def get_SUMsalinity(year, month, product_n = 3):
# 全球での塩分の総和を求める。
# 単位はkg。
rho0 = 1024.0 # 海水の密度はこれで統一。
_, title_name, _ = product_n_to_name(product_n)
s = get_data(year, month, 's', 0, title_name)
xgrid, ygrid, zgrid = product_grid_info('w', 'data', product_n)
xn = xgrid.size
yn = ygrid.size
zn = zgrid.size
ew_dist = zeros([yn, xn])
ns_dist = zeros([yn, xn])
S = zeros([yn, xn])
for i in range(0, xn):
for j in range(0, yn):
ew_dist[j, i] = dist_on_sphere([0, ygrid[j]], [1, ygrid[j]])
ns_dist[j, i] = dist_on_sphere([0, ygrid[j] - 0.5], [0, ygrid[j] + 0.5])
if isnan(s[j, i, 0]) == True:
S[j, i] = 0.0
else:
for k in range(0, zn):
if k == 0:
dH = zgrid[k]
else:
dH = zgrid[k] - zgrid[k - 1]
if isnan(s[j, i, k]) == False:
S[j, i] = S[j, i] + dH * ew_dist[j, i] * ns_dist[j, i] * rho0 * (s[j, i, k]/1000)
else:
break
return sum(S)
def get_Gradient_of_variable_from_data(data, var, product_n = 3): xgrid, ygrid, zgrid = D.get_grid_value(var, product_n) xn = xgrid.size yn = ygrid.size zn = zgrid.size Data_ns = np.zeros((yn, xn, zn)) Data_ew = np.zeros((yn, xn, zn)) ns_dist = subroutine.dist_on_sphere([0.0, - 0.5], [0.0, 0.5]) for j in range(0, yn): if j == 0 or j == yn - 1: Data_ns[j, :, :] = np.nan Data_ew[j, :, :] = np.nan else: y0 = j - 1 y1 = j y2 = j + 1 lat1 = ygrid[y1] ew_dist = subroutine.dist_on_sphere([0.0, lat1], [1.0, lat1]) for i in range(0, xn): x1 = i lon1 = xgrid[x1] if i == 0: x0 = xn - 1 x2 = i + 1 elif i == xn - 1: x0 = i - 1 x2 = xn - 1 else: x0 = i - 1 x2 = i + 1 data01 = data[y1, x0, :] data10 = data[y0, x1, :] data11 = data[y1, x1, :] data21 = data[y1, x2, :] data12 = data[y2, x1, :] if np.isnan(data01[0]) == True and np.isnan(data21[0]) == False: Data_ew[j, i, :] = (data21 - data11) / ew_dist elif np.isnan(data21[0]) == True and np.isnan(data01[0]) == False: Data_ew[j, i, :] = (data11 - data01) / ew_dist else: Data_ew[j, i, :] = (data21 - data01) / (2.0 * ew_dist) if np.isnan(data10[0]) == True and np.isnan(data12[0]) == False: Data_ns[j, i, :] = (data12 - data11) / ns_dist elif np.isnan(data12[0]) == True and np.isnan(data10[0]) == False: Data_ns[j, i, :] = (data11 - data10) / ns_dist else: Data_ns[j, i, :] = (data12 - data10) / (2.0 * ns_dist) return Data_ew, Data_ns
def cal_GeostrophicCurrent_from_SSH(ssh, product_n = 3):
ns_dist = subroutine.dist_on_sphere([0.0, - 0.5], [0.0, 0.5])
xgrid, ygrid, zgrid = D.get_grid_value('ht', product_n)
yn = ygrid.size
xn = xgrid.size
Ug = np.zeros((yn, xn))
Vg = np.zeros((yn, xn))
xgrid_new = np.zeros(xn)
ygrid_new = np.zeros(yn)
for j in range(0, yn):
if j == yn - 1:
ygrid_new[j] = 0.5 * (ygrid[j] + ygrid[j] + 1)
Ug[j, :] = np.nan
Vg[j, :] = np.nan
else:
y0 = j
y1 = j + 1
ygrid_new[j] = 0.5 * (ygrid[y0] + ygrid[y1])
if abs(ygrid_new[j]) <= 2.0: # 赤道付近においては地衡流は計算しない
Ug[j, :] = np.nan
Vg[j, :] = np.nan
else:
ew_dist = subroutine.dist_on_sphere([0.0, ygrid_new[j]], [1.0, ygrid_new[j]])
f = subroutine.f0(ygrid_new[j])
for i in range(0, xn):
x0 = i
if i == xn - 1:
x1 = 0
