with Dataset(GCM_temp,'r') as D:
lat=D.variables['lat'][:]
lon=D.variables['lon'][:]
Y= lat.size
X= lon.size
vars= ['PRECT']
data_filt={}
data_map_filt={}
self_corr= {}
self_corr_mask= {}
for vv in vars:
#== trop mean data
tmp= data[vv]
tmp= tmp.reshape(tmp.size)
tmp= panta(tmp)
tmp_smooth= running_mean(tmp,6)
tmp_f = tmp - tmp_smooth; data_filt[vv]= tmp_f
#== map data
tmp= data_map[vv]
tmp= tmp.reshape(tmp.shape[0]*tmp.shape[1], tmp.shape[2], tmp.shape[3])
tmp= panta(tmp)
tmp_smooth= running_mean(tmp,6)
tmp_map_f= tmp - tmp_smooth; data_map_filt[vv]= tmp_map_f
#== correlation
Y,X= tmp_map_f.shape[1:3]
cor= np.zeros((Y,X))
cor_mask= np.zeros((Y,X))
for yy in range(Y):
for xx in range(X):
tmp_map_f_loc= tmp_map_f[:, yy, xx]
data_file = data_path_map + 'RCE.pk'
with open(data_file, 'rb') as f:
data_map = pickle.load(f)
data_file = data_path_P
with open(data_file, 'rb') as f:
pEOF = pickle.load(f)
plat = pEOF['lat']
plon = pEOF['lon']
data_file = '/data/cloud/Goldtimes5/data/GCM_SPCAM/CPL64/CRM_region_mean/PRECT.pk'
with open(data_file, 'rb') as f:
CRMP = pickle.load(f)
CRMP = CRMP.reshape((CRMP.size))
CRMP = panta(CRMP)
CRMP_f = running_mean(CRMP, 6)
CRMP = CRMP_f
#Slow
CRMP = CRMP * 0.2583 # CRM region area / total tropical area
vars = [
'RCE', 'PRECT', 'Rad_cool', 'SHFLX', 'FLNS', 'FSNS', 'FLNT', 'FSNTOA',
'FSDTOA', 'FSUTOA'
]
data_filt = {}
data_map_filt = {}
self_corr = {}
for vv in vars:
#== trop mean data
tmp = data[vv]
Rad = data['Rad_cool']
Radr = Rad.reshape(Rad.size)
pr = data['PRECT']
prr = pr.reshape(pr.size)
LWs = data['FLNS']
LWsr = LWs.reshape(LWs.size)
SWs = data['FSNS']
SWsr = SWs.reshape(SWs.size)
LWt = data['FLNT']
LWtr = LWt.reshape(LWt.size)
SWt = data['FSNTOA']
SWtr = SWt.reshape(SWt.size)
prl = data['PRECT_largeVar']
prlr = prl.reshape(prl.size)
RCEr = panta(RCEr)
cvr = panta(cvr)
prr = panta(prr)
prlr = panta(prlr)
Radr = panta(Radr)
CRMP = panta(CRMP)
LWsr = panta(LWsr)
SWsr = panta(SWsr)
LWtr = panta(LWtr)
SWtr = panta(SWtr)
# 5 yr
plt.figure(figsize=(8, 3))
plt.plot(np.arange(1, 6, 5 / 365), np.zeros(RCEr.size), 'k', linewidth=0.5)
plt.plot(np.arange(1, 6, 5 / 365),
RCEr - np.mean(RCEr),
maxY = lat.size
maxX = lon.size
Lv = 2.477 * 10**6 # J/kg
rho = 1000 # kg/m3
#=== load data and plot
with open(data_file, 'rb') as f:
data = pickle.load(f)
#=== data process
Nyr, Nd = data.shape[0:2]
data = data * Lv * rho # convert m/s to W/m2
data = data.reshape((Nyr * Nd, maxY, maxX)) # Time, lat, lon
data = panta(data)
Nt = data.shape[0] # panta mean
data_mean = np.mean(data, axis=0).reshape((1, maxY, maxX))
data_mean = np.tile(data_mean, [Nt, 1, 1])
data = data - data_mean # anomaly
#=== weighting matrix
weight = np.array(np.cos(lat * np.pi / 180)**0.5, dtype='float')
weight = weight.reshape((1, maxY, 1))
weight = np.tile(weight, [Nt, 1, maxX])
data = data * weight # weight by square cos(lat)
#=== mask
lat_mask = np.squeeze(np.array([(lat >= -30) & (lat <= 30)], dtype='bool'))
data_r = data[:, lat_mask, :]
Nt2, Ny, Nx = data_r.shape