Preprocess¶
The preprocessing steps consist of merging all retrieved files into one xarray dataset and extracting the spatial and temporal average of the event of interest.
Merge¶
Here it is shown how all retrieved files are loaded into one xarray dataset, for both SEAS5 and for ERA5.
SEAS5¶
All retrieved seasonal forecasts are loaded into one xarray dataset. The amount of files retrieved depends on the temporal extent of the extreme event that is being analyzed (i.e are you looking at a monthly average or a seasonal average?). For the Siberian heatwave, we have retrieved 105 files (one for each of the 35 years and for each of the three lead times, (see Retrieve). For the UK, we are able to use more forecasts, because the target month is shorter: one month as compared to three months for the Siberian example. We retrieved 5 leadtimes x 35 = 175 files.
Each netcdf file contains 25 ensemble members, hence has the dimensions lat, lon, number (25 ensembles). Here we create an xarray dataset that also contains the dimensions time (35 years) and leadtime (5 initialization months). To generate this, we loop over lead times, and open all 35 years of the lead time and then concatenate those leadtimes.
[1]:
##This is so variables get printed within jupyter
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
[2]:
import os
import sys
sys.path.insert(0, os.path.abspath('../../../'))
import src.cdsretrieve as retrieve
[3]:
os.chdir(os.path.abspath('../../../'))
os.getcwd() #print the working directory
[3]:
'C:\\Users\\Timo\\OneDrive - Loughborough University\\GitHub\\UNSEEN-open'
[4]:
import xarray as xr
import numpy as np
def merge_SEAS5(folder, target_months):
init_months, leadtimes = retrieve._get_init_months(target_months)
print('Lead time: ' + "%.2i" % init_months[0])
SEAS5_ld1 = xr.open_mfdataset(
folder + '*' + "%.2i" % init_months[0] + '.nc',
combine='by_coords') # Load the first lead time
SEAS5 = SEAS5_ld1 # Create the xarray dataset to concatenate over
for init_month in init_months[1:len(init_months)]: ## Remove the first that we already have
print(init_month)
SEAS5_ld = xr.open_mfdataset(
folder + '*' + "%.2i" % init_month + '.nc',
combine='by_coords')
SEAS5 = xr.concat([SEAS5, SEAS5_ld], dim='leadtime')
SEAS5 = SEAS5.assign_coords(leadtime = np.arange(len(init_months)) + 2) # assign leadtime coordinates
return(SEAS5)
[ ]:
SEAS5_Siberia = merge_SEAS5(folder='../Siberia_example/SEAS5/',
target_months=[3, 4, 5])
[8]:
SEAS5_Siberia
[8]:
xarray.Dataset
- latitude: 41
- leadtime: 3
- longitude: 132
- number: 51
- time: 117
- time(time)datetime64[ns]1982-03-01 ... 2020-05-01
- long_name :
- time
array(['1982-03-01T00:00:00.000000000', '1982-04-01T00:00:00.000000000', '1982-05-01T00:00:00.000000000', '1983-03-01T00:00:00.000000000', '1983-04-01T00:00:00.000000000', '1983-05-01T00:00:00.000000000', '1984-03-01T00:00:00.000000000', '1984-04-01T00:00:00.000000000', '1984-05-01T00:00:00.000000000', '1985-03-01T00:00:00.000000000', '1985-04-01T00:00:00.000000000', '1985-05-01T00:00:00.000000000', '1986-03-01T00:00:00.000000000', '1986-04-01T00:00:00.000000000', '1986-05-01T00:00:00.000000000', '1987-03-01T00:00:00.000000000', '1987-04-01T00:00:00.000000000', '1987-05-01T00:00:00.000000000', '1988-03-01T00:00:00.000000000', '1988-04-01T00:00:00.000000000', '1988-05-01T00:00:00.000000000', '1989-03-01T00:00:00.000000000', '1989-04-01T00:00:00.000000000', '1989-05-01T00:00:00.000000000', '1990-03-01T00:00:00.000000000', '1990-04-01T00:00:00.000000000', '1990-05-01T00:00:00.000000000', '1991-03-01T00:00:00.000000000', '1991-04-01T00:00:00.000000000', '1991-05-01T00:00:00.000000000', '1992-03-01T00:00:00.000000000', '1992-04-01T00:00:00.000000000', '1992-05-01T00:00:00.000000000', '1993-03-01T00:00:00.000000000', '1993-04-01T00:00:00.000000000', '1993-05-01T00:00:00.000000000', '1994-03-01T00:00:00.000000000', '1994-04-01T00:00:00.000000000', '1994-05-01T00:00:00.000000000', '1995-03-01T00:00:00.000000000', '1995-04-01T00:00:00.000000000', '1995-05-01T00:00:00.000000000', '1996-03-01T00:00:00.000000000', '1996-04-01T00:00:00.000000000', '1996-05-01T00:00:00.000000000', '1997-03-01T00:00:00.000000000', '1997-04-01T00:00:00.000000000', '1997-05-01T00:00:00.000000000', '1998-03-01T00:00:00.000000000', '1998-04-01T00:00:00.000000000', '1998-05-01T00:00:00.000000000', '1999-03-01T00:00:00.000000000', '1999-04-01T00:00:00.000000000', '1999-05-01T00:00:00.000000000', '2000-03-01T00:00:00.000000000', '2000-04-01T00:00:00.000000000', '2000-05-01T00:00:00.000000000', '2001-03-01T00:00:00.000000000', '2001-04-01T00:00:00.000000000', '2001-05-01T00:00:00.000000000', '2002-03-01T00:00:00.000000000', '2002-04-01T00:00:00.000000000', '2002-05-01T00:00:00.000000000', '2003-03-01T00:00:00.000000000', '2003-04-01T00:00:00.000000000', '2003-05-01T00:00:00.000000000', '2004-03-01T00:00:00.000000000', '2004-04-01T00:00:00.000000000', '2004-05-01T00:00:00.000000000', '2005-03-01T00:00:00.000000000', '2005-04-01T00:00:00.000000000', '2005-05-01T00:00:00.000000000', '2006-03-01T00:00:00.000000000', '2006-04-01T00:00:00.000000000', '2006-05-01T00:00:00.000000000', '2007-03-01T00:00:00.000000000', '2007-04-01T00:00:00.000000000', '2007-05-01T00:00:00.000000000', '2008-03-01T00:00:00.000000000', '2008-04-01T00:00:00.000000000', '2008-05-01T00:00:00.000000000', '2009-03-01T00:00:00.000000000', '2009-04-01T00:00:00.000000000', '2009-05-01T00:00:00.000000000', '2010-03-01T00:00:00.000000000', '2010-04-01T00:00:00.000000000', '2010-05-01T00:00:00.000000000', '2011-03-01T00:00:00.000000000', '2011-04-01T00:00:00.000000000', '2011-05-01T00:00:00.000000000', '2012-03-01T00:00:00.000000000', '2012-04-01T00:00:00.000000000', '2012-05-01T00:00:00.000000000', '2013-03-01T00:00:00.000000000', '2013-04-01T00:00:00.000000000', '2013-05-01T00:00:00.000000000', '2014-03-01T00:00:00.000000000', '2014-04-01T00:00:00.000000000', '2014-05-01T00:00:00.000000000', '2015-03-01T00:00:00.000000000', '2015-04-01T00:00:00.000000000', '2015-05-01T00:00:00.000000000', '2016-03-01T00:00:00.000000000', '2016-04-01T00:00:00.000000000', '2016-05-01T00:00:00.000000000', '2017-03-01T00:00:00.000000000', '2017-04-01T00:00:00.000000000', '2017-05-01T00:00:00.000000000', '2018-03-01T00:00:00.000000000', '2018-04-01T00:00:00.000000000', '2018-05-01T00:00:00.000000000', '2019-03-01T00:00:00.000000000', '2019-04-01T00:00:00.000000000', '2019-05-01T00:00:00.000000000', '2020-03-01T00:00:00.000000000', '2020-04-01T00:00:00.000000000', '2020-05-01T00:00:00.000000000'], dtype='datetime64[ns]') - latitude(latitude)float3270.0 69.0 68.0 ... 32.0 31.0 30.0
