def get_cat3_iwc(model, region, stat=STAT):
"""Returns the profiles of iwc only for CAT 3"""
fwp = load.get_iwp(model, region, ice_only=False)
if model.lower() != "sam":
fwp = fwp[11::12]
twc = load.get_twc(model, region)
print(fwp.shape, twc.shape)
if (fwp.shape[0] != twc.shape[0]):
raise Exception("fwp and twc time not aligned: shapes", fwp.shape,
twc.shape)
if len(fwp.shape) == len(twc.shape):
twc3 = np.where((fwp > 1e-4) & (fwp < 1e-2), twc, np.nan)
elif len(fwp.shape) < len(twc.shape):
fwp = fwp[:, np.newaxis]
twc3 = np.where((fwp > 1e-4) & (fwp < 1e-2), twc, np.nan)
else:
raise Exception("fwp and twc shapes don't match", fwp.shape, twc.shape)
if stat.lower() == "mean":
if len(twc3.shape) < 4: # icon
twc3 = np.nanmean(twc3, axis=(0, 2))
else:
twc3 = np.nanmean(twc3, axis=(0, 2, 3))
elif stat.lower() == "median":
if len(twc3.shape) < 4: # icon
twc3 = np.nanmedian(twc3, axis=(0, 2))
else:
twc3 = np.nanmedian(twc3, axis=(0, 2, 3))
else:
raise Exception(
"stat ({}) not defined. Try 'mean' or 'median'.".format(stat))
return twc3
def cat3_profiles(model, region):
if model.lower() == "nicam":
ice_only = False
else:
ice_only = True
if model.lower() == "cccm":
ds = load01deg.get_cccm(region)
iwc = ds[
"iwc used in CERES radiation, avg over cloudy part of CERES footprint"].values / 1000
fwp = ds["iwp MODIS"].values / 1000
z = ds["alt"].values * 1000
# print(model, "\n", iwc.mean())
elif model.lower() == "dardar":
ds = load01deg.get_dardar(region)
iwc = ds["iwc"].values / 1000
fwp = ds["iwp"].values / 1000
z = ds["height"].values
# print(model, "\n", iwc.mean())
else:
iwc = load.get_twc(model, region).values
fwp = load.get_iwp(model, region, ice_only=False).values
z = load.get_levels(model, region)
# print(model,"\n\t", iwc.shape, fwp.shape, z.shape)
if model.lower() == "fv3" or model.lower() == "icon":
fwp = fwp[11::12, np.newaxis]
elif model.lower() == "nicam":
fwp = fwp[11::12]
else:
fwp = fwp[:, np.newaxis]
# print(fwp.shape, iwc.shape, z.shape)
iwc3 = np.where((fwp >= 1e-4) & (fwp < 1e-2), iwc, np.nan)
# print(fwp.shape, iwc.shape, iwc3.shape, z.shape)
return (iwc3, z)
def get_cat_ttl_iwp(model, region):
""" Returns tuple of (ttliwp1, ttliwp2, ttliwp3),
the frozen water path in the 14-18km layer for
each category."""
if model == "DARDAR":
if region == "TWP":
diwc = xr.open_dataset(ap.DARDAR_TWP)['iwc']
iwp = xr.open_dataset(ap.DARDAR_TWP)['iwp']
elif region == "NAU":
diwc = xr.open_dataset(ap.DARDAR_NAU)['iwc']
iwp = xr.open_dataset(ap.DARDAR_NAU)['iwp']
else:
diwc = xr.open_dataset(ap.DARDAR_SHL)['iwc']
iwp = xr.open_dataset(ap.DARDAR_SHL)['iwp']
ttliwp = util.int_wrt_alt(
diwc.values[:,
np.argmin(abs(diwc.height.values - 18000)):np.
argmin(abs(diwc.height.values - 14000))],
diwc.height.values[np.argmin(abs(diwc.height.values - 18000)):np.
argmin(abs(diwc.height.values - 14000))])
del diwc
print(iwp.shape, ttliwp.shape)
ttliwp1 = np.where((iwp >= 1000), ttliwp, np.nan)
ttliwp2 = np.where((iwp >= 10), ttliwp, np.nan)
ttliwp3 = np.where((iwp >= 0.1) & (iwp < 10), ttliwp, np.nan)
del iwp
else:
ttliwp = load.get_ttliwp(model, region)
iwp = load.get_iwp(model, region, ice_only=False)
print(ttliwp.shape, iwp.shape)
if model.lower() == "nicam":
iwp = iwp[1::12, 0, :, :]
if model.lower() == "icon":
iwp = iwp[1::12, :]
elif model.lower() == "fv3":
iwp = iwp[1::12, :, :]
print(iwp.shape, ttliwp.shape)
ttliwp1 = np.where((iwp >= 1.0), ttliwp, np.nan)
ttliwp2 = np.where((iwp >= 1e-2) & (iwp < 1.0), ttliwp, np.nan)
ttliwp3 = np.where((iwp >= 1e-4) & (iwp < 1e-2), ttliwp, np.nan)
del iwp
print("returning ttliwp per category for ", model, region)
return (ttliwp1, ttliwp2, ttliwp3)
def get_isottlci(model, region, thres=0.1):
"""
Saves the clear sky olr, alb, lw and sw cre to a csv file for
both the mean and median values of isolated TTL cirrus columns.
