_1 = i_0 * np.exp(-kPAR_1 * 5.0)
I_2 = I_1 * np.exp(-kPAR_2 * 23.0 - 5.0)
I_3 = I_2 * np.exp(-kPAR_3 * mld - 23.0)
Ibase = [i_0, I_1, I_2, I_3]
aphybase = [0, 0, 0, 0]
aphybase[
0] = 0.36796 + 0.17537 * ss - 0.065276 * ss**2 + 0.013528 * ss**3 - 0.0011108 * ss**4 # a (chl absorption) at ocean surface as function chl
L_Isum = 0
for ilay in range(1, jnlay + 1):
L_I = self.SmithFunc(zdep[ilay], Ibase[ilay - 1], Ibase[ilay],
kPAR[ilay - 1], alpha, VpT)
L_I = L_I * 24 / CtoChl # / VpT
L_Isum = L_Isum + L_I * zdep[ilay]
# for ilay in range(1, jnlay+1):
# ahash = self.FNaphy(ss,zbase[ilay-1],zbase[ilay],aphybase[ilay-1]) # change in a with depth
# aphybase[ilay] = aphybase[ilay-1]+ahash # a at base of layer
# aphyav = aphybase[ilay-1]+ahash*0.5 # average a in layer (from which alpha is calculated: alpha = a*alphamax
# L_I = self.FNLIcalcA93(zdep[ilay],Ibase[ilay-1],Ibase[ilay],kPAR[ilay-1],alpha,VpT,aphyav)
# L_Isum = L_Isum + L_I*zdep[ilay] # multiply by layer depth in order to set up weighted average for total mixed layer
L_I = L_Isum / mld
return L_I
def SmithFunc(self, zdepth, Iin, Iout, kPARlay, alpha, Vp):
x0 = alpha * Iin
xH = alpha * Iout # *np.exp(-kPARlay*zdepth)
VpH = Vp / kPARlay / zdepth * (np.log(x0 + (Vp**2 + x0**2)**0.5) -
np.log(xH + (Vp**2 + xH**2)**0.5))
return VpH
def FNaphy(self, ss, ztop, zbottom, aphylast):
"""
FNaphy calculates values of a (chlorophyll abosorption) for each layer;
alpha[layer] is then calculated as a[layer]*alphamax
"""
g = [
0.048014, 0.00023779, -0.023074, 0.0031095, -0.0090545, 0.0027974,
0.00085217, -3.9804E-06, 0.0012398, -0.00061991
] # coeffs. for calculating a#
x = zbottom + 1.0
xlg = np.log(x)
termf1a = x * xlg - x
termf2a = x * xlg**2 - 2 * x * xlg + 2 * x
termf3a = x * xlg**3 - 3 * x * xlg**2 + 6 * x * xlg - 6 * x
x = ztop + 1.0
xlg = np.log(x)
termf1b = x * xlg - x
termf2b = x * xlg**2 - 2 * x * xlg + 2 * x
termf3b = x * xlg**3 - 3 * x * xlg**2 + 6 * x * xlg - 6 * x
terma = g[0] + g[1] * ss + g[4] * ss**2 + g[6] * ss**3
termb = g[2] + g[3] * ss + g[8] * ss**2
termc = g[5] + g[9] * ss
acalc = (zbottom + 1.0) * terma + termf1a * termb + \
termf2a * termc + termf3a * g[7] - ((ztop + 1.0) * terma + \
termf1b * termb + termf2b * termc + termf3b * g[7])
return acalc
def FNLIcalcA93(self, zdepth, Iin, Iout, kPARlay, alpha, Vp, ahashnow):
"""
# Photosynthesis calculated using polynomial approximation (Anderson, 1993)
"""
omeg = [
1.9004, -2.8333E-01, 2.8050E-02, -1.4729E-03, 3.0841E-05
] # polynomial coefficients for calculating photosynthesis (Platt et al., 1990)
# Calculate alphamax
alphamax = alpha * 2.602 # alphamax is alpha at wavelength of maximum absorption cross section
# Calculate daily photosynthesis in each layer, mg C m-2 d-1
V0 = Vp / (np.pi * kPARlay)
V1 = alphamax * ahashnow * Iin / Vp
V2 = alphamax * ahashnow * Iout / Vp
Qpsnow = V0 * (omeg[0] * (V1 - V2) + omeg[1] *
(V1**2 - V2**2) + omeg[2] * (V1**3 - V2**3) + omeg[3] *
(V1**4 - V2**4) + omeg[4] * (V1**5 - V2**5))
# convert to dimensionless units to get 0 <= J <= 1
Lim_I = Qpsnow / zdepth / Vp # /24.