- units :
- degrees_north
- long_name :
- latitude
array([70., 69., 68., 67., 66., 65., 64., 63., 62., 61., 60., 59., 58., 57., 56., 55., 54., 53., 52., 51., 50., 49., 48., 47., 46., 45., 44., 43., 42., 41., 40., 39., 38., 37., 36., 35., 34., 33., 32., 31., 30.], dtype=float32) - number(number)int640 1 2 3 4 5 6 ... 45 46 47 48 49 50
- long_name :
- ensemble_member
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]) - longitude(longitude)float32-11.0 -10.0 -9.0 ... 119.0 120.0
- units :
- degrees_east
- long_name :
- longitude
array([-11., -10., -9., -8., -7., -6., -5., -4., -3., -2., -1., 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120.], dtype=float32) - leadtime(leadtime)int642 3 4
array([2, 3, 4])
- t2m(leadtime, time, number, latitude, longitude)float32dask.array<chunksize=(1, 3, 51, 41, 132), meta=np.ndarray>
- units :
- K
- long_name :
- 2 metre temperature
Array Chunk Bytes 387.52 MB 3.31 MB Shape (3, 117, 51, 41, 132) (1, 3, 51, 41, 132) Count 887 Tasks 117 Chunks Type float32 numpy.ndarray - d2m(leadtime, time, number, latitude, longitude)float32dask.array<chunksize=(1, 3, 51, 41, 132), meta=np.ndarray>
- units :
- K
- long_name :
- 2 metre dewpoint temperature
Array Chunk Bytes 387.52 MB 3.31 MB Shape (3, 117, 51, 41, 132) (1, 3, 51, 41, 132) Count 887 Tasks 117 Chunks Type float32 numpy.ndarray
- Conventions :
- CF-1.6
- history :
- 2020-09-08 09:33:24 GMT by grib_to_netcdf-2.16.0: /opt/ecmwf/eccodes/bin/grib_to_netcdf -S param -o /cache/data1/adaptor.mars.external-1599557575.5884402-22815-11-a3e13f38-976c-41fd-bc34-d70ac6258b8d.nc /cache/tmp/a3e13f38-976c-41fd-bc34-d70ac6258b8d-adaptor.mars.external-1599557575.5891798-22815-3-tmp.grib
You can for example select a the lat, long, time, ensemble member and lead time as follows (add .load() to see the values):
[ ]:
SEAS5_Siberia.sel(latitude=60,
longitude=-10,
time='2000-03',
number=26,
leadtime=3).load()
We can repeat this for the UK example, where just February is the target month:
[10]:
SEAS5_UK = merge_SEAS5(folder = '../UK_example/SEAS5/', target_months = [2])
Lead time: 01
12
11
10
9
The SEAS5 total precipitation rate is in m/s. You can easily convert this and change the attributes. Click on the show/hide attributes button to see the assigned attributes.
[11]:
SEAS5_UK['tprate'] = SEAS5_UK['tprate'] * 1000 * 3600 * 24 ## From m/s to mm/d
SEAS5_UK['tprate'].attrs = {'long_name': 'rainfall',
'units': 'mm/day',
'standard_name': 'thickness_of_rainfall_amount'}
SEAS5_UK
[11]:
xarray.Dataset
- latitude: 11
- leadtime: 5
- longitude: 14
- number: 25
- time: 35
- time(time)datetime64[ns]1982-02-01 ... 2016-02-01
- long_name :
- time
array(['1982-02-01T00:00:00.000000000', '1983-02-01T00:00:00.000000000', '1984-02-01T00:00:00.000000000', '1985-02-01T00:00:00.000000000', '1986-02-01T00:00:00.000000000', '1987-02-01T00:00:00.000000000', '1988-02-01T00:00:00.000000000', '1989-02-01T00:00:00.000000000', '1990-02-01T00:00:00.000000000', '1991-02-01T00:00:00.000000000', '1992-02-01T00:00:00.000000000', '1993-02-01T00:00:00.000000000', '1994-02-01T00:00:00.000000000', '1995-02-01T00:00:00.000000000', '1996-02-01T00:00:00.000000000', '1997-02-01T00:00:00.000000000', '1998-02-01T00:00:00.000000000', '1999-02-01T00:00:00.000000000', '2000-02-01T00:00:00.000000000', '2001-02-01T00:00:00.000000000', '2002-02-01T00:00:00.000000000', '2003-02-01T00:00:00.000000000', '2004-02-01T00:00:00.000000000', '2005-02-01T00:00:00.000000000', '2006-02-01T00:00:00.000000000', '2007-02-01T00:00:00.000000000', '2008-02-01T00:00:00.000000000', '2009-02-01T00:00:00.000000000', '2010-02-01T00:00:00.000000000', '2011-02-01T00:00:00.000000000', '2012-02-01T00:00:00.000000000', '2013-02-01T00:00:00.000000000', '2014-02-01T00:00:00.000000000', '2015-02-01T00:00:00.000000000', '2016-02-01T00:00:00.000000000'], dtype='datetime64[ns]') - latitude(latitude)float3260.0 59.0 58.0 ... 52.0 51.0 50.0
- units :
- degrees_north
- long_name :
- latitude
array([60., 59., 58., 57., 56., 55., 54., 53., 52., 51., 50.], dtype=float32)
- number(number)int320 1 2 3 4 5 6 ... 19 20 21 22 23 24
- long_name :
- ensemble_member
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], dtype=int32) - longitude(longitude)float32-11.0 -10.0 -9.0 ... 0.0 1.0 2.0
- units :
- degrees_east
- long_name :
- longitude
array([-11., -10., -9., -8., -7., -6., -5., -4., -3., -2., -1., 0., 1., 2.], dtype=float32) - leadtime(leadtime)int642 3 4 5 6
array([2, 3, 4, 5, 6])
- tprate(leadtime, time, number, latitude, longitude)float32dask.array<chunksize=(1, 1, 25, 11, 14), meta=np.ndarray>
- long_name :
- rainfall
- units :
- mm/day
- standard_name :
- thickness_of_rainfall_amount
Array Chunk Bytes 2.69 MB 15.40 kB Shape (5, 35, 25, 11, 14) (1, 1, 25, 11, 14) Count 1715 Tasks 175 Chunks Type float32 numpy.ndarray
- Conventions :
- CF-1.6
- history :
- 2020-05-13 14:49:43 GMT by grib_to_netcdf-2.16.0: /opt/ecmwf/eccodes/bin/grib_to_netcdf -S param -o /cache/data7/adaptor.mars.external-1589381366.1540039-11561-3-ad31a097-72e2-45ce-a565-55c62502f358.nc /cache/tmp/ad31a097-72e2-45ce-a565-55c62502f358-adaptor.mars.external-1589381366.1545565-11561-1-tmp.grib
ERA5¶
For each year a netcdf file is downloaded. They are named ERA5_yyyy, for example ERA5_1981. Therefore, we can load ERA5 by combining all downloaded years:
[12]:
ERA5_Siberia = xr.open_mfdataset('../Siberia_example/ERA5/ERA5_????.nc',combine='by_coords') ## open the data
ERA5_Siberia
[12]:
xarray.Dataset
- latitude: 41
- longitude: 132
- time: 126
- latitude(latitude)float3270.0 69.0 68.0 ... 32.0 31.0 30.0
- units :
- degrees_north
- long_name :
- latitude
array([70., 69., 68., 67., 66., 65., 64., 63., 62., 61., 60., 59., 58., 57., 56., 55., 54., 53., 52., 51., 50., 49., 48., 47., 46., 45., 44., 43., 42., 41., 40., 39., 38., 37., 36., 35., 34., 33., 32., 31., 30.], dtype=float32) - longitude(longitude)float32-11.0 -10.0 -9.0 ... 119.0 120.0
- units :
- degrees_east
- long_name :
- longitude