Parameters:
-thres (f) : clear-sky threshold in g/m2
-model (str): model acronym
-region(str): region acronym
"""
thres = thres # to kg/m2
olr, alb = load.get_olr_alb(model, region)
time = np.arange(3,alb.shape[0]*3+3,3)%24
if model.lower()=="nicam":
time = time[:, np.newaxis, np.newaxis, np.newaxis]
elif model.lower()=="icon":
time = time[:, np.newaxis]
else:
time = np.array(time)[:, np.newaxis, np.newaxis]
if region.lower()=="twp":
alb = alb.where(time>=20)
olr = olr.where(time>=20)
elif region.lower()=="nau":
alb = alb.where((time>=22)|(time<=2))
olr = olr.where((time>=22)|(time<=2))
else:
alb = alb.where((time>=11)&(time<=15))
olr = olr.where((time>=11)&(time<=15))
fwp = load.get_iwp(model, region, ice_only=False)*1000
ttliwp = load.get_ttliwp(model, region)*1000
lwp = load.get_lwp(model, region, rain=False)*1000
if model.lower()!="sam":
fwp = fwp[11::12]
lwp = lwp[11::12]
print(olr.shape, fwp.shape, lwp.shape, alb.shape)
olrcs = load.get_clearskyolr(model, region, fwp, lwp)
albcs = load.get_clearskyalb(model, region, fwp, lwp)
print(olr.shape, olrcs.shape, fwp.shape, lwp.shape, alb.shape, albcs.shape)
if model.lower()=="nicam":
fwp = fwp[:,0]
lwp = lwp[:,0]
olr = olr[:,0]
alb = alb[:,0]
albcs = albcs[:,0]
olrcs = olrcs[:,0]
# get isolated ttl ci
print("fwp and ttliwp shape", fwp.shape, ttliwp.shape)
wp_below14km = (fwp.values - ttliwp.values) + lwp.values
del fwp, lwp
print("got wp below 14km")
if region.lower()=="shl":
solar_const = 435.2760211
else:
solar_const = 413.2335274
isottlci_mask = np.where((wp_below14km<thres)&(ttliwp>=thres), True, False)
olr_iso = np.where(isottlci_mask, olr, np.nan)
del olr
alb_iso = np.where(isottlci_mask, alb, np.nan)
del alb
freq = np.sum(isottlci_mask)/len(isottlci_mask.flatten())
print("freq", model, region, freq, np.sum(isottlci_mask), len(isottlci_mask.flatten()))
lwcre_mean = np.nanmean(olrcs-olr_iso)
swcre_mean = np.nanmean(albcs-alb_iso)*solar_const
df_mean = pd.read_csv("../tables/iso_ttl_ci_all_mean.csv", index_col=0)
print("mean",[np.nanmean(olr_iso), np.nanmean(alb_iso), lwcre_mean, swcre_mean])
df_mean[model+"_"+region] = [freq, np.nanmean(olr_iso), np.nanmean(alb_iso), lwcre_mean, swcre_mean]
df_mean.to_csv("../tables/iso_ttl_ci_all_mean.csv")
lwcre_med = np.nanmedian(olrcs-olr_iso)
swcre_med = np.nanmedian(albcs-alb_iso)*solar_const
df_med = pd.read_csv("../tables/iso_ttl_ci_all_median.csv", index_col=0)
print("median",[np.nanmedian(olr_iso), np.nanmedian(alb_iso), lwcre_med, swcre_med])
df_med[model+"_"+region] = [freq, np.nanmedian(olr_iso), np.nanmedian(alb_iso), lwcre_med, swcre_med]
df_med.to_csv("../tables/iso_ttl_ci_all_median.csv")
print("...done.")