lon = 0.5 * (xgrid[i] + xgrid[i] + 1.0)
else:
x1 = i + 1
lon = 0.5 * (xgrid[x0] + xgrid[x1])
if j == 1:
xgrid_new[i] = lon
ssh00 = ssh[y0, x0]
ssh01 = ssh[y1, x0]
ssh10 = ssh[y0, x1]
ssh11 = ssh[y1, x1]
a = Using_jit.average_of_2data(ssh01, ssh11)
b = Using_jit.average_of_2data(ssh00, ssh10)
c = Using_jit.average_of_2data(ssh10, ssh11)
d = Using_jit.average_of_2data(ssh00, ssh01)
Ug[j, i] = -g / f * (a - b) / ns_dist
Vg[j, i] = g / f * (c - d) / ew_dist
return Ug, Vg, xgrid_new, ygrid_new
def cal_curl(year, month, product_n = 3):
taux = D.get_data(year, month, 'taux', 1, product_n)
tauy = D.get_data(year, month, 'tauy', 1, product_n)
xgrid, ygrid, zgrid = D.get_grid_value('taux', product_n)
xn = xgrid.size
yn = ygrid.size
curl = np.zeros([yn, xn])
ns_dist = subroutine.dist_on_sphere([0.0, - 0.5], [0.0, 0.5])
for j in range(0, yn):
if j == yn - 1:
curl[j, :] = np.nan
else:
y0 = j
y1 = j + 1
lat0 = ygrid[y0]
lat1 = ygrid[y1]
tmpdist = np.average(np.array([lat0, lat1]))
ew_dist = subroutine.dist_on_sphere([0.0, tmpdist], [1.0, tmpdist])
for i in range(0, xn):
x0 = i
lon0 = xgrid[x0]
if i == xn - 1:
x1 = 0
lon1 = xgrid[x1]
else:
x1 = i + 1
lon1 = xgrid[x1]
taux00 = taux[y0, x0]
tauy00 = tauy[y0, x0]
taux01 = taux[y1, x0]
tauy01 = tauy[y1, x0]
taux10 = taux[y0, x1]
tauy10 = tauy[y0, x1]
taux11 = taux[y1, x1]
tauy11 = tauy[y1, x1]
a = Using_jit.average_of_2data(tauy10, tauy11)
b = Using_jit.average_of_2data(tauy00, tauy01)
c = Using_jit.average_of_2data(taux01, taux11)
d = Using_jit.average_of_2data(taux00, taux10)
if np.isnan(a - b) == False and np.isnan(c - d) == False:
curl[j, i] = (a - b) / ew_dist - (c - d) / ns_dist
elif np.isnan(a - b) == False:
curl[j, i] = (a - b) / ew_dist
elif np.isnan(c - d) == False:
curl[j, i] = - (c - d) / ns_dist
else:
curl[j, i] = np.nan
return curl
def __init__(self, ID, AreaName, slat, nlat, wlon, elon): import subroutine self.ID = ID self.AreaName = AreaName self.slat = slat self.nlat = nlat self.wlon = wlon self.elon = elon self.xn = int(elon - wlon + 1) self.yn = int(nlat - slat + 1) self.EWdist = subroutine.dist_on_sphere([wlon, 0.5 * (slat + nlat)], [elon + 1, 0.5 * (slat + nlat)]) self.NSdist = subroutine.dist_on_sphere([0.0, slat], [0.0, nlat + 1]) self.Square = self.EWdist * self.NSdist # Areaの面積
def cal_Salt_Transport(s, u, Zonal_or_Meridional, product_n):
# 東西輸送量も鉛直輸送量もどっちも求められます。
# Zonal : Zonal_or_Meridional = 0
# Meridional : Zonal_or_Meridional = 1
rho0 = 1024.0 # 面倒くさいので密度は一定値。
yn = s.shape[0]
xn = s.shape[1]
zn = s.shape[2]
ns_dist = subroutine.dist_on_sphere([0.0, - 0.5], [0.0, 0.5])
xgrid, ygrid, zgrid = D.get_grid_value('s', product_n)
Data = np.zeros((yn, xn, zn))
for j in range(0, yn):
if j == 0:
Data[j, :, :] = np.nan
else:
y0 = j - 1
y1 = j
lat1 = ygrid[y1]
if Zonal_or_Meridional == 0:
L = ns_dist # 東西輸送量であれば、南北に積分
elif Zonal_or_Meridional == 1:
L = subroutine.dist_on_sphere([0.0, lat1], [1.0, lat1]) # 南北輸送量であれば、東西に積分
else:
raise ValueError('your Zonal_or_Meridional argument is not valid!')