array([-11., -10., -9., -8., -7., -6., -5., -4., -3., -2., -1., 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120.], dtype=float32) - time(time)datetime64[ns]1979-03-01 ... 2020-05-01
- long_name :
- time
array(['1979-03-01T00:00:00.000000000', '1979-04-01T00:00:00.000000000', '1979-05-01T00:00:00.000000000', '1980-03-01T00:00:00.000000000', '1980-04-01T00:00:00.000000000', '1980-05-01T00:00:00.000000000', '1981-03-01T00:00:00.000000000', '1981-04-01T00:00:00.000000000', '1981-05-01T00:00:00.000000000', '1982-03-01T00:00:00.000000000', '1982-04-01T00:00:00.000000000', '1982-05-01T00:00:00.000000000', '1983-03-01T00:00:00.000000000', '1983-04-01T00:00:00.000000000', '1983-05-01T00:00:00.000000000', '1984-03-01T00:00:00.000000000', '1984-04-01T00:00:00.000000000', '1984-05-01T00:00:00.000000000', '1985-03-01T00:00:00.000000000', '1985-04-01T00:00:00.000000000', '1985-05-01T00:00:00.000000000', '1986-03-01T00:00:00.000000000', '1986-04-01T00:00:00.000000000', '1986-05-01T00:00:00.000000000', '1987-03-01T00:00:00.000000000', '1987-04-01T00:00:00.000000000', '1987-05-01T00:00:00.000000000', '1988-03-01T00:00:00.000000000', '1988-04-01T00:00:00.000000000', '1988-05-01T00:00:00.000000000', '1989-03-01T00:00:00.000000000', '1989-04-01T00:00:00.000000000', '1989-05-01T00:00:00.000000000', '1990-03-01T00:00:00.000000000', '1990-04-01T00:00:00.000000000', '1990-05-01T00:00:00.000000000', '1991-03-01T00:00:00.000000000', '1991-04-01T00:00:00.000000000', '1991-05-01T00:00:00.000000000', '1992-03-01T00:00:00.000000000', '1992-04-01T00:00:00.000000000', '1992-05-01T00:00:00.000000000', '1993-03-01T00:00:00.000000000', '1993-04-01T00:00:00.000000000', '1993-05-01T00:00:00.000000000', '1994-03-01T00:00:00.000000000', '1994-04-01T00:00:00.000000000', '1994-05-01T00:00:00.000000000', '1995-03-01T00:00:00.000000000', '1995-04-01T00:00:00.000000000', '1995-05-01T00:00:00.000000000', '1996-03-01T00:00:00.000000000', '1996-04-01T00:00:00.000000000', '1996-05-01T00:00:00.000000000', '1997-03-01T00:00:00.000000000', '1997-04-01T00:00:00.000000000', '1997-05-01T00:00:00.000000000', '1998-03-01T00:00:00.000000000', '1998-04-01T00:00:00.000000000', '1998-05-01T00:00:00.000000000', '1999-03-01T00:00:00.000000000', '1999-04-01T00:00:00.000000000', '1999-05-01T00:00:00.000000000', '2000-03-01T00:00:00.000000000', '2000-04-01T00:00:00.000000000', '2000-05-01T00:00:00.000000000', '2001-03-01T00:00:00.000000000', '2001-04-01T00:00:00.000000000', '2001-05-01T00:00:00.000000000', '2002-03-01T00:00:00.000000000', '2002-04-01T00:00:00.000000000', '2002-05-01T00:00:00.000000000', '2003-03-01T00:00:00.000000000', '2003-04-01T00:00:00.000000000', '2003-05-01T00:00:00.000000000', '2004-03-01T00:00:00.000000000', '2004-04-01T00:00:00.000000000', '2004-05-01T00:00:00.000000000', '2005-03-01T00:00:00.000000000', '2005-04-01T00:00:00.000000000', '2005-05-01T00:00:00.000000000', '2006-03-01T00:00:00.000000000', '2006-04-01T00:00:00.000000000', '2006-05-01T00:00:00.000000000', '2007-03-01T00:00:00.000000000', '2007-04-01T00:00:00.000000000', '2007-05-01T00:00:00.000000000', '2008-03-01T00:00:00.000000000', '2008-04-01T00:00:00.000000000', '2008-05-01T00:00:00.000000000', '2009-03-01T00:00:00.000000000', '2009-04-01T00:00:00.000000000', '2009-05-01T00:00:00.000000000', '2010-03-01T00:00:00.000000000', '2010-04-01T00:00:00.000000000', '2010-05-01T00:00:00.000000000', '2011-03-01T00:00:00.000000000', '2011-04-01T00:00:00.000000000', '2011-05-01T00:00:00.000000000', '2012-03-01T00:00:00.000000000', '2012-04-01T00:00:00.000000000', '2012-05-01T00:00:00.000000000', '2013-03-01T00:00:00.000000000', '2013-04-01T00:00:00.000000000', '2013-05-01T00:00:00.000000000', '2014-03-01T00:00:00.000000000', '2014-04-01T00:00:00.000000000', '2014-05-01T00:00:00.000000000', '2015-03-01T00:00:00.000000000', '2015-04-01T00:00:00.000000000', '2015-05-01T00:00:00.000000000', '2016-03-01T00:00:00.000000000', '2016-04-01T00:00:00.000000000', '2016-05-01T00:00:00.000000000', '2017-03-01T00:00:00.000000000', '2017-04-01T00:00:00.000000000', '2017-05-01T00:00:00.000000000', '2018-03-01T00:00:00.000000000', '2018-04-01T00:00:00.000000000', '2018-05-01T00:00:00.000000000', '2019-03-01T00:00:00.000000000', '2019-04-01T00:00:00.000000000', '2019-05-01T00:00:00.000000000', '2020-03-01T00:00:00.000000000', '2020-04-01T00:00:00.000000000', '2020-05-01T00:00:00.000000000'], dtype='datetime64[ns]')
- t2m(time, latitude, longitude)float32dask.array<chunksize=(3, 41, 132), meta=np.ndarray>
- units :
- K
- long_name :
- 2 metre temperature
Array Chunk Bytes 2.73 MB 64.94 kB Shape (126, 41, 132) (3, 41, 132) Count 126 Tasks 42 Chunks Type float32 numpy.ndarray - d2m(time, latitude, longitude)float32dask.array<chunksize=(3, 41, 132), meta=np.ndarray>
- units :
- K
- long_name :
- 2 metre dewpoint temperature
Array Chunk Bytes 2.73 MB 64.94 kB Shape (126, 41, 132) (3, 41, 132) Count 126 Tasks 42 Chunks Type float32 numpy.ndarray
- Conventions :
- CF-1.6
- history :
- 2020-09-08 13:26:08 GMT by grib_to_netcdf-2.16.0: /opt/ecmwf/eccodes/bin/grib_to_netcdf -S param -o /cache/data5/adaptor.mars.internal-1599571563.605006-1463-33-b77abfe4-0299-4cba-9b8c-c5a877f44943.nc /cache/tmp/b77abfe4-0299-4cba-9b8c-c5a877f44943-adaptor.mars.internal-1599571563.6055787-1463-13-tmp.grib
[13]:
ERA5_UK = xr.open_mfdataset('../UK_example/ERA5/ERA5_????.nc',combine='by_coords') ## open the data
ERA5_UK
[13]:
xarray.Dataset
- latitude: 11
- longitude: 14
- time: 42
- latitude(latitude)float3260.0 59.0 58.0 ... 52.0 51.0 50.0
- units :
- degrees_north
- long_name :
- latitude
array([60., 59., 58., 57., 56., 55., 54., 53., 52., 51., 50.], dtype=float32)
- longitude(longitude)float32-11.0 -10.0 -9.0 ... 0.0 1.0 2.0
- units :
- degrees_east
- long_name :
- longitude
array([-11., -10., -9., -8., -7., -6., -5., -4., -3., -2., -1., 0., 1., 2.], dtype=float32) - time(time)datetime64[ns]1979-02-01 ... 2020-02-01
- long_name :
- time