return Lim_I
class EMPOWER_IrradianceFromLat:
""" Component that calculates daily irradiance from latitude of station """
I0 = phydra.forcing(setup_func='calculate_I0', description='calculated irradiance for latitude',
attrs={'unit': 'W m^-2'})
station = phydra.parameter(description="name of station, options: 'india', 'biotrans', 'kerfix', 'papa'")
def calculate_I0(self, station):
""" Function adapted from EMPOWER model (Anderson et al. 2015)"""
def day_length_calc(jday, latradians):
""" Function to calculate day length for location """
declin = 23.45 * np.sin(2 * np.pi * (284 + jday) * 0.00274) * np.pi / 180 # solar declination angle
daylnow = 2 * np.arccos(-1 * np.tan(latradians) * np.tan(declin)) * 12 / np.pi # day length
return daylnow
def noon_PAR_calc(jday, latradians, clouds, e0):
albedo = 0.04 # albedo
solarconst = 1368.0 # solar constant, w m-2
parrac = 0.43 # PAR fraction
declin = 23.45 * np.sin(2 * np.pi * (284 + jday) * 0.00274) * np.pi / 180 # solar declination angle
coszen = np.sin(latradians) * np.sin(declin) + np.cos(latradians) * np.cos(declin) # cosine of zenith angle
zen = np.arccos(coszen) * 180 / np.pi # zenith angle, degrees
Rvector = 1 / np.sqrt(1 + 0.033 * np.cos(2 * np.pi * jday * 0.00274)) # Earth's radius vector
Iclear = solarconst * coszen ** 2 / (Rvector ** 2) / (
1.2 * coszen + e0 * (1.0 + coszen) * 0.001 + 0.0455) # irradiance at ocean surface, clear sky
cfac = (1 - 0.62 * clouds * 0.125 + 0.0019 * (90 - zen)) # cloud factor (atmospheric transmission)
Inoon = Iclear * cfac * (1 - albedo) # noon irradiance: total solar
noonparnow = parrac * Inoon
return noonparnow
if station == 'india':
latitude = 60.0 # latitude, degrees
clouds = 6.0 # cloud fraction, oktas
e0 = 12.0 # atmospheric vapour pressure
elif station == 'biotrans':
latitude = 47.0
clouds = 6.0
e0 = 12.0
elif station == 'kerfix':
latitude = -50.67
clouds = 6.0
e0 = 12.0
elif station == 'papa':
latitude = 50.0
clouds = 6.0
e0 = 12.0
else:
raise ValueError("station label not found, options: 'india', 'biotrans', 'kerfix', 'papa'")
latradians = latitude * np.pi / 180.
def return_PAR_forcing(time):
day_length = day_length_calc(time, latradians)
# print("day_len", day_length)
noonpar = noon_PAR_calc(time, latradians, clouds, e0)
return noonpar * day_length * np.sin(2 / np.pi) # sinusoidal integration
# return noonpar * day_length / 2 # trapezoidal integration
return return_PAR_forcing
class EMPOWER_ForcingFromFile:
""" """
MLD = phydra.forcing(setup_func='create_MLD_forcing', description='Empower MLD Forcing')
MLDderiv = phydra.forcing(setup_func='create_MLD_deriv_forcing', description='Empower MLDderiv Forcing')
SST = phydra.forcing(setup_func='create_SST_forcing', description='Empower SST Forcing')
N0 = phydra.forcing(setup_func='create_N0_forcing', description='Empower N0 Forcing')
station = phydra.parameter(description="name of station, options: 'india', 'biotrans', 'kerfix', 'papa'")
def read_intrp_forcing(self, station, data, k, smooth, deriv):
""" """
stations_dict = {'india': {'MLD': 'MLD_India', 'SST': 'SST_India'},
'biotrans': {'MLD': 'MLD_Biotrans', 'SST': 'SST_Biotrans'},
'kerfix': {'MLD': 'MLD_Kerfix', 'SST': 'SST_Kerfix'},
'papa': {'MLD': 'MLD_Papa', 'SST': 'SST_Papa'}}
all_forcings = pandas.read_csv("stations_forcing.txt", sep=r'\s*,\s*',
header=0, encoding='ascii', engine='python')
station_data = all_forcings[stations_dict[station][data]].values[:-1]
dayspermonth = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
dpm = dayspermonth
dpm_cumsum = np.cumsum(dpm) - np.array(dpm) / 2
time = np.concatenate([[0], dpm_cumsum, [365]], axis=None)
boundary_int = [(station_data[0] + station_data[-1]) / 2]
dat = np.concatenate([boundary_int, station_data, boundary_int], axis=None)
spl = intrp.splrep(time, dat, per=True, k=k, s=smooth)
def forcing(time):
return intrp.splev(np.mod(time, 365), spl, der=deriv)
return forcing
def create_MLD_forcing(self, station):
return self.read_intrp_forcing(station, 'MLD', deriv=0, k=1, smooth=1)
def create_MLD_deriv_forcing(self, station):
return self.read_intrp_forcing(station, 'MLD', deriv=1, k=1, smooth=1)
def create_SST_forcing(self, station):
return self.read_intrp_forcing(station, 'SST', deriv=0, k=1, smooth=1)
def create_N0_forcing(self, station):
MLD_func = self.read_intrp_forcing(station, 'MLD', deriv=0, k=1, smooth=1)
if station == 'india':
aN = 0.0074 # coeff. for N0 as fn depth
bN = 10.85 # coeff. for N0 as fn depth
elif station == 'biotrans':
aN = 0.0174
bN = 4.0
elif station == 'kerfix':
aN = 0
bN = 26.1
elif station == 'papa':
aN = 0.0
bN = 14.6
else:
raise ValueError("station label not found, options: 'india', 'biotrans', 'kerfix', 'papa'")
def N0_forcing(time):
return aN * MLD_func(time) + bN
return N0_forcing