for i in range(0, xn):
x1 = i
if i == 0:
x0 = xn - 1
else:
x0 = i - 1
if np.isnan(s[y1, x1, 0]) == True:
Data[y1, x1, :] = np.nan
else:
for k in range(0, zn):
# U = np.array([u[y0, x0, k], u[y1, x0, k], u[y0, x1, k], u[y1, x1, k]])
# Data[y1, x1, k] = L * lz[k] * 1e-3 * rho0 * s[y1, x1, k] * \
# np.average(U[np.where(np.isnan(U) == False)])
Data[y1, x1, k] = L * lz[k] * 1e-3 * rho0 * s[y1, x1, k] * \
average_of_4data(u[y0, x0, k], u[y1, x0, k], u[y0, x1, k], u[y1, x1, k])
return Data
def cal_diffusion_of_variable(S, kappa, product_n):
# kappaは水平拡散係数。
# 海洋ではkappa=500(m^2/s)程度としている文献多し。
Mt = 60 * 60 * 24 * 30
xgrid, ygrid, zgrid = subroutine.product_grid_info('s', 'data', product_n)
yn = S.shape[0]
xn = S.shape[1]
zn = S.shape[2]
ns_dist = subroutine.dist_on_sphere([0.0, - 0.5], [0.0, 0.5])
Diff = np.zeros((yn, xn, zn))
for j in range(yn):
if j == 0 or j == yn - 1:
Diff[j, :, :] = np.nan
else:
y0 = j - 1
y1 = j
y2 = j + 1
lat1 = ygrid[y1]
ew_dist = subroutine.dist_on_sphere([0.0, lat1], [1.0, lat1])
for i in range(xn):
x1 = i
if i == 0:
x0 = xn - 1
else:
x0 = i - 1
if i == xn - 1:
x2 = 0
else:
x2 = i + 1
S01 = S[y1, x0, :]
S10 = S[y0, x1, :]
S11 = S[y1, x1, :]
S21 = S[y1, x2, :]
S12 = S[y2, x1, :]
Diff[j, i, :] = Mt * kappa * (S21 - 2.0 * S11 + S01) / (ew_dist)**2 + \
Mt * kappa * (S12 - 2.0 * S11 + S10) / (ns_dist)**2
return Diff
def cal_ew_ns_dist(year,month,product_n=3):
_,title_name,_=product_n_to_name(product_n)
xgrid,ygrid,zgrid=product_grid_info('u','data',product_n)
u=get_data(year,month,'u',0,title_name)
v=get_data(year,month,'v',0,title_name)
xn=xgrid.size
yn=ygrid.size
zn=zgrid.size
ew_dist=zeros([yn-1,xn])
ns_dist=zeros([yn-1,xn])
xgrid_for_manual_w=zeros(xn)
ygrid_for_manual_w=zeros(yn-1)
zgrid_for_manual_w=zeros(zn)
for i in range(0,xn):
x0=i
lon0=xgrid[x0]
if i==xn-1:
x1=0
lon1=xgrid[x1]
xgrid_for_manual_w[i]=average([lon0,lon1+360])
else:
x1=i+1
lon1=xgrid[x1]
xgrid_for_manual_w[i]=average([lon0,lon1])
for j in range(0,yn-1):
y0=j
y1=j+1
lat0=ygrid[y0]
lat1=ygrid[y1]
ygrid_for_manual_w[j]=average([lat0,lat1])
ew_dist[j,i]=dist_on_sphere([lon0,ygrid_for_manual_w[j]],[lon1,ygrid_for_manual_w[j]])
ns_dist[j,i]=dist_on_sphere([xgrid_for_manual_w[i],lat0],[xgrid_for_manual_w[i],lat1])
return ew_dist,ns_dist
def get_water_mass(product_n = 3):
_, title_name, _ = product_n_to_name(product_n)
s = get_data(2001, 1, 's', 0, title_name)
xgrid, ygrid, zgrid = product_grid_info('w', 'data', product_n)
xn = xgrid.size
yn = ygrid.size
zn = zgrid.size
ew_dist = zeros([yn, xn])
ns_dist = zeros([yn, xn])
Mass = zeros([yn, xn])
for i in range(0, xn):
for j in range(0, yn):
ew_dist[j, i] = dist_on_sphere([0, ygrid[j]], [1, ygrid[j]])