array(['1979-02-01T00:00:00.000000000', '1980-02-01T00:00:00.000000000', '1981-02-01T00:00:00.000000000', '1982-02-01T00:00:00.000000000', '1983-02-01T00:00:00.000000000', '1984-02-01T00:00:00.000000000', '1985-02-01T00:00:00.000000000', '1986-02-01T00:00:00.000000000', '1987-02-01T00:00:00.000000000', '1988-02-01T00:00:00.000000000', '1989-02-01T00:00:00.000000000', '1990-02-01T00:00:00.000000000', '1991-02-01T00:00:00.000000000', '1992-02-01T00:00:00.000000000', '1993-02-01T00:00:00.000000000', '1994-02-01T00:00:00.000000000', '1995-02-01T00:00:00.000000000', '1996-02-01T00:00:00.000000000', '1997-02-01T00:00:00.000000000', '1998-02-01T00:00:00.000000000', '1999-02-01T00:00:00.000000000', '2000-02-01T00:00:00.000000000', '2001-02-01T00:00:00.000000000', '2002-02-01T00:00:00.000000000', '2003-02-01T00:00:00.000000000', '2004-02-01T00:00:00.000000000', '2005-02-01T00:00:00.000000000', '2006-02-01T00:00:00.000000000', '2007-02-01T00:00:00.000000000', '2008-02-01T00:00:00.000000000', '2009-02-01T00:00:00.000000000', '2010-02-01T00:00:00.000000000', '2011-02-01T00:00:00.000000000', '2012-02-01T00:00:00.000000000', '2013-02-01T00:00:00.000000000', '2014-02-01T00:00:00.000000000', '2015-02-01T00:00:00.000000000', '2016-02-01T00:00:00.000000000', '2017-02-01T00:00:00.000000000', '2018-02-01T00:00:00.000000000', '2019-02-01T00:00:00.000000000', '2020-02-01T00:00:00.000000000'], dtype='datetime64[ns]')
- tp(time, latitude, longitude)float32dask.array<chunksize=(1, 11, 14), meta=np.ndarray>
- units :
- m
- long_name :
- Total precipitation
Array Chunk Bytes 25.87 kB 616 B Shape (42, 11, 14) (1, 11, 14) Count 126 Tasks 42 Chunks Type float32 numpy.ndarray
- Conventions :
- CF-1.6
- history :
- 2020-09-08 13:36:54 GMT by grib_to_netcdf-2.16.0: /opt/ecmwf/eccodes/bin/grib_to_netcdf -S param -o /cache/data6/adaptor.mars.internal-1599572211.936053-12058-17-60c5a89d-0507-4593-8870-aacff3b72426.nc /cache/tmp/60c5a89d-0507-4593-8870-aacff3b72426-adaptor.mars.internal-1599572211.9366653-12058-6-tmp.grib
Event definition¶
Time selection¶
For the UK, the event of interest is UK February average precipitation. Since we download monthly averages, we do not have to do any preprocessing along the time dimension here. For the Siberian heatwave, we are interested in the March-May average. Therefore we need to take the seasonal average of the monthly timeseries. We cannot take the simple mean of the three months, because they have a different number of days in the months, see this example. Therefore we take a weighted average:
[14]:
month_length = SEAS5_Siberia.time.dt.days_in_month
month_length
[14]:
xarray.DataArray
'days_in_month'
- time: 117
- 31 30 31 31 30 31 31 30 31 31 30 ... 30 31 31 30 31 31 30 31 31 30 31
array([31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31, 31, 30, 31]) - time(time)datetime64[ns]1982-03-01 ... 2020-05-01
- long_name :
- time
array(['1982-03-01T00:00:00.000000000', '1982-04-01T00:00:00.000000000', '1982-05-01T00:00:00.000000000', '1983-03-01T00:00:00.000000000', '1983-04-01T00:00:00.000000000', '1983-05-01T00:00:00.000000000', '1984-03-01T00:00:00.000000000', '1984-04-01T00:00:00.000000000', '1984-05-01T00:00:00.000000000', '1985-03-01T00:00:00.000000000', '1985-04-01T00:00:00.000000000', '1985-05-01T00:00:00.000000000', '1986-03-01T00:00:00.000000000', '1986-04-01T00:00:00.000000000', '1986-05-01T00:00:00.000000000', '1987-03-01T00:00:00.000000000', '1987-04-01T00:00:00.000000000', '1987-05-01T00:00:00.000000000', '1988-03-01T00:00:00.000000000', '1988-04-01T00:00:00.000000000', '1988-05-01T00:00:00.000000000', '1989-03-01T00:00:00.000000000', '1989-04-01T00:00:00.000000000', '1989-05-01T00:00:00.000000000', '1990-03-01T00:00:00.000000000', '1990-04-01T00:00:00.000000000', '1990-05-01T00:00:00.000000000', '1991-03-01T00:00:00.000000000', '1991-04-01T00:00:00.000000000', '1991-05-01T00:00:00.000000000', '1992-03-01T00:00:00.000000000', '1992-04-01T00:00:00.000000000', '1992-05-01T00:00:00.000000000', '1993-03-01T00:00:00.000000000', '1993-04-01T00:00:00.000000000', '1993-05-01T00:00:00.000000000', '1994-03-01T00:00:00.000000000', '1994-04-01T00:00:00.000000000', '1994-05-01T00:00:00.000000000', '1995-03-01T00:00:00.000000000', '1995-04-01T00:00:00.000000000', '1995-05-01T00:00:00.000000000', '1996-03-01T00:00:00.000000000', '1996-04-01T00:00:00.000000000', '1996-05-01T00:00:00.000000000', '1997-03-01T00:00:00.000000000', '1997-04-01T00:00:00.000000000', '1997-05-01T00:00:00.000000000', '1998-03-01T00:00:00.000000000', '1998-04-01T00:00:00.000000000', '1998-05-01T00:00:00.000000000', '1999-03-01T00:00:00.000000000', '1999-04-01T00:00:00.000000000', '1999-05-01T00:00:00.000000000', '2000-03-01T00:00:00.000000000', '2000-04-01T00:00:00.000000000', '2000-05-01T00:00:00.000000000', '2001-03-01T00:00:00.000000000', '2001-04-01T00:00:00.000000000', '2001-05-01T00:00:00.000000000', '2002-03-01T00:00:00.000000000', '2002-04-01T00:00:00.000000000', '2002-05-01T00:00:00.000000000', '2003-03-01T00:00:00.000000000', '2003-04-01T00:00:00.000000000', '2003-05-01T00:00:00.000000000', '2004-03-01T00:00:00.000000000', '2004-04-01T00:00:00.000000000', '2004-05-01T00:00:00.000000000', '2005-03-01T00:00:00.000000000', '2005-04-01T00:00:00.000000000', '2005-05-01T00:00:00.000000000', '2006-03-01T00:00:00.000000000', '2006-04-01T00:00:00.000000000', '2006-05-01T00:00:00.000000000', '2007-03-01T00:00:00.000000000', '2007-04-01T00:00:00.000000000', '2007-05-01T00:00:00.000000000', '2008-03-01T00:00:00.000000000', '2008-04-01T00:00:00.000000000', '2008-05-01T00:00:00.000000000', '2009-03-01T00:00:00.000000000', '2009-04-01T00:00:00.000000000', '2009-05-01T00:00:00.000000000', '2010-03-01T00:00:00.000000000', '2010-04-01T00:00:00.000000000', '2010-05-01T00:00:00.000000000', '2011-03-01T00:00:00.000000000', '2011-04-01T00:00:00.000000000', '2011-05-01T00:00:00.000000000', '2012-03-01T00:00:00.000000000', '2012-04-01T00:00:00.000000000', '2012-05-01T00:00:00.000000000', '2013-03-01T00:00:00.000000000', '2013-04-01T00:00:00.000000000', '2013-05-01T00:00:00.000000000', '2014-03-01T00:00:00.000000000', '2014-04-01T00:00:00.000000000', '2014-05-01T00:00:00.000000000', '2015-03-01T00:00:00.000000000', '2015-04-01T00:00:00.000000000', '2015-05-01T00:00:00.000000000', '2016-03-01T00:00:00.000000000', '2016-04-01T00:00:00.000000000', '2016-05-01T00:00:00.000000000', '2017-03-01T00:00:00.000000000', '2017-04-01T00:00:00.000000000', '2017-05-01T00:00:00.000000000', '2018-03-01T00:00:00.000000000', '2018-04-01T00:00:00.000000000', '2018-05-01T00:00:00.000000000', '2019-03-01T00:00:00.000000000', '2019-04-01T00:00:00.000000000', '2019-05-01T00:00:00.000000000', '2020-03-01T00:00:00.000000000', '2020-04-01T00:00:00.000000000', '2020-05-01T00:00:00.000000000'], dtype='datetime64[ns]')
[15]:
