Analysis of the Gravity wave drag¶
In [1]:
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 definitions import * # imports all functions from definitions.py
In [2]:
station = "Leipzig"
In [3]:
# Parameters
station = "Davis"
In [4]:
if station == "Leipzig":
dir = "/home/gemeinsam_tmp/UA_students/data/PW_GW_analysis/GWD_2004-08-01_2020-12-01_Col.nc"
elif station == "CMOR":
dir = "/home/gemeinsam_tmp/UA_students/data/PW_GW_analysis/GWD_2002-01-01_2020-11-01_CMA.nc"
elif station == "Esrange":
dir = "/home/gemeinsam_tmp/UA_students/data/PW_GW_analysis/GWD_1999-08-01_2021-03-01_Kir.nc"
elif station == "Sodankyla":
dir = "/home/gemeinsam_tmp/UA_students/data/PW_GW_analysis/GWD_2008-10-01_2021-03-01_Sod.nc"
elif station == "Davis":
dir = "/home/gemeinsam_tmp/UA_students/data/PW_GW_analysis/GWD_2005-01-01_2020-12-01_Dav.nc"
elif station == "RioGrande":
dir = "/home/gemeinsam_tmp/UA_students/data/PW_GW_analysis/GWD_2008-02-01_2021-01-01_Rio.nc"
elif station == "Sodankyla_Kiruna":
dir = "/home/gemeinsam_tmp/UA_students/data/PW_GW_analysis/GWD_1999-08-01_2019-12-01_SES.nc"
In [5]:
ds = xr.open_dataset(dir)
In [6]:
ds
Out[6]:
xarray.Dataset
- alt: 26
- time: 140256
- month(time)int64...
[140256 values with dtype=int64]
- alt(alt)int6470 72 74 76 78 ... 114 116 118 120
- units :
- km
array([ 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]) - time(time)datetime64[ns]2005-01-01 ... 2020-12-31T23:00:00
array(['2005-01-01T00:00:00.000000000', '2005-01-01T01:00:00.000000000', '2005-01-01T02:00:00.000000000', ..., '2020-12-31T21:00:00.000000000', '2020-12-31T22:00:00.000000000', '2020-12-31T23:00:00.000000000'], dtype='datetime64[ns]')
- GWD(time, alt)float64...
- units :
- m/s/day
- long_name :
- zonal GWF from fluxes
[3646656 values with dtype=float64]
- history :
- Tue Jun 1 17:50:43 2021: ncrename -v __xarray_dataarray_variable__,GWD GWD_2005-01-01_2020-12-01_Dav.nc
- NCO :
- netCDF Operators version 4.8.1 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)
In [7]:
gwd = ds["GWD"]
gwd_anomalie = anomalie("day", gwd)
sea_gwd = sea(40, station, gwd)
sea_gwd_anomalie = sea(40, station, gwd_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)
Climatology¶
In [8]:
plotting_routine(sea_gwd, "GWD")
Anomalies¶
In [9]:
plotting_routine(sea_gwd_anomalie, "GWD")
Individual events¶
In [10]:
def sea_individual(days_period, station, var):
df_dates = pd.read_csv(
dir_path
+ "dates/without_final_warmings/ssw_dates_without_final_warmings_"
+ station
+ ".csv"
) # you can load SSWs from a csv file like attached
dates = df_dates.set_index("BeginDate")
xa_ls = []
print(dates.index)
for days in dates.index:
td = pd.Timedelta(
str(days_period) + " days"
) # define timedelta e.g., +/- 30 days
comp_m = var.sel(
time=slice(pd.to_datetime(days) - td, pd.to_datetime(days) + td)
) # select the data in the time range around the SSW onset
comp_m["time"] = np.linspace(
-days_period, days_period, comp_m.time.shape[0]
) # set the time dimension of comp_m to an array of +/- the period
xa_ls.append(comp_m) # xa_ls is an array of the datasets of all single events
xa_comp = xr.concat(
xa_ls, dim="event"
) # assign 'event' as new dimension to the data array --> new array xa_comp
xa_comp[
"event"
] = (
dates.index.values
) # xa_comp has now the dimension 'event' and the values of this dimension are the indices of dates (i.e. the dates of the SSW onsets)
return xa_comp
In [11]:
sea_gwd_individual = sea_individual(40, station, gwd)
sea_gwd_anomalie_individual = sea_individual(40, station, gwd_anomalie)
Index(['2006-01-21', '2007-02-24', '2008-02-22', '2009-01-24', '2010-02-09',
'2013-01-06', '2018-02-12'],
dtype='object', name='BeginDate')
Index(['2006-01-21', '2007-02-24', '2008-02-22', '2009-01-24', '2010-02-09',
'2013-01-06', '2018-02-12'],
dtype='object', name='BeginDate')
In [12]:
p = sea_gwd_individual.sel(alt=slice(84, 95)).plot.contourf(
x="time", row="event", size=9, robust=True, levels=41, aspect=4
)
In [13]:
p = sea_gwd_anomalie_individual.sel(alt=slice(84, 95)).plot.contourf(
x="time", row="event", size=9, robust=True, levels=41, aspect=4
)
In [ ]: