# Calculate Time Averages from Time Series Data#

Author: Tom Vo

Date: 05/27/22

Last Edited: 08/17/22 (v0.3.1)

Related APIs:

The data used in this example can be found through the Earth System Grid Federation (ESGF) search portal.

## Overview#

Suppose we have netCDF4 files for air temperature data (tas) with monthly, daily, and 3hr frequencies.

We want to calculate averages using these files with the time dimension removed (a single time snapshot), and averages by time group (yearly, seasonal, and daily).

[1]:

%matplotlib inline

import pandas as pd
import matplotlib.pyplot as plt
import xcdat


## 1. Calculate averages with the time dimension removed (single snapshot)#

Related API: xarray.Dataset.temporal.average()

• The frequency for the time interval is inferred before calculating weights.

• The frequency is inferred by calculating the minimum delta between time coordinates and using the conditional logic below. This frequency is used to calculate weights.

if min_delta < pd.Timedelta(days=1):
return "hour"
elif min_delta >= pd.Timedelta(days=1) and min_delta < pd.Timedelta(days=28):
return "day"
elif min_delta >= pd.Timedelta(days=28) and min_delta < pd.Timedelta(days=365):
return "month"
else:
return "year"

• Masked (missing) data is automatically handled.

• The weight of masked (missing) data are excluded when averages are calculated. This is the same as giving them a weight of 0.

### Open the Dataset#

In this example, we will be calculating the time weighted averages with the time dimension removed (single snapshot) for monthly tas data.

We are using xarray’s OPeNDAP support to read a netCDF4 dataset file directly from its source. The data is not loaded over the network until we perform operations on it (e.g., temperature unit adjustment).

[2]:

filepath = "https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/Amon/tas/gn/v20200605/tas_Amon_ACCESS-ESM1-5_historical_r10i1p1f1_gn_185001-201412.nc"
ds = xcdat.open_dataset(filepath)

# Unit adjust (-273.15, K to C)
ds["tas"] = ds.tas - 273.15

ds

[2]:

<xarray.Dataset>
Dimensions:    (time: 1980, bnds: 2, lat: 145, lon: 192)
Coordinates:
* time       (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12-16T12:00:00
* lat        (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0
* lon        (lon) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1
height     float64 2.0
Dimensions without coordinates: bnds
Data variables:
time_bnds  (time, bnds) datetime64[ns] 1850-01-01 1850-02-01 ... 2015-01-01
lat_bnds   (lat, bnds) float64 -90.0 -89.38 -89.38 ... 89.38 89.38 90.0
lon_bnds   (lon, bnds) float64 -0.9375 0.9375 0.9375 ... 357.2 357.2 359.1
tas        (time, lat, lon) float32 -27.19 -27.19 -27.19 ... -25.29 -25.29
Attributes: (12/48)
Conventions:                     CF-1.7 CMIP-6.2
activity_id:                     CMIP
branch_method:                   standard
branch_time_in_child:            0.0
branch_time_in_parent:           87658.0
creation_date:                   2020-06-05T04:06:11Z
...                              ...
variant_label:                   r10i1p1f1
version:                         v20200605
license:                         CMIP6 model data produced by CSIRO is li...
cmor_version:                    3.4.0
tracking_id:                     hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...
DODS_EXTRA.Unlimited_Dimension:  time
[3]:

ds_avg = ds.temporal.average("tas", weighted=True)

[4]:

ds_avg.tas

[4]:

<xarray.DataArray 'tas' (lat: 145, lon: 192)>
array([[-48.01481628, -48.01481628, -48.01481628, ..., -48.01481628,
-48.01481628, -48.01481628],
[-44.94085363, -44.97948214, -45.01815398, ..., -44.82408252,
-44.86273067, -44.9009281 ],
[-44.11875274, -44.23060624, -44.33960158, ..., -43.76766492,
-43.88593717, -44.00303006],
...,
[-18.21076615, -18.17513373, -18.13957458, ..., -18.32720478,
-18.28428828, -18.2486193 ],
[-18.50778243, -18.49301854, -18.47902819, ..., -18.55410851,
-18.5406963 , -18.52413098],
[-19.07366375, -19.07366375, -19.07366375, ..., -19.07366375,
-19.07366375, -19.07366375]])
Coordinates:
* lat      (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0
* lon      (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1
height   float64 2.0
Attributes:
operation:  temporal_avg
mode:       average
freq:       month
weighted:   True
[5]:

ds_avg.tas.plot(label="weighted")

[5]:

<matplotlib.collections.QuadMesh at 0x7f120aa2a9a0>


## 2. Calculate grouped averages#

Related API: xarray.Dataset.temporal.group_average()

• Each specified frequency has predefined groups for grouping time coordinates.

• The table below maps type of averages with its API frequency and grouping convention.

Type of Averages

API Frequency

Group By

Yearly

freq=“year”

year

Monthly

freq=“month”

year, month

Seasonal

freq=“season”

year, season

Custom seasonal

freq="season" and season_config={"custom_seasons": <2D ARRAY>}

year, season

Daily

freq=“day”

year, month, day

Hourly

freq=“hour”

year, month, day, hour

• The grouping conventions are based on CDAT/cdutil, except for daily and hourly means which aren’t implemented in CDAT/cdutil.

