def _get_comparison_data_swe( start_date = datetime(1980,1,1),
end_date = datetime(1997, 12, 31),
crcm4_data_folder = "/home/huziy/skynet1_rech3/crcm4_data/aex_p1sno",
swe_obs_data_path = "data/swe_ross_brown/swe.nc",
swe_var_name = "swe", season_to_months = None, crcm4_id = "aex"):
cache_file = "seasonal_bias_{0}.bin".format(swe_var_name)
if not os.path.isfile(cache_file):
#get crcm4 data
crcm4_data_store = {}
for season, month_list in season_to_months.iteritems():
data = ccc_analysis.get_seasonal_mean(crcm4_data_folder, start_date=start_date,
end_date=end_date, months=month_list
)
crcm4_data_store[season] = data
#get cru data
cru_data_store = {}
cru = CruReader(path=swe_obs_data_path, var_name=swe_var_name, transpose_xy_dimensions=False)
lons2d_amno, lats2d_amno = polar_stereographic.lons, polar_stereographic.lats
for season, month_list in season_to_months.iteritems():
data = cru.get_seasonal_mean_field(months=month_list, start_date=start_date, end_date=end_date)
nneighbors = 1 if swe_var_name in ["pre", "swe"] else 4
data = cru.interpolate_data_to(data, lons2d_amno, lats2d_amno, nneighbors=nneighbors)
cru_data_store[season] = data
data_list = [cru_data_store, crcm4_data_store]
pickle.dump(data_list, open(cache_file, "w"))
else:
data_list = pickle.load(open(cache_file))
cru_data_store, crcm4_data_store = data_list
return cru_data_store, crcm4_data_store
def _get_comparison_data( start_date = datetime(1970,1,1),
end_date = datetime(1999, 12, 31),
crcm4_data_folder = "data/ccc_data/aex/aex_p1st",
cru_data_path = "data/cru_data/CRUTS3.1/cru_ts_3_10.1901.2009.tmp.dat.nc",
cru_var_name = "tmp", season_to_months = None, crcm4_id = "aex"):
cache_file = "seasonal_bias_{0}.bin".format(cru_var_name)
if not os.path.isfile(cache_file):
#get crcm4 data
crcm4_data_store = {}
day_seconds = 24.0 * 60.0 * 60.0
dt = members.id_to_step[crcm4_id]
crcm4_coef = day_seconds
for season, month_list in season_to_months.iteritems():
data = ccc_analysis.get_seasonal_mean(crcm4_data_folder, start_date=start_date,
end_date=end_date, months=month_list
)
if cru_var_name == "pre":
data *= crcm4_coef
crcm4_data_store[season] = data
#get cru data
cru_data_store = {}
cru = CruReader(path=cru_data_path, var_name=cru_var_name)
lons2d_amno, lats2d_amno = polar_stereographic.lons, polar_stereographic.lats
dt = cru.get_time_step()
cru_coef = day_seconds / (dt.seconds + dt.days * day_seconds)
for season, month_list in season_to_months.iteritems():
data = cru.get_seasonal_mean_field(months=month_list, start_date=start_date, end_date=end_date)
nneighbors = 1 if cru_var_name == "pre" else 4
data = cru.interpolate_data_to(data, lons2d_amno, lats2d_amno, nneighbors=nneighbors)
if cru_var_name == "pre":
data *= cru_coef
cru_data_store[season] = data
data_list = [cru_data_store, crcm4_data_store]
pickle.dump(data_list, open(cache_file, "w"))
else:
data_list = pickle.load(open(cache_file))
cru_data_store, crcm4_data_store = data_list
return cru_data_store, crcm4_data_store