ns_dist[j, i] = dist_on_sphere([0, ygrid[j] - 0.5], [0, ygrid[j] + 0.5])
if isnan(s[j, i, 0]) == True:
Mass[j, i] = 0.0
else:
L = max(where(isnan(s[j, i, :]) == False)[0])
Mass[j, i] = zgrid[L] * ew_dist[j, i] * ns_dist[j, i]
return sum(Mass)
def get_SUMsff(year, month, product_n = 3):
# 全球での淡水フラックスの総和を求める
# 単位はm^3/month。
_, title_name, _ = product_n_to_name(product_n)
sff = get_data(year, month, 'sff', 1, title_name)
xgrid, ygrid, _ = product_grid_info('t', 'data', product_n)
xn = xgrid.size
yn = ygrid.size
ew_dist = zeros([yn, xn])
ns_dist = zeros([yn, xn])
SFF = zeros([yn, xn])
for i in range(0, xn):
for j in range(0, yn):
ew_dist[j, i] = dist_on_sphere([0, ygrid[j]], [1, ygrid[j]])
ns_dist[j, i] = dist_on_sphere([0, ygrid[j] - 0.5], [0, ygrid[j] + 0.5])
if isnan(sff[j, i]) == True:
SFF[j, i] = 0.0
else:
SFF[j, i] = sff[j, i] * ew_dist[j, i] * ns_dist[j, i]
return sum(SFF)
def cal_Hor_and_Ver_Flux(s, u, v, w):
product_n = 3
Mt = 60 * 60 * 24 * 30
xgrid, ygrid, zgrid = product_grid_info('t', 'data', product_n)
xn = xgrid.size
yn = ygrid.size
zn = zgrid.size
s[where(abs(s) == inf)] = nan
u[where(abs(u) == inf)] = nan
v[where(abs(v) == inf)] = nan
w[where(abs(w) == inf)] = nan
ZonalFlux = zeros((yn, xn, zn), dtype = float64)
MeridFlux = zeros((yn, xn, zn), dtype = float64)
VertcFlux = zeros((yn, xn, zn), dtype = float64)
ns_dist = dist_on_sphere([0.0, - 0.5], [0.0, 0.5])
for j in range(0, yn):
if j % 10 == 0:
print 'j= %3i' % j
if j == 0 or j == yn - 1:
ZonalFlux[j, :, :] = nan
MeridFlux[j, :, :] = nan
VertcFlux[j, :, :] = nan
else:
y0 = j - 1
y1 = j
y2 = j + 1
lat1 = ygrid[y1]
ew_dist = dist_on_sphere([0.0, lat1], [1.0, lat1])
for i in range(0, xn):
x1 = i
lon1 = xgrid[x1]
if i == 0:
x0 = xn - 1
x2 = i + 1
elif i == xn - 1:
x0 = i - 1
x2 = xn - 1
else:
x0 = i - 1
x2 = i + 1
u00 = u[y0, x0, :]
v00 = v[y0, x0, :]
s00 = s[y0, x0, :]
s01 = s[y1, x0, :]
s10 = s[y0, x1, :]
s11 = s[y1, x1, :]
ZonalFlux[j, i, :] = cal_a_Flux(u00, s00, s01, s10, s11, ew_dist)
MeridFlux[j, i, :] = cal_a_Flux(v00, s00, s10, s01, s11, ns_dist)
for k in range(0, zn):
# VertcFlux
if k != zn - 1:
dz = zgrid[k + 1] - zgrid[k]
VertcFlux[:, :, k] = w[:, :, k] * (s[:, :, k + 1] - s[:, :, k])/dz * Mt
else:
VertcFlux[:, :, k] = nan
# VertcFlux
VertcFlux = convert.convert_Sgrid_value_to_UVgrid_value_3D(VertcFlux)
return ZonalFlux, MeridFlux, VertcFlux
def cal_manual_w(year,month,product_n=3, GM = 'N'):
_,title_name,_=product_n_to_name(product_n)
if GM == 'N':
u=get_data(year,month,'u',0,title_name)
v=get_data(year,month,'v',0,title_name)
elif GM == 'Y':
u = get_data(year, month, 'ustar', 0, title_name)
v = get_data(year, month, 'vstar', 0, title_name)
else:
raise Exception('your GM argument is not valid!')
