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