return
def get_cat_lw_sw(model, region, save=True, mean=False):
if model.lower()=="cccm":
ds = load01deg.get_cccm(region)
olr = ds["Outgoing LW radiation at TOA"]
swu = ds["Outgoing SW radiation at TOA"]
swd = ds["Incoming SW radiation at TOA"]
alb = swu/swd.values
olrcs = ds["Clear-sky outgoing LW radiation at TOA"]
swucs = ds["Clear-sky outgoing SW radiation at TOA"]
albcs = swucs/swd.values
fwp = ds["iwp MODIS"]
lwp = ds["lwp MODIS"]
else:
olr, alb = load.get_olr_alb(model, region)
fwp = load.get_iwp(model, region, ice_only=False)*1000
lwp = load.get_lwp(model, region, rain=False)*1000
if model.lower()!="sam":
fwp = fwp[11::12]
lwp = lwp[11::12]
print(olr.shape, fwp.shape, lwp.shape, alb.shape)
olrcs = load.get_clearskyolr(model, region, fwp, lwp)
albcs = load.get_clearskyalb(model, region, fwp, lwp)
if mean:
olrcs = np.nanmean(olrcs, axis=0)
albcs = np.nanmean(albcs, axis=0)
else:
olrcs = np.nanmedian(olrcs, axis=0)
albcs = np.nanmedian(albcs, axis=0)
print(olr.shape, olrcs.shape, fwp.shape, lwp.shape, alb.shape, albcs.shape)
# cat mean values (olr, lwcre, alb, swcre)
thres1 = 1000
thres2 = 10
thres3 = 0.1
if mean:
# cat 1
olr1 = np.nanmean(np.where(fwp>thres1, olr, np.nan))
lwcre1 = np.nanmean(olrcs-(np.where(fwp>thres1, olr, np.nan)))
alb1 = np.nanmean(np.where(fwp>thres1, alb, np.nan))
swcre1 = np.nanmean(albcs-(np.where(fwp>thres1, alb, np.nan)))
# cat 2
olr2 = np.nanmean(np.where((fwp>thres2)&(fwp<=thres1), olr, np.nan))
lwcre2= np.nanmean(olrcs-(np.where((fwp>thres2)&(fwp<=thres1), olr, np.nan)))
alb2 = np.nanmean(np.where((fwp>thres2)&(fwp<=thres1), alb, np.nan))
swcre2 = np.nanmean(albcs-(np.where((fwp>thres2)&(fwp<=thres1), alb, np.nan)))
# cat 3
olr3 = np.nanmean(np.where((fwp>thres3)&(fwp<=thres2), olr, np.nan))
lwcre3= np.nanmean(olrcs-(np.where((fwp>thres3)&(fwp<=thres2), olr, np.nan)))
alb3 = np.nanmean(np.where((fwp>thres3)&(fwp<=thres2), alb, np.nan))
swcre3 = np.nanmean(albcs-(np.where((fwp>thres3)&(fwp<=thres2), alb, np.nan)))
# clear sky
olr4 = np.nanmean(olrcs)
alb4 = np.nanmean(albcs)
else:
# cat 1
olr1 = np.nanmedian(np.where(fwp>thres1, olr, np.nan))
lwcre1 = np.nanmedian(olrcs-(np.where(fwp>thres1, olr, np.nan)))
alb1 = np.nanmedian(np.where(fwp>thres1, alb, np.nan))
swcre1 = np.nanmedian(albcs-(np.where(fwp>thres1, alb, np.nan)))
# cat 2
olr2 = np.nanmedian(np.where((fwp>thres2)&(fwp<=thres1), olr, np.nan))
lwcre2= np.nanmedian(olrcs-(np.where((fwp>thres2)&(fwp<=thres1), olr, np.nan)))
alb2 = np.nanmedian(np.where((fwp>thres2)&(fwp<=thres1), alb, np.nan))
swcre2 = np.nanmedian(albcs-(np.where((fwp>thres2)&(fwp<=thres1), alb, np.nan)))
# cat 3
olr3 = np.nanmedian(np.where((fwp>thres3)&(fwp<=thres2), olr, np.nan))
lwcre3= np.nanmedian(olrcs-(np.where((fwp>thres3)&(fwp<=thres2), olr, np.nan)))
alb3 = np.nanmedian(np.where((fwp>thres3)&(fwp<=thres2), alb, np.nan))
swcre3 = np.nanmedian(albcs-(np.where((fwp>thres3)&(fwp<=thres2), alb, np.nan)))
# clear sky
olr4 = np.nanmedian(olrcs)
alb4 = np.nanmedian(albcs)
if region.lower()=="shl":
solar_const = 435.2760211
else:
solar_const = 413.2335274
olr_list = np.array([olr1, olr2, olr3, olr4])
alb_list = np.array([alb1, alb2, alb3, alb4])
lw_list = np.array([lwcre1, lwcre2, lwcre3, 0])
sw_list = np.array([swcre1, swcre2, swcre3, 0])*solar_const
df = pd.DataFrame(np.array([olr_list, alb_list, lw_list, sw_list]).T, columns=["olr","alb","lwcre","swcre"], index=["CAT1","CAT2","CAT3","CS"])
print(df)
if save:
df.to_csv("../tables/mean_{}_{}.csv".format(model, region))
print("saved.")