xgrid,ygrid,zgrid=product_grid_info('u','data',product_n)
xn=xgrid.size
yn=ygrid.size
zn=zgrid.size
manual_w=zeros([yn,xn,zn])
xgrid_for_manual_w=zeros(xn)
ygrid_for_manual_w=zeros(yn)
zgrid_for_manual_w=zeros(zn)
dH=zeros(zn)
for k in range(0,zn):
if k==0:
zgrid_for_manual_w[k]=2*zgrid[k]
dH[k]=zgrid_for_manual_w[k]
else:
zgrid_for_manual_w[k]=2*zgrid[k]-zgrid_for_manual_w[k-1]
dH[k]=zgrid_for_manual_w[k]-zgrid_for_manual_w[k-1]
for i in range(0,xn):
x0=i
lon0=xgrid[x0]
if i==xn-1:
x1=0
lon1=xgrid[x1]
xgrid_for_manual_w[i]=average([lon0,lon1+360])
else:
x1=i+1
lon1=xgrid[x1]
xgrid_for_manual_w[i]=average([lon0,lon1])
for j in range(0,yn):
if j == yn - 1:
manual_w[j,i,:]=nan
ygrid_for_manual_w[j]=average([ygrid[yn - 1],ygrid[yn - 1] + 1])
else:
y0=j
y1=j + 1
lat0=ygrid[y0]
lat1=ygrid[y1]
ygrid_for_manual_w[j]=average([lat0,lat1])
ew_dist=dist_on_sphere([lon0,ygrid_for_manual_w[j]],[lon1,ygrid_for_manual_w[j]])
ns_dist=dist_on_sphere([xgrid_for_manual_w[i],lat0],[xgrid_for_manual_w[i],lat1])
for k in range(0,zn):
if k==0:
manual_w0=0.0
else:
manual_w0=manual_w[j,i,k-1]
u00=u[y0,x0,k]
v00=v[y0,x0,k]
u01=u[y1,x0,k]
v01=v[y1,x0,k]
u10=u[y0,x1,k]
v10=v[y0,x1,k]
u11=u[y1,x1,k]
v11=v[y1,x1,k]
if any(isnan(u00, u01, u10, u11)) == False:
manual_w[j,i,k]=dH[k]*(((v01+v11)/2.0-(v00+v10)/2.0)/ns_dist
+((u10+u11)/2.0-(u00+u01)/2.0)/ew_dist)+manual_w0
else:
manual_w[j,i,k]=nan
return manual_w,xgrid_for_manual_w,ygrid_for_manual_w,zgrid_for_manual_w
def cal_GM_diffusion(S, ustar, vstar):
# GM velocityを用いてGM拡散項を計算
# 増田さんいわく、この項を入れなくても収支は閉じるということだが、
# GM項がどれくらいの大きさなのかを見ておくのは悪くないと思ったので計算してみる。
product_n = 3
Mt = 60 * 60 * 24 * 30
xgrid, ygrid, zgrid = product_grid_info("t", "data", product_n)
xn = xgrid.size
yn = ygrid.size
zn = zgrid.size
ZonalFlux = zeros((yn, xn, zn))
MeridFlux = zeros((yn, xn, zn))
ns_dist = dist_on_sphere([0.0, -0.5], [0.0, 0.5])
for j in range(0, yn):
if j % 10 == 0:
print "j= %3i" % j
if j == 0 or j == yn - 1:
ZonalFlux[j, :, :] = nan
MeridFlux[j, :, :] = nan
else:
y0 = j - 1
y1 = j
y2 = j + 1
lat1 = ygrid[y1]
ew_dist = dist_on_sphere([0.0, lat1], [1.0, lat1])
for i in range(0, xn):
x1 = i
lon1 = xgrid[x1]
if i == 0:
x0 = xn - 1
x2 = i + 1
elif i == xn - 1:
x0 = i - 1
x2 = xn - 1
else:
x0 = i - 1