# Calculate the weights by grouping by 'time.season'.
weights = month_length.groupby('time.year') / month_length.groupby('time.year').sum()
weights
[15]:
xarray.DataArray
'days_in_month'
- time: 117
- 0.337 0.3261 0.337 0.337 0.3261 ... 0.3261 0.337 0.337 0.3261 0.337
array([0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652, 0.33695652, 0.32608696, 0.33695652]) - time(time)datetime64[ns]1982-03-01 ... 2020-05-01
- long_name :
- time
array(['1982-03-01T00:00:00.000000000', '1982-04-01T00:00:00.000000000', '1982-05-01T00:00:00.000000000', '1983-03-01T00:00:00.000000000', '1983-04-01T00:00:00.000000000', '1983-05-01T00:00:00.000000000', '1984-03-01T00:00:00.000000000', '1984-04-01T00:00:00.000000000', '1984-05-01T00:00:00.000000000', '1985-03-01T00:00:00.000000000', '1985-04-01T00:00:00.000000000', '1985-05-01T00:00:00.000000000', '1986-03-01T00:00:00.000000000', '1986-04-01T00:00:00.000000000', '1986-05-01T00:00:00.000000000', '1987-03-01T00:00:00.000000000', '1987-04-01T00:00:00.000000000', '1987-05-01T00:00:00.000000000', '1988-03-01T00:00:00.000000000', '1988-04-01T00:00:00.000000000', '1988-05-01T00:00:00.000000000', '1989-03-01T00:00:00.000000000', '1989-04-01T00:00:00.000000000', '1989-05-01T00:00:00.000000000', '1990-03-01T00:00:00.000000000', '1990-04-01T00:00:00.000000000', '1990-05-01T00:00:00.000000000', '1991-03-01T00:00:00.000000000', '1991-04-01T00:00:00.000000000', '1991-05-01T00:00:00.000000000', '1992-03-01T00:00:00.000000000', '1992-04-01T00:00:00.000000000', '1992-05-01T00:00:00.000000000', '1993-03-01T00:00:00.000000000', '1993-04-01T00:00:00.000000000', '1993-05-01T00:00:00.000000000', '1994-03-01T00:00:00.000000000', '1994-04-01T00:00:00.000000000', '1994-05-01T00:00:00.000000000', '1995-03-01T00:00:00.000000000', '1995-04-01T00:00:00.000000000', '1995-05-01T00:00:00.000000000', '1996-03-01T00:00:00.000000000', '1996-04-01T00:00:00.000000000', '1996-05-01T00:00:00.000000000', '1997-03-01T00:00:00.000000000', '1997-04-01T00:00:00.000000000', '1997-05-01T00:00:00.000000000', '1998-03-01T00:00:00.000000000', '1998-04-01T00:00:00.000000000', '1998-05-01T00:00:00.000000000', '1999-03-01T00:00:00.000000000', '1999-04-01T00:00:00.000000000', '1999-05-01T00:00:00.000000000', '2000-03-01T00:00:00.000000000', '2000-04-01T00:00:00.000000000', '2000-05-01T00:00:00.000000000', '2001-03-01T00:00:00.000000000', '2001-04-01T00:00:00.000000000', '2001-05-01T00:00:00.000000000', '2002-03-01T00:00:00.000000000', '2002-04-01T00:00:00.000000000', '2002-05-01T00:00:00.000000000', '2003-03-01T00:00:00.000000000', '2003-04-01T00:00:00.000000000', '2003-05-01T00:00:00.000000000', '2004-03-01T00:00:00.000000000', '2004-04-01T00:00:00.000000000', '2004-05-01T00:00:00.000000000', '2005-03-01T00:00:00.000000000', '2005-04-01T00:00:00.000000000', '2005-05-01T00:00:00.000000000', '2006-03-01T00:00:00.000000000', '2006-04-01T00:00:00.000000000', '2006-05-01T00:00:00.000000000', '2007-03-01T00:00:00.000000000', '2007-04-01T00:00:00.000000000', '2007-05-01T00:00:00.000000000', '2008-03-01T00:00:00.000000000', '2008-04-01T00:00:00.000000000', '2008-05-01T00:00:00.000000000', '2009-03-01T00:00:00.000000000', '2009-04-01T00:00:00.000000000', '2009-05-01T00:00:00.000000000', '2010-03-01T00:00:00.000000000', '2010-04-01T00:00:00.000000000', '2010-05-01T00:00:00.000000000', '2011-03-01T00:00:00.000000000', '2011-04-01T00:00:00.000000000', '2011-05-01T00:00:00.000000000', '2012-03-01T00:00:00.000000000', '2012-04-01T00:00:00.000000000', '2012-05-01T00:00:00.000000000', '2013-03-01T00:00:00.000000000', '2013-04-01T00:00:00.000000000', '2013-05-01T00:00:00.000000000', '2014-03-01T00:00:00.000000000', '2014-04-01T00:00:00.000000000', '2014-05-01T00:00:00.000000000', '2015-03-01T00:00:00.000000000', '2015-04-01T00:00:00.000000000', '2015-05-01T00:00:00.000000000', '2016-03-01T00:00:00.000000000', '2016-04-01T00:00:00.000000000', '2016-05-01T00:00:00.000000000', '2017-03-01T00:00:00.000000000', '2017-04-01T00:00:00.000000000', '2017-05-01T00:00:00.000000000', '2018-03-01T00:00:00.000000000', '2018-04-01T00:00:00.000000000', '2018-05-01T00:00:00.000000000', '2019-03-01T00:00:00.000000000', '2019-04-01T00:00:00.000000000', '2019-05-01T00:00:00.000000000', '2020-03-01T00:00:00.000000000', '2020-04-01T00:00:00.000000000', '2020-05-01T00:00:00.000000000'], dtype='datetime64[ns]') - year(time)int641982 1982 1982 ... 2020 2020 2020
array([1982, 1982, 1982, 1983, 1983, 1983, 1984, 1984, 1984, 1985, 1985, 1985, 1986, 1986, 1986, 1987, 1987, 1987, 1988, 1988, 1988, 1989, 1989, 1989, 1990, 1990, 1990, 1991, 1991, 1991, 1992, 1992, 1992, 1993, 1993, 1993, 1994, 1994, 1994, 1995, 1995, 1995, 1996, 1996, 1996, 1997, 1997, 1997, 1998, 1998, 1998, 1999, 1999, 1999, 2000, 2000, 2000, 2001, 2001, 2001, 2002, 2002, 2002, 2003, 2003, 2003, 2004, 2004, 2004, 2005, 2005, 2005, 2006, 2006, 2006, 2007, 2007, 2007, 2008, 2008, 2008, 2009, 2009, 2009, 2010, 2010, 2010, 2011, 2011, 2011, 2012, 2012, 2012, 2013, 2013, 2013, 2014, 2014, 2014, 2015, 2015, 2015, 2016, 2016, 2016, 2017, 2017, 2017, 2018, 2018, 2018, 2019, 2019, 2019, 2020, 2020, 2020])
[16]:
# Test that the sum of the weights for the season is 1.0
np.testing.assert_allclose(weights.groupby('time.year').sum().values, np.ones(39)) ## the weight is one for each year
[30]:
# Calculate the weighted average
SEAS5_Siberia_weighted = (SEAS5_Siberia * weights).groupby('time.year').sum(dim='time', min_count = 3)
SEAS5_Siberia_weighted
[30]:
xarray.Dataset
- latitude: 41
- leadtime: 3
- longitude: 132
- number: 51
- year: 39
- leadtime(leadtime)int642 3 4
array([2, 3, 4])
- latitude(latitude)float3270.0 69.0 68.0 ... 32.0 31.0 30.0
- units :
- degrees_north
- long_name :
- latitude
array([70., 69., 68., 67., 66., 65., 64., 63., 62., 61., 60., 59., 58., 57., 56., 55., 54., 53., 52., 51., 50., 49., 48., 47., 46., 45., 44., 43., 42., 41., 40., 39., 38., 37., 36., 35., 34., 33., 32., 31., 30.], dtype=float32) - number(number)int640 1 2 3 4 5 6 ... 45 46 47 48 49 50
- long_name :
- ensemble_member
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]) - longitude(longitude)float32-11.0 -10.0 -9.0 ... 119.0 120.0
- units :
- degrees_east
- long_name :
- longitude
array([-11., -10., -9., -8., -7., -6., -5., -4., -3., -2., -1., 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120.], dtype=float32) - year(year)int641982 1983 1984 ... 2018 2019 2020
array([1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020])
- t2m(year, leadtime, number, latitude, longitude)float64268.8 269.4 269.8 ... 288.7 289.9
