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
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/"
def sea_SES_special(days_period, station_name, var):
df_dates = pd.read_csv(
dir_path
+ "dates/without_final_warmings/ssw_dates_without_final_warmings_SES_all_dates.csv"
) # you can load SSWs from a csv file like attached
dates = df_dates.set_index("BeginDate")
xa_ls = []
max_lag = days_period
for il, days in enumerate(range(-max_lag, max_lag + 1)):
sel_dates = pd.to_datetime(dates.index) + pd.Timedelta(str(days) + " days")
mask = np.in1d(var.time.dt.floor("1D"), sel_dates)
comp_m = var.sel(time=mask).mean("time")
comp_s = var.sel(time=mask).std("time")
comp_m.name = f"{var.name}_mean" # Variable Mittelwert umbenennen
comp_s.name = f"{var.name}_std" # Variable Standardabweichung umbenennen
comp_m.attrs["units"] = var.attrs["units"]
xa_ls.append(
xr.merge([comp_m, comp_s])
) # Merge arrays of mean and standard deviation in one data array
xa_comp = xr.concat(xa_ls, dim="days")
xa_comp["days"] = range(-max_lag, max_lag + 1)
return xa_comp
station = "Leipzig"
# Parameters
station = "Sodankyla_Kiruna"
Wind measurements¶
ds_wind = read_group("wind", station, root_path)
u = read_var(ds_wind, "u0")
v = read_var(ds_wind, "v0")
u_anomalie = anomalie("dayofyear", u)
v_anomalie = anomalie("dayofyear", v)
sea_u = sea_SES_special(40, station, u)
sea_u_anomalie = sea_SES_special(40, station, u_anomalie)
sea_v = sea_SES_special(40, station, v)
sea_v_anomalie = sea_SES_special(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_anomalie = anomalie("dayofyear", gw)
pw_anomalie = anomalie("dayofyear", pw)
sea_gw = sea_SES_special(40, station, gw)
# sea_gw.name = 'sea_gw'
sea_gw_anomalie = sea_SES_special(40, station, gw_anomalie)
sea_gw_anomalie["name"] = "sea_gw_anomalie"
sea_pw = sea_SES_special(40, station, pw)
# sea_pw.name = 'sea_pw'
sea_pw_anomalie = sea_SES_special(40, station, pw_anomalie)
sea_pw_anomalie["name"] = "sea_pw_anomalie"
Gravity waves¶
Gravity wave activity¶
Climatology¶
plotting_routine(sea_gw, "GW", True)
Anomalies¶
plotting_routine(sea_gw_anomalie, "GW")
Gravity wave drag¶
station_name = station
directory = glob.glob(f"{root_path}GWD*_{station}.nc")[0]
# directory = root_path + 'GWD_1999-08-01_2021-05-01_Sodankya_Esrange.nc'
ds = xr.open_dataset(directory)
gwd = ds["GWD"]
gwd_anomalie = anomalie("dayofyear", gwd)
sea_gwd = sea_SES_special(40, station_name, gwd)
# sea_gwd.name = 'sea_gwd'
sea_gwd_anomalie = sea_SES_special(40, station_name, gwd_anomalie)
Climatology¶
plotting_routine(sea_gwd, "GWD")
Anomalies¶
plotting_routine(sea_gwd_anomalie, "GWD")
