def var_calcs(var, jday=0, latlon=(-90, 90, 40, 120), plevs=(850, 200),
dp_vars=['U', 'OMEGA'], sector_lons=(60, 100)):
"""Process a single variable from a single day."""
lat1, lat2, lon1, lon2 = latlon
opts = merra.url_opts(var.name)
vertical = opts['vertical']
if vertical == 'X':
plevs = [None]
if dp_vars is not None and var.name in dp_vars:
dp = True
else:
dp = False
data = xray.Dataset()
# Lat-lon data
print('Lat-lon data')
for plev in plevs:
print('plev', plev)
var_out = latlon_data(var, lat1, lat2, lon1, lon2, plev)
data[var_out.name] = var_out
if dp:
print('Computing d/dp')
var_out = pgradient(var, lat1, lat2, lon1, lon2, plev)
data[var_out.name] = var_out
# Sector and zonal mean data
print('Computing zonal mean')
var_out = sector_mean(var, 0, 360)
data[var_out.name] = var_out
if vertical == 'P':
print('Computing sector mean')
var_out = sector_mean(var, sector_lons[0], sector_lons[1])
data[var_out.name] = var_out
# Compute daily data from subdaily data
nperday = len(atm.get_coord(data, 'time'))
data = atm.daily_from_subdaily(data, nperday, dayname='day',
dayvals=[jday])
# Make sure output is in a Dataset
if isinstance(data, xray.DataArray):
data = data.to_dataset()
return data
season = 'jja' lon1, lon2 = 0, 100 lat1, lat2 = -20, 50 u = load_daily_season(ustr, year, season, 'U', lat1, lat2, lon1, lon2) plt.figure() atm.pcolor_latlon(u.mean(dim='TIME')) ds = load_daily_season(ustr, year, season, None, lat1, lat2, lon1, lon2) u2 = ds['U'] print((u == u2).any()) season = 'ann' lon1, lon2 = 20, 120 lat1, lat2 = -60, 60 n = 8 # Number of time points per day days = np.arange(1, 366) u = load_daily_season(ustr, year, season, 'U', lat1, lat2, lon1, lon2) udaily = atm.daily_from_subdaily(u, n, dayvals=days) plt.figure() atm.pcolor_latlon(udaily.mean(axis=0), axlims=(lat1,lat2,lon1,lon2)) lat1, lat2 = 10, 30 lon1, lon2 = 60, 100 ubar = atm.mean_over_geobox(u, lat1, lat2, lon1, lon2) ubar_daily = atm.mean_over_geobox(udaily, lat1, lat2, lon1, lon2) plt.figure(figsize=(7,8)) plt.subplot(211) plt.plot(ubar) plt.subplot(212) plt.plot(ubar_daily)
#datadir = '/home/jennifer/datastore/' # ---------------------------------------------------------------------- # MERRA Daily filename = datadir + 'merra/daily/merra_u200_198601.nc' ds = atm.ncload(filename) u = ds['U'] lat = atm.get_coord(u, 'lat') lon = atm.get_coord(u, 'lon') # Number of time points per day n = 8 # Daily mean u_split = atm.split_timedim(u, n, time0_name='day') u_new = daily_from_subdaily(u, n, dayvals=np.arange(1,32)) print(np.array_equal(u_new, u_split.mean(axis=1))) # ndarray version u_new2 = daily_from_subdaily(u.values, n) print(np.array_equal(u_new, u_new2)) # Sub-sample version i = 2 u_new3 = daily_from_subdaily(u, n, method=i) print(np.array_equal(u_split[:,i], u_new3)) # Plot data to check d = 5 plt.figure()
lon1, lon2 = 40, 100
lat1, lat2 = -15, 35
varlist = ['T']
pmin, pmax = 200e2, 600e2
years = range(1979, 2015)
months = [4, 5, 6, 7, 8, 9]
g = atm.constants.g.values
nperday = 8
subset_dict = {'lon' : (lon1, lon2), 'lat' : (lat1, lat2)}
for year in years:
for mon in months:
dayvals = atm.season_days(atm.month_str(mon), atm.isleap(year))
T = merra.read_daily(varlist, year, mon, subset_dict=subset_dict)
# Daily means of 3-hourly data
T = atm.daily_from_subdaily(T, nperday, dayvals=dayvals)
# Vertical integral
Tbar = atm.int_pres(T, pmin=pmin, pmax=pmax)
Tbar = Tbar * g / (pmax - pmin)
Tbar.name='Tbar'
Tbar.attrs['long_name'] = 'Vertical mean atmospheric temperature'
Tbar.attrs['pmin'] = pmin
Tbar.attrs['pmax'] = pmax
# Save to file
atm.save_nc(savefile(year, mon, pmin, pmax), T, Tbar)
#datadir = '/home/jennifer/datastore/' # ---------------------------------------------------------------------- # MERRA Daily filename = datadir + 'merra/daily/merra_u200_198601.nc' ds = atm.ncload(filename) u = ds['U'] lat = atm.get_coord(u, 'lat') lon = atm.get_coord(u, 'lon') # Number of time points per day n = 8 # Daily mean u_split = atm.split_timedim(u, n, time0_name='day') u_new = daily_from_subdaily(u, n, dayvals=np.arange(1, 32)) print(np.array_equal(u_new, u_split.mean(axis=1))) # ndarray version u_new2 = daily_from_subdaily(u.values, n) print(np.array_equal(u_new, u_new2)) # Sub-sample version i = 2 u_new3 = daily_from_subdaily(u, n, method=i) print(np.array_equal(u_split[:, i], u_new3)) # Plot data to check d = 5 plt.figure() plt.subplot(211)
lat1, lat2 = -20, 30
varlist = ['u', 'v', 'q']
pmin = 300e2
years = range(1979, 2015)
#months = [5, 6, 7, 8]
months = [4]
subset_dict = {'lon' : (lon1, lon2), 'lat' : (lat1, lat2)}
for year in years:
for mon in months:
dayvals = atm.season_days(atm.month_str(mon), atm.isleap(year))
ds = merra.read_daily(varlist, year, mon, subset_dict=subset_dict)
uq = ds['U'] * ds['QV']
vq = ds['V'] * ds['QV']
# Daily means of 3-hourly data
nperday = 8
uq = atm.daily_from_subdaily(uq, nperday, dayvals=dayvals)
vq = atm.daily_from_subdaily(vq, nperday, dayvals=dayvals)
# Vertical integral
uq_int = atm.int_pres(uq, pmin=pmin)
vq_int = atm.int_pres(vq, pmin=pmin)
uq_int.name='uq_int'
uq_int.attrs['pmin'] = pmin
vq_int.name='vq_int'
vq_int.attrs['pmin'] = pmin
# Save to file
atm.save_nc(savefile(year, mon), uq_int, vq_int)