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..., 261.53465171, 261.49541424, 261.42802545], [270.45658045, 270.58849932, 270.77898341, ..., 262.04208507, 261.97272077, 261.9328809 ], [270.79295913, 270.96300042, 271.13901055, ..., 262.51901726, 262.46326679, 262.43545134], ..., [285.77619437, 285.97922184, 281.4484986 , ..., 283.44110074, 283.73295859, 283.96879014], [286.33802862, 285.7675141 , 274.54297008, ..., 284.5286016 , 284.64702075, 284.77585171], [286.79267485, 287.35202922, 275.54740209, ..., 283.88906363, 283.43960372, 284.62318984]], [[269.65833946, 269.82059114, 269.96195155, ..., 261.50137097, 261.65283203, 261.65924537], [270.45514347, 270.55032946, 270.7102764 , ..., 261.8522309 , 261.94138369, 262.08751745], [271.03518942, 271.12782155, 271.28776849, ..., 262.18311774, 262.22812553, 262.34417061], ..., [286.04266988, 286.26989779, 281.85792243, ..., 284.21988313, 284.5260431 , 284.80774656], [286.40065168, 286.15243331, 274.49307749, ..., 285.4626979 , 285.53848598, 285.6328815 ], [286.69893248, 287.215748 , 275.39704729, ..., 285.00986514, 284.62417503, 285.75246728]]], [[[269.63361624, 269.92423049, 270.12769052, ..., 259.91745592, 259.96462565, 259.8618927 ], [270.38940264, 270.63698379, 270.82497572, ..., 260.2762481 , 260.31311682, 260.36056867], [270.99676082, 271.24271691, 271.46427254, ..., 260.83055546, 260.79375607, 260.80125096], ..., [287.14729409, 287.17281408, 281.61405149, ..., 284.57436238, 284.58561043, 284.791143 ], [287.43472091, 286.66974076, 272.99914683, ..., 285.61914958, 285.51639258, 285.58796227], [287.87012316, 288.36948627, 273.63380366, ..., 284.86240686, 284.49651337, 285.52478492]], [[270.74638201, 270.94932822, 271.09642029, ..., 259.19024227, 259.14991594, 259.06394942], [271.36874091, 271.61198027, 271.76087321, ..., 259.41725225, 259.37519787, 259.41342561], [271.93496605, 272.17532913, 272.41339841, ..., 259.85985068, 259.90705474, 259.93438986], ..., [287.44328242, 287.38022149, 281.78762088, ..., 284.35671898, 284.53977004, 284.9054111 ], [287.64597486, 286.89695839, 274.91562852, ..., 285.78773167, 285.91673478, 286.05213331], [287.90841177, 288.22809667, 275.12800034, ..., 286.01304892, 285.5640428 , 286.47001781]], [[271.16478132, 271.37713922, 271.54244464, ..., 263.80586807, 263.79702891, 263.64949367], [271.68433513, 271.86363983, 272.02985515, ..., 264.08574776, 264.02179685, 263.9569523 ], [272.02640434, 272.2225992 , 272.40095686, ..., 264.66426086, 264.54396488, 264.46557086], ..., [286.13494375, 286.17098501, 280.66769077, ..., 285.25776606, 285.54686538, 285.85950072], [286.56084674, 285.76588274, 273.20012068, ..., 286.07636161, 286.12984997, 286.43562649], [286.98658421, 287.34512329, 273.90970346, ..., 285.26094122, 285.03325554, 286.18697589]], ..., [[268.92368084, 269.07489213, 269.17253212, ..., 259.83751645, 259.913653 , 259.94724124], [270.0595514 , 270.19366322, 270.27090786, ..., 259.95971447, 259.93671832, 259.9540999 ], [271.04721501, 271.09286499, 271.1620636 , ..., 260.37109541, 260.2984931 , 260.34910766], ..., [286.50934999, 286.68430395, 280.79426011, ..., 284.19371796, 284.31128228, 284.59494184], [286.82588295, 286.23899112, 273.42682814, ..., 285.9723534 , 285.9193908 , 286.02114138], [287.21891951, 287.73579042, 273.96557717, ..., 285.47012429, 285.08115188, 286.17530159]], [[271.3296134 , 271.55052351, 271.72476661, ..., 259.74678404, 259.68608624, 259.56615017], [271.86742302, 272.06877899, 272.24192976, ..., 260.14764222, 260.07023903, 260.06534145], [272.06580353, 272.3102752 , 272.55315731, ..., 260.54530699, 260.50725804, 260.51385697], ..., [287.93825266, 287.86485423, 279.99292556, ..., 284.12559675, 284.40058965, 284.626159 ], [288.15636544, 286.90952931, 271.02514217, ..., 285.45817699, 285.55548626, 285.63036247], [288.58792711, 288.75779757, 270.29120636, ..., 285.19180829, 284.98752893, 285.93219425]], [[270.12012581, 270.26921513, 270.41291942, ..., 257.76817736, 257.87240285, 257.8775308 ], [270.76040318, 270.9358965 , 271.05466959, ..., 258.00180319, 258.09825599, 258.27359987], [271.20120306, 271.33157448, 271.50358714, ..., 258.49354321, 258.63722594, 258.82817277], ..., [286.18709796, 286.3604932 , 281.00520059, ..., 284.18015754, 284.41231835, 284.82863385], [286.48317221, 285.92893119, 274.05155016, ..., 285.49128425, 285.53281502, 285.69833772], [286.94503187, 287.61717257, 274.18027496, ..., 285.05898517, 284.73168945, 286.00680442]]]]])
Or as function:
[31]:
def season_mean(ds, years, calendar='standard'):
# Make a DataArray with the number of days in each month, size = len(time)
month_length = ds.time.dt.days_in_month
# Calculate the weights by grouping by 'time.season'
weights = month_length.groupby('time.year') / month_length.groupby('time.year').sum()
# Test that the sum of the weights for each season is 1.0
np.testing.assert_allclose(weights.groupby('time.year').sum().values, np.ones(years))
# Calculate the weighted average
return (ds * weights).groupby('time.year').sum(dim='time', min_count = 3)
[33]:
ERA5_Siberia_weighted = season_mean(ERA5_Siberia, years = 42)
ERA5_Siberia_weighted
[33]:
xarray.Dataset
- latitude: 41
- longitude: 132
- year: 42
- latitude(latitude)float3270.0 69.0 68.0 ... 32.0 31.0 30.0
- units :
- degrees_north
- long_name :
- latitude
array([70., 69., 68., 67., 66., 65., 64., 63., 62., 61., 60., 59., 58., 57., 56., 55., 54., 53., 52., 51., 50., 49., 48., 47., 46., 45., 44., 43., 42., 41., 40., 39., 38., 37., 36., 35., 34., 33., 32., 31., 30.], dtype=float32) - longitude(longitude)float32-11.0 -10.0 -9.0 ... 119.0 120.0
- units :
- degrees_east
- long_name :
- longitude
array([-11., -10., -9., -8., -7., -6., -5., -4., -3., -2., -1., 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120.], dtype=float32) - year(year)int641979 1980 1981 ... 2018 2019 2020
array([1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020])
- t2m(year, latitude, longitude)float64270.5 270.9 271.2 ... 289.1 290.6