return df
# return ret[n - 1:] / n
if len(a)%3==1:
a = a[:-1]
elif len(a)%3==2:
a = a[:-2]
print(len(a)%3)
avg = (a[0::3]+a[1::3]+a[2::3])/3
return avg
# load all fwp
obs = "DARDAR"
print("Getting frozen hydrometeors")
# giwp = util.iwp_wrt_pres("GEOS", REGION, hydro_type=hydro_type, geos_graupel=True)
print("geos zeros until we get new data")
iiwp_twp = load.get_iwp("ICON", "TWP", ice_only=False).values
iiwp_shl = load.get_iwp("ICON", "SHL", ice_only=False).values
iiwp_nau = load.get_iwp("ICON", "NAU", ice_only=False).values
print("loaded icon")
fiwp_twp = load.get_iwp("FV3", "TWP", ice_only=False).values
fiwp_shl = load.get_iwp("FV3", "SHL", ice_only=False).values
fiwp_nau = load.get_iwp("FV3", "NAU", ice_only=False).values
print("loaded fv3")
niwp_twp = load.get_iwp("NICAM", "TWP", ice_only=False).values
niwp_shl = load.get_iwp("NICAM", "SHL", ice_only=False).values
niwp_nau = load.get_iwp("NICAM", "NAU", ice_only=False).values
print("loaded nicam")
siwp_twp = load.get_iwp("SAM", "TWP", ice_only=False)
siwp_shl = load.get_iwp("SAM", "SHL", ice_only=False)
siwp_nau = load.get_iwp("SAM", "NAU", ice_only=False)
sno_twp = siwp_twp.count().values
c1s = np.nanmedian(c1s)
c2s = np.nanmedian(c2s)
c3s = np.nanmedian(c3s)
c4s = np.nanmedian(c4s)
c1n = np.nanmedian(c1n)
c2n = np.nanmedian(c2n)
c3n = np.nanmedian(c3n)
c4n = np.nanmedian(c4n)
print("... done")
# %%
# load sam
cat1 = 1
cat2 = 1e-2
cat3 = 1e-4
siwpt = load.get_iwp("SAM", "TWP", ice_only=False)
siwps = load.get_iwp("SAM", "SHL", ice_only=False)
siwpn = load.get_iwp("SAM", "NAU", ice_only=False)
print("sam...")
s1t = np.nanmedian(np.where(siwpt >= cat1, siwpt, np.nan))
s2t = np.nanmedian(np.where((siwpt >= cat2) & (siwpt < cat1), siwpt, np.nan))
s3t = np.nanmedian(np.where((siwpt >= cat3) & (siwpt < cat2), siwpt, np.nan))
s4t = np.nanmedian(np.where((siwpt < cat3), siwpt, np.nan))
s1s = np.nanmedian(np.where(siwps >= cat1, siwps, np.nan))
s2s = np.nanmedian(np.where((siwps >= cat2) & (siwps < cat1), siwps, np.nan))
s3s = np.nanmedian(np.where((siwps >= cat3) & (siwps < cat2), siwps, np.nan))
s4s = np.nanmedian(np.where((siwps < cat3), siwps, np.nan))
s1n = np.nanmedian(np.where(siwpn >= cat1, siwpn, np.nan))
s2n = np.nanmedian(np.where((siwpn >= cat2) & (siwpn < cat1), siwpn, np.nan))
s3n = np.nanmedian(np.where((siwpn >= cat3) & (siwpn < cat2), siwpn, np.nan))
s4n = np.nanmedian(np.where((siwpn < cat3), siwpn, np.nan))
#!/usr/bin/env python
""" fig03_nicam_iwc_cat_profiles.py
author: sami turbeville
"""
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
from utility import load, util, analysis_parameters as ap
MODEL = "NICAM"
iwp = load.get_iwp(MODEL, "TWP").values[11::12]
# load one at a time to get category mean
print("... categorize ice ...")