x2 = i + 1
if isnan(S[j, i, 0]) == True:
ZonalFlux[j, i, :] = nan
MeridFlux[j, i, :] = nan
else:
for k in range(0, zn):
S00 = S[y0, x0, k]
S01 = S[y1, x0, k]
S10 = S[y0, x1, k]
S11 = S[y1, x1, k]
ustar01 = ustar[y1, x0, k]
ustar11 = ustar[y1, x1, k]
ustar21 = ustar[y1, x2, k]
vstar10 = vstar[y0, x1, k]
vstar11 = vstar[y1, x1, k]
vstar12 = vstar[y2, x1, k]
if isnan(S11) == True:
ZonalFlux[j, i, k:] = nan
MeridFlux[j, i, k:] = nan
break
else:
ZonalFlux[j, i, k] = cal_a_GM_Flux(S00, S01, S10, S11, ustar01, ustar11, ustar21, ew_dist)
MeridFlux[j, i, k] = cal_a_GM_Flux(S00, S10, S01, S11, vstar10, vstar11, vstar12, ns_dist)
return ZonalFlux, MeridFlux
def cal_Hor_and_Ver_Flux(s, u, v, w, ID):
Method_doc = Budget_at_Global_Ocean.Method[ID].Method_doc
Hadv_func = getattr(Using_jit, 'cal_a_HFlux' + Method_doc)
Vadv_func = getattr(Using_jit, 'cal_a_VFlux' + Method_doc)
product_n = 3
xgrid, ygrid, zgrid = subroutine.product_grid_info('t', 'data', product_n)
xn = xgrid.size
yn = ygrid.size
zn = zgrid.size
s[np.where(abs(s) == np.inf)] = np.nan
u[np.where(abs(u) == np.inf)] = np.nan
v[np.where(abs(v) == np.inf)] = np.nan
w[np.where(abs(w) == np.inf)] = np.nan
ZonalFlux = np.zeros((yn, xn, zn))
MeridFlux = np.zeros((yn, xn, zn))
VertcFlux = np.zeros((yn, xn, zn))
ns_dist = subroutine.dist_on_sphere([0.0, - 0.5], [0.0, 0.5])
for j in range(0, yn):
if j == 0 or j == yn - 1:
ZonalFlux[j, :, :] = np.nan
MeridFlux[j, :, :] = np.nan
VertcFlux[j, :, :] = np.nan
else:
y0 = j - 1
y1 = j
y2 = j + 1
lat1 = ygrid[y1]
ew_dist = subroutine.dist_on_sphere([0.0, lat1], [1.0, lat1])
for i in range(0, xn):
x1 = i
lon1 = xgrid[x1]
if i == 0:
x0 = xn - 1
x2 = i + 1
elif i == xn - 1:
x0 = i - 1
x2 = xn - 1
else:
x0 = i - 1
x2 = i + 1
u00 = u[y0, x0, :]
u01 = u[y1, x0, :]
u10 = u[y0, x1, :]
u11 = u[y1, x1, :]
v00 = v[y0, x0, :]
v01 = v[y1, x0, :]
v10 = v[y0, x1, :]
v11 = v[y1, x1, :]
s01 = s[y1, x0, :]
s10 = s[y0, x1, :]
s11 = s[y1, x1, :]
s21 = s[y1, x2, :]
s12 = s[y2, x1, :]
ZonalFlux[j, i, :] = Hadv_func(u00, u01, u10, u11, s01, s11, s21, ew_dist)
MeridFlux[j, i, :] = Hadv_func(v00, v10, v01, v11, s10, s11, s12, ns_dist)
VertcFlux[j, i, :] = Vadv_func(w[j, i, :], s[j, i, :])
Flux = np.array([ZonalFlux, MeridFlux, VertcFlux])
return Flux