array([[[270.53845348, 270.90226215, 271.22064939, ..., 256.88882678, 256.76314014, 256.44316632], [271.21075605, 271.58743916, 272.10160761, ..., 258.50939145, 258.35444674, 258.34003249], [271.61115928, 271.98949233, 272.36104982, ..., 259.58987725, 259.61455818, 259.5761601 ], ..., [289.77074333, 289.52894526, 289.55374345, ..., 287.64928668, 287.54833089, 287.2222645 ], [289.92099298, 289.98857448, 285.8466024 , ..., 288.41151163, 288.06052001, 288.03151703], [290.27319634, 290.02542214, 288.90056776, ..., 287.6235335 , 286.62242956, 287.85563295]], [[272.47546884, 272.75750998, 273.00497967, ..., 259.44512807, 259.23107429, 258.71411664], [273.02040963, 273.30033277, 273.79829672, ..., 260.57161879, 260.22838526, 260.16624102], [273.60945362, 273.79806419, 274.03776418, ..., 260.85592552, 260.81758234, 260.90060823], ..., [290.29287952, 289.9930201 , 290.14131131, ..., 286.97752281, 286.94516953, 286.3993182 ], [290.32587765, 290.11277837, 286.06247015, ..., 287.71539572, 287.1034861 , 287.41468446], [290.48734449, 290.24152872, 288.26933123, ..., 286.70426178, 285.67877628, 287.07606639]], [[270.85066754, 271.2507709 , 271.57826896, ..., 260.76708255, 260.78421352, 260.35821367], [271.70493682, 272.06639066, 272.54059203, ..., 261.8014878 , 261.67763967, 261.59550576], [272.3355212 , 272.6204018 , 272.93550939, ..., 262.37989442, 262.33512115, 262.37479799], ..., [290.14578181, 289.86415399, 289.97064508, ..., 288.84671949, 288.58071369, 288.42606818], [290.29717852, 290.04429527, 285.92263396, ..., 289.40699602, 289.05557948, 289.11679575], [290.42925992, 290.23387179, 288.20256341, ..., 288.45622817, 287.61726512, 288.78954481]], ..., [[273.93356224, 274.21504477, 274.45377881, ..., 261.43229327, 261.43808763, 260.99563201], [274.19589532, 274.48848658, 275.03751042, ..., 262.39660761, 262.2738193 , 262.09600415], [274.55402507, 274.74373726, 275.02716728, ..., 263.07369481, 262.92978469, 262.88688626], ..., [289.97606924, 289.72782334, 289.86853094, ..., 290.54649751, 290.40096382, 290.259144 ], [290.08727828, 289.40257064, 285.77515876, ..., 291.61008155, 291.35451043, 291.37790116], [290.29168336, 289.63168368, 287.78337761, ..., 290.87840039, 290.21575497, 291.59742173]], [[272.928425 , 273.18573562, 273.41191433, ..., 263.66581245, 263.67493704, 263.23926511], [273.46121614, 273.77523572, 274.24082317, ..., 264.7070548 , 264.54024887, 264.37905254], [273.9498762 , 274.12044724, 274.33304463, ..., 265.20289181, 265.18147394, 265.24060589], ..., [290.73547264, 290.23868926, 291.9598936 , ..., 289.92534438, 289.92701754, 289.64130534], [290.66058582, 290.17593351, 288.22335451, ..., 290.76902406, 290.51999764, 290.59552599], [290.81727103, 290.13983685, 291.03420589, ..., 289.73828888, 288.9285421 , 290.25149636]], [[271.86548681, 272.1268692 , 272.38349152, ..., 264.56114479, 264.38686586, 263.81075569], [272.51668084, 272.7602277 , 273.16793027, ..., 265.4545993 , 265.15164831, 265.09558504], [273.1618513 , 273.33289702, 273.55358455, ..., 266.01881657, 265.8605491 , 265.85815994], ..., [291.15030073, 290.76279051, 291.74977875, ..., 290.25953077, 289.86484428, 289.58513608], [291.21759663, 290.39006275, 287.89739526, ..., 290.94006845, 290.63725049, 290.79393105], [291.49315975, 290.68042888, 289.96384828, ..., 290.03618721, 289.1397526 , 290.5711388 ]]]) - d2m(year, latitude, longitude)float64267.1 267.4 267.8 ... 283.5 284.7
array([[[267.08530592, 267.44651861, 267.78679027, ..., 252.60430344, 252.59938331, 252.54768007], [267.75876717, 268.13905567, 268.57483043, ..., 253.91470951, 253.85659807, 253.86594739], [268.28329468, 268.6458173 , 268.94433925, ..., 254.96331853, 254.90412206, 254.82613124], ..., [285.2836652 , 285.52568253, 282.18790071, ..., 281.98624122, 282.0235599 , 282.09372977], [285.81397015, 285.74176954, 275.2587466 , ..., 283.47724981, 283.29016611, 283.73143005], [286.27695631, 286.54966238, 276.11770232, ..., 283.09150099, 282.35261735, 283.54230234]], [[270.0560462 , 270.28085194, 270.50536512, ..., 254.73382121, 254.53267437, 254.22853171], [270.64404629, 270.90351403, 271.29315783, ..., 255.49363708, 255.13780428, 254.97790925], [271.23682536, 271.45255512, 271.69985099, ..., 255.72470076, 255.46575181, 255.3253693 ], ..., [286.34983129, 286.44495193, 284.45133408, ..., 281.90314152, 281.87271085, 281.82724165], [286.76781298, 286.81808405, 279.2684711 , ..., 283.19177644, 282.87829822, 283.40576769], [287.14303854, 287.42607183, 281.56492582, ..., 282.82251673, 281.96020176, 283.20188506]], [[267.48594533, 267.75006535, 267.98242519, ..., 255.7427743 , 255.73121328, 255.49709801], [268.30199897, 268.56740968, 268.89460257, ..., 256.59632376, 256.48026126, 256.30581798], [269.08470983, 269.28889532, 269.52602685, ..., 257.26749702, 257.06190341, 256.8050834 ], ..., [285.6106919 , 285.80548792, 283.16996533, ..., 282.80867336, 283.04773115, 283.06608018], [286.05431532, 286.18231102, 277.65601946, ..., 284.3706121 , 284.1623429 , 284.54197991], [286.42097672, 286.77872633, 279.5448071 , ..., 284.05219169, 283.33775728, 284.42656509]], ..., [[270.8889399 , 271.09284874, 271.30223382, ..., 256.22794724, 256.34163848, 256.16001826], [271.37453029, 271.57490307, 271.92048413, ..., 257.14158829, 257.04828694, 256.84056008], [271.89897587, 272.05106586, 272.23722707, ..., 257.96104896, 257.65194271, 257.40792432], ..., [285.86931079, 286.0414578 , 283.64100116, ..., 285.465679 , 285.35143844, 285.22891733], [286.3004701 , 286.23255157, 277.32089532, ..., 286.72803962, 286.51520671, 287.08830891], [286.65807343, 286.88324638, 279.45262279, ..., 285.97165017, 285.23219565, 286.46798507]], [[269.58005623, 269.73718129, 269.90134629, ..., 258.65804755, 258.74450634, 258.56263385], [270.28108414, 270.45363352, 270.73014566, ..., 259.4485535 , 259.27823606, 259.1722495 ], [270.95068658, 271.06147368, 271.16166753, ..., 260.15164798, 259.97192383, 259.90708127], ..., [287.14252605, 287.17520473, 283.60553111, ..., 283.28956504, 283.36251765, 283.51566016], [287.46080647, 286.90985373, 275.88732777, ..., 284.7537603 , 284.6344505 , 285.4749255 ], [287.82753422, 287.799535 , 276.99410314, ..., 284.49364737, 283.64042332, 284.94194031]], [[268.33366295, 268.53917329, 268.75751694, ..., 259.42017729, 259.39134017, 259.11995863], [269.06553683, 269.25900368, 269.56070411, ..., 259.9021696 , 259.6504034 , 259.52636603], [269.90905098, 270.0588097 , 270.22716788, ..., 260.5269316 , 260.27090885, 260.2320288 ], ..., [287.246657 , 287.37171538, 284.46413554, ..., 283.36024508, 283.78340514, 283.89836618], [287.64222153, 287.32740386, 277.1937969 , ..., 285.15094127, 284.92933655, 285.47841677], [287.93024146, 288.0426241 , 279.17122153, ..., 284.31629977, 283.4837139 , 284.74968753]]])
What is the difference between the mean and weighted mean?
Barely visible the difference
[34]:
ERA5_Siberia_weighted['t2m'].mean(['longitude', 'latitude']).plot()
ERA5_Siberia['t2m'].groupby('time.year').mean().mean(['longitude','latitude']).plot()
[34]:
[<matplotlib.lines.Line2D at 0x2aedbb30ca90>]
[34]:
[<matplotlib.lines.Line2D at 0x2aed3bf98b50>]
Spatial selection¶
What spatial extent defines the event you are analyzing? The easiest option is to select a lat-lon box, like we did for the Siberian heatwave example (i.e we average the temperature over 30-70N, -11-120E).