iwc = xr.open_dataarray(ap.NICAM+"NICAM_iwc_TWP.nc")[16:]
print(iwp.shape, iwc.shape)
iwc1 = iwc.where(iwp>=1).mean(axis=(0,2,3))
iwc2 = iwc.where((iwp<1)&(iwp>=1e-2)).mean(axis=(0,2,3))
iwc3 = iwc.where((iwp<1e-2)&(iwp>=1e-4)).mean(axis=(0,2,3))
del iwc
print("... categorize snow ...")
swc = xr.open_dataarray(ap.NICAM+"NICAM_swc_TWP.nc")[16:]
swc1 = swc.where(iwp>=1).mean(axis=(0,2,3))
swc2 = swc.where((iwp<1)&(iwp>=1e-2)).mean(axis=(0,2,3))
swc3 = swc.where((iwp<1e-2)&(iwp>=1e-4)).mean(axis=(0,2,3))
del swc
print("... categorize graupel ...")
gwc = xr.open_dataarray(ap.NICAM+"NICAM_gwc_TWP.nc")[16:]
gwc1 = gwc.where(iwp>=1).mean(axis=(0,2,3))
gwc2 = gwc.where((iwp<1)&(iwp>=1e-2)).mean(axis=(0,2,3))
gwc3 = gwc.where((iwp<1e-2)&(iwp>=1e-4)).mean(axis=(0,2,3))
print(
"skipping observed IWV until I can figure out how to integrate wv mixing ratio"
)
# %%
# define cat limits
cat1 = 1
cat2 = 1e-2
cat3 = 1e-4
# %%
# load icon
iiwvt = load.get_iwv("ICON", "TWP")
iiwvs = load.get_iwv("ICON", "SHL")
iiwvn = load.get_iwv("ICON", "NAU")
ifwpt = load.get_iwp("ICON", "TWP", ice_only=False).values[::12]
ifwps = load.get_iwp("ICON", "SHL", ice_only=False).values[::12]
ifwpn = load.get_iwp("ICON", "NAU", ice_only=False).values[::12]
print("icon...")
i1t = np.nanmedian(np.where(ifwpt >= cat1, iiwvt, np.nan))
i2t = np.nanmedian(np.where((ifwpt >= cat2) & (ifwpt < cat1), iiwvt, np.nan))
i3t = np.nanmedian(np.where((ifwpt >= cat3) & (ifwpt < cat2), iiwvt, np.nan))
i4t = np.nanmedian(np.where((ifwpt < cat3), iiwvt, np.nan))
i1s = np.nanmedian(np.where(ifwps >= cat1, iiwvs, np.nan))
i2s = np.nanmedian(np.where((ifwps >= cat2) & (ifwps < cat1), iiwvs, np.nan))
i3s = np.nanmedian(np.where((ifwps >= cat3) & (ifwps < cat2), iiwvs, np.nan))
i4s = np.nanmedian(np.where((ifwps < cat3), iiwvs, np.nan))
i1n = np.nanmedian(np.where(ifwpn >= cat1, iiwvn, np.nan))
i2n = np.nanmedian(np.where((ifwpn >= cat2) & (ifwpn < cat1), iiwvn, np.nan))
i3n = np.nanmedian(np.where((ifwpn >= cat3) & (ifwpn < cat2), iiwvn, np.nan))
i4n = np.nanmedian(np.where((ifwpn < cat3), iiwvn, np.nan))
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from scipy import stats
from utility import load, load01deg
REGION = "NAU"
# %%
c_olrcs = load01deg.get_cccm(REGION)["Clear-sky outgoing LW radiation at TOA"]
c_swucs = load01deg.get_cccm(REGION)["Clear-sky outgoing SW radiation at TOA"]
c_swd = load01deg.get_cccm(REGION)["Incoming SW radiation at TOA"]
c_albcs = c_swucs / c_swd
del c_swucs, c_swd
fwp = load.get_iwp("NICAM", REGION, ice_only=False)
lwp = load.get_lwp("NICAM", REGION, rain=False)
fwp = fwp[11::12]
lwp = lwp[11::12]
n_olrcs = load.get_clearskyolr("NICAM", REGION, fwp, lwp)
n_albcs = load.get_clearskyalb("NICAM", REGION, fwp, lwp)
n_olrcs = n_olrcs[:, 0]
n_albcs = n_albcs[:, 0]
fwp = load.get_iwp("FV3", REGION, ice_only=False)
lwp = load.get_lwp("FV3", REGION, rain=False)
fwp = fwp[11::12]
lwp = lwp[11::12]
f_olrcs = load.get_clearskyolr("FV3", REGION, fwp, lwp)
f_albcs = load.get_clearskyalb("FV3", REGION, fwp, lwp)
fwp = load.get_iwp("ICON", REGION, ice_only=False)
lwp = load.get_lwp("ICON", REGION, rain=False)
