Analysis of the new datasets¶
import xarray as xr
import xarray.ufuncs as xrf
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import glob
from matplotlib import colors
from definitions import * # imports all functions from definitions.py
#from bootstrap_sampling import *
xr.set_options(
keep_attrs=True, display_style="html"
) # Attributes of DataArrays will be kept through operations.
plt.rcParams["font.size"] = "22"
alt = [
70,
72,
74,
76,
78,
80,
82,
84,
86,
88,
90,
92,
94,
96,
98,
100,
102,
104,
106,
108,
110,
112,
114,
116,
118,
120,
]
root_path = "/home/gemeinsam_tmp/UA_students/data/PW_GW_analysis/"
#dir_path = "/home/hochatmstud/bene/"
station = "CMOR"
# Parameters
station = "Leipzig"
Wind measurements¶
ds_wind = read_group("wind", station, root_path)
u = read_var(ds_wind, "u0")
v = read_var(ds_wind, "v0")
if station == "Sodankyla_Kiruna":
u = u.sel(time=slice("2008", "2020"))
v = v.sel(time=slice("2008", "2020"))
u_anomalie = anomalie("dayofyear", u)
v_anomalie = anomalie("dayofyear", v)
sea_u = sea(40, station, u)
sea_u_anomalie = sea(40, station, u_anomalie)
sea_v = sea(40, station, v)
sea_v_anomalie = sea(40, station, v_anomalie)
Wave estimates¶
ds_waves = read_group("waves", station, root_path)
gw = read_var(ds_waves, "GW")
pw = read_var(ds_waves, "PW")
# GW activity without tides
root_path = "/home/gemeinsam_tmp/UA_students/data/PW_GW_analysis/t_tide_sa/"
ds_without_tides = read_group("waves", station, root_path)
gw_without_tides = read_var(ds_without_tides, "GW")
root_path = "/home/gemeinsam_tmp/UA_students/data/PW_GW_analysis/"
# end of GW activity without tides
if station == "Sodankyla_Kiruna":
gw = gw.sel(time=slice("2008", "2020"))
pw = pw.sel(time=slice("2008", "2020"))
gw_without_tides = gw_without_tides.sel(time=slice("2008", "2020"))
gw_anomalie = anomalie("dayofyear", gw)
pw_anomalie = anomalie("dayofyear", pw)
sea_gw = sea(40, station, gw)
# sea_gw.name = 'sea_gw'
sea_gw_anomalie = sea(40, station, gw_anomalie)
sea_gw_anomalie["name"] = "sea_gw_anomalie"
sea_gw_without_tides = sea(40, station, gw_without_tides)
sea_pw = sea(40, station, pw)
# sea_pw.name = 'sea_pw'
sea_pw_anomalie = sea(40, station, pw_anomalie)
sea_pw_anomalie["name"] = "sea_pw_anomalie"
Gravity waves¶
Gravity wave activity¶
Climatology¶
plotting_routine(sea_gw, "GW", True)
Climatology without tides¶
plotting_routine(sea_gw_without_tides, "GW", True)
Anomalies¶
plotting_routine(sea_gw_anomalie, "GW")
Gravity wave drag¶
station_name = station
directory = glob.glob(f"{root_path}fluxes*_{station}*.nc")[0]
ds = xr.open_dataset(directory)
gwd = ds["GWD"]
if station == "Sodankyla_Kiruna":
gwd = gwd.sel(time=slice("2008", "2021"))
gwd_anomalie = anomalie("dayofyear", gwd)
sea_gwd = sea(40, station_name, gwd)
# sea_gwd.name = 'sea_gwd'
sea_gwd_anomalie = sea(40, station_name, gwd_anomalie)
Climatology¶
plotting_routine(sea_gwd, "GWD")
Anomalies¶
plotting_routine(sea_gwd_anomalie, "GWD")
Momentum fluxes¶
uw = ds["uw"]
vw = ds["vw"]
#if station == "Sodankyla_Kiruna":
# uw = uw.sel(time=slice("2008", "2021"))
# vw = vw.sel(time=slice("2008", "2021"))
uw_anomalie = anomalie("dayofyear", uw)
vw_anomalie = anomalie("dayofyear", vw)
sea_uw = sea(40, station_name, uw)
sea_uw_anomalie = sea(40, station_name, uw_anomalie)
sea_vw = sea(40, station_name, vw)
sea_vw_anomalie = sea(40, station_name, vw_anomalie)
/home/hochatmstud/.conda/envs/uas/lib/python3.6/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
return np.nanmean(a, axis=axis, dtype=dtype)
/home/hochatmstud/.conda/envs/uas/lib/python3.6/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
return np.nanmean(a, axis=axis, dtype=dtype)