In case you want to specify another domain than a lat-lon box, you could mask the datasets. For the California Fires example, we select the domain with high temperature anomalies (>2 standard deviation), see California_august_temperature_anomaly. For the UK example, we want a country-averaged timeseries instead of a box. In this case, we use another observational product: the EOBS dataset that covers Europe. We upscale this dataset to the same resolution as SEAS5 and create a mask to take the spatial average over the UK, see Using EOBS + upscaling.
[35]:
ERA5_Siberia_events = (
ERA5_Siberia_weighted['t2m'].sel( # Select 2 metre temperature
latitude=slice(70, 30), # Select the latitudes
longitude=slice(-11, 120)). # Select the longitude
mean(['longitude', 'latitude'])) # And average
ERA5_Siberia_events
[35]:
xarray.DataArray
't2m'
- year: 42
- 277.2 277.0 278.0 277.9 278.0 277.7 ... 280.0 279.6 279.0 279.6 280.4
array([277.16074991, 277.03096514, 277.97289103, 277.91908656, 277.97520289, 277.65083809, 277.41882817, 277.91126484, 276.91748994, 277.7290794 , 278.77625033, 279.26028161, 277.8734671 , 277.73297789, 278.05803743, 278.33474762, 278.8209804 , 277.56676759, 279.12484889, 278.01516085, 277.87531784, 279.26812975, 278.86348174, 279.10661042, 278.37979554, 278.49025936, 278.74264131, 278.30305153, 279.58431666, 279.54500525, 278.82677061, 278.60606811, 279.51421559, 279.21937005, 278.59269493, 279.92328752, 279.6721145 , 280.00902662, 279.55917477, 278.99138847, 279.61443777, 280.41497365]) - year(year)int641979 1980 1981 ... 2018 2019 2020
array([1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020])
In addition to the large domain, we select one more specific domain that faced the highest anomalies, also used here
[36]:
ERA5_Siberia_events_zoomed = (
ERA5_Siberia_weighted['t2m'].sel( # Select 2 metre temperature
latitude=slice(70, 50), # Select the latitudes
longitude=slice(65, 120)). # Select the longitude
mean(['longitude', 'latitude']))
And we repeat this for the SEAS5 events
[37]:
SEAS5_Siberia_events = (
SEAS5_Siberia_weighted['t2m'].sel(
latitude=slice(70, 30),
longitude=slice(-11, 120)).
mean(['longitude', 'latitude']))
SEAS5_Siberia_events.load()
/soge-home/users/cenv0732/.conda/envs/UNSEEN-open/lib/python3.8/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
return np.nanmean(a, axis=axis, dtype=dtype)
[37]:
xarray.DataArray
't2m'
- year: 39
- leadtime: 3
- number: 51
- 277.7 277.1 277.1 276.4 277.6 277.0 ... 277.9 277.8 279.0 278.8 278.2
array([[[277.70246026, 277.08011538, 277.05805243, ..., nan, nan, nan], [276.54063304, 277.63527276, 276.53540684, ..., nan, nan, nan], [276.94382457, 277.22540106, 277.25375804, ..., nan, nan, nan]], [[276.68638666, 276.64418409, 276.88169219, ..., nan, nan, nan], [277.06362955, 277.30470221, 276.49967939, ..., nan, nan, nan], [276.37166345, 276.63563118, 277.05456392, ..., nan, nan, nan]], [[277.53103277, 277.49691758, 278.32115366, ..., nan, nan, nan], [277.53911427, 278.11393678, 277.66278741, ..., nan, nan, nan], [278.24589375, 277.71341079, 277.3067712 , ..., nan, nan, nan]], ..., [[278.78906418, 278.0626699 , 278.09438675, ..., 278.10947691, 278.27620377, 278.18620179], [278.65929139, 277.33954004, 278.65951576, ..., 278.22959696, 278.32163068, 278.94724957], [278.90689211, 277.9030209 , 279.13818072, ..., 278.76767259, 279.13397914, 277.76992423]], [[278.6218426 , 277.95006232, 278.22900254, ..., 277.56330007, 277.99480916, 277.66857676], [278.39808792, 277.65889255, 277.92266928, ..., 278.39390445, 277.90353039, 278.01793147], [278.63429219, 278.11630486, 278.58465727, ..., 277.7081865 , 277.73949614, 278.65482764]], [[279.28124227, 278.53474142, 278.47436518, ..., 278.93209185, 278.11716261, 279.3904762 ], [277.77935773, 279.15571385, 279.02168652, ..., 279.25803913, 278.8991169 , 278.72803803], [278.54721722, 278.25816177, 279.65502139, ..., 279.01242149, 278.80174459, 278.23615329]]]) - leadtime(leadtime)int642 3 4
array([2, 3, 4])
- number(number)int640 1 2 3 4 5 6 ... 45 46 47 48 49 50
- long_name :
- ensemble_member
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]) - year(year)int641982 1983 1984 ... 2018 2019 2020
array([1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020])
[39]:
SEAS5_Siberia_events_zoomed = (
SEAS5_Siberia_weighted['t2m'].sel(
latitude=slice(70, 50),
longitude=slice(65, 120)).
mean(['longitude', 'latitude']))
SEAS5_Siberia_events_zoomed.load()
[39]:
xarray.DataArray
't2m'
- year: 39
- leadtime: 3
- number: 51
- 269.4 267.5 268.9 266.8 269.6 267.9 ... 268.2 270.0 270.0 269.8 267.2
array([[[269.41349502, 267.46724112, 268.92858923, ..., nan, nan, nan], [267.30541714, 269.45786213, 267.88083134, ..., nan, nan, nan], [266.70463988, 269.36559057, 267.85380896, ..., nan, nan, nan]], [[267.65255078, 267.85459971, 267.93397041, ..., nan, nan, nan], [267.86908721, 269.16875401, 266.25505375, ..., nan, nan, nan], [267.37705396, 268.0216673 , 269.216725 , ..., nan, nan, nan]], [[269.11559244, 270.30993792, 268.97992022, ..., nan, nan, nan], [268.42632866, 270.23730451, 268.14872887, ..., nan, nan, nan], [269.91675564, 269.37623754, 268.62430776, ..., nan, nan, nan]], ..., [[270.1395106 , 269.2763681 , 269.63408345, ..., 267.98167445, 270.99148119, 269.2334804 ], [269.47838693, 267.62436995, 270.46814617, ..., 269.60061317, 269.2030878 , 270.34174847], [271.39762301, 268.06102893, 271.27933216, ..., 269.56866515, 270.64943148, 266.36754614]], [[269.32829344, 268.54758963, 269.10385302, ..., 268.03762412, 269.16690743, 268.41760566], [269.09579833, 268.27844902, 268.80430384, ..., 269.38437353, 269.58112924, 269.15390309], [269.2755198 , 269.28402279, 270.34429505, ..., 268.05044769, 269.01128231, 270.09822017]], [[271.35447849, 269.73507649, 269.16852164, ..., 270.77523033, 268.30803885, 271.87237937], [269.33923167, 270.81122764, 269.97102764, ..., 272.56708782, 270.69195734, 269.90622653], [270.37695877, 269.72247595, 272.524012 , ..., 270.04026793, 269.81585811, 267.24061429]]]) - leadtime(leadtime)int642 3 4
array([2, 3, 4])
- number(number)int640 1 2 3 4 5 6 ... 45 46 47 48 49 50
- long_name :
- ensemble_member
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]) - year(year)int641982 1983 1984 ... 2018 2019 2020
array([1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020])
[40]:
SEAS5_Siberia_events.to_dataframe().to_csv('Data/SEAS5_Siberia_events.csv')
ERA5_Siberia_events.to_dataframe().to_csv('Data/ERA5_Siberia_events.csv')
[41]:
SEAS5_Siberia_events_zoomed.to_dataframe().to_csv('Data/SEAS5_Siberia_events_zoomed.csv')
ERA5_Siberia_events_zoomed.to_dataframe().to_csv('Data/ERA5_Siberia_events_zoomed.csv')