• Masked (missing) data is automatically handled.

• The weight of masked (missing) data are excluded when averages are calculated. This is the same as giving them a weight of 0.

### Open the Dataset#

In this example, we will be calculating the weighted grouped time averages for tas data.

We are using xarray’s OPeNDAP support to read a netCDF4 dataset file directly from its source. The data is not loaded over the network until we perform operations on it (e.g., temperature unit adjustment).

[6]:

filepath = "https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/Amon/tas/gn/v20200605/tas_Amon_ACCESS-ESM1-5_historical_r10i1p1f1_gn_185001-201412.nc"
ds = xcdat.open_dataset(filepath)

# Unit adjust (-273.15, K to C)
ds["tas"] = ds.tas - 273.15

ds

[6]:

<xarray.Dataset>
Dimensions:    (time: 1980, bnds: 2, lat: 145, lon: 192)
Coordinates:
* time       (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12-16T12:00:00
* lat        (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0
* lon        (lon) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1
height     float64 2.0
Dimensions without coordinates: bnds
Data variables:
time_bnds  (time, bnds) datetime64[ns] 1850-01-01 1850-02-01 ... 2015-01-01
lat_bnds   (lat, bnds) float64 -90.0 -89.38 -89.38 ... 89.38 89.38 90.0
lon_bnds   (lon, bnds) float64 -0.9375 0.9375 0.9375 ... 357.2 357.2 359.1
tas        (time, lat, lon) float32 -27.19 -27.19 -27.19 ... -25.29 -25.29
Attributes: (12/48)
Conventions:                     CF-1.7 CMIP-6.2
activity_id:                     CMIP
branch_method:                   standard
branch_time_in_child:            0.0
branch_time_in_parent:           87658.0
creation_date:                   2020-06-05T04:06:11Z
...                              ...
variant_label:                   r10i1p1f1
version:                         v20200605
license:                         CMIP6 model data produced by CSIRO is li...
cmor_version:                    3.4.0
tracking_id:                     hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...
DODS_EXTRA.Unlimited_Dimension:  time

### Yearly Averages#

Group time coordinates by year

[7]:

ds_yearly = ds.temporal.group_average("tas", freq="year", weighted=True)

[8]:

ds_yearly.tas

[8]:

<xarray.DataArray 'tas' (time: 165, lat: 145, lon: 192)>
array([[[-48.75573349, -48.75573349, -48.75573349, ..., -48.75573349,
-48.75573349, -48.75573349],
[-45.65206528, -45.69302368, -45.73506165, ..., -45.52127838,
-45.56386566, -45.60668945],
[-44.77523422, -44.90583801, -45.03297043, ..., -44.37118149,
-44.50630951, -44.64050293],
...,
[-20.50597572, -20.48132133, -20.45456505, ..., -20.58895874,
-20.55752182, -20.53087234],
[-20.79759216, -20.78425217, -20.77545547, ..., -20.83267975,
-20.82335663, -20.80768394],
[-21.20114899, -21.20114899, -21.20114899, ..., -21.20114899,
-21.20114899, -21.20114899]],

[[-48.95254898, -48.95254898, -48.95254898, ..., -48.95254898,
-48.95254898, -48.95254898],
[-45.83190918, -45.8649025 , -45.89875031, ..., -45.7321701 ,
-45.76544189, -45.79859543],
[-44.93536758, -45.03795624, -45.13800812, ..., -44.61143112,
-44.71986008, -44.82937241],
...
[-14.91627121, -14.89926147, -14.88381004, ..., -14.99542999,
-14.96513653, -14.93853188],
[-15.40592194, -15.39668083, -15.38595486, ..., -15.43246269,
-15.42605591, -15.41356754],
[-15.94499969, -15.94499969, -15.94499969, ..., -15.94499969,
-15.94499969, -15.94499969]],

[[-47.59732056, -47.59732056, -47.59732056, ..., -47.59732056,
-47.59732056, -47.59732056],
[-44.72136688, -44.76342773, -44.80350494, ..., -44.59239197,
-44.63444519, -44.67822647],
[-43.85031891, -43.96956253, -44.08713913, ..., -43.47090149,
-43.59676361, -43.72407913],
...,
[-14.52023029, -14.47407913, -14.43230724, ..., -14.67551422,
-14.62093163, -14.56736755],
[-14.91123581, -14.89230919, -14.86901569, ..., -14.9820118 ,
-14.96266842, -14.93872261],
[-15.6184063 , -15.6184063 , -15.6184063 , ..., -15.6184063 ,
-15.6184063 , -15.6184063 ]]])
Coordinates:
* lat      (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0
* lon      (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1
height   float64 2.0
* time     (time) object 1850-01-01 00:00:00 ... 2014-01-01 00:00:00
Attributes:
operation:  temporal_avg
mode:       group_average
freq:       year
weighted:   True

This GIF was created using xmovie.

Sample xmovie code:

import xmovie
mov = xmovie.Movie(ds_yearly_avg.tas)
mov.save("temporal-average-yearly.gif")


### Seasonal Averages#

Group time coordinates by year and season

[9]:

ds_season = ds.temporal.group_average("tas", freq="season", weighted=True)

[10]:

ds_season.tas

[10]:

<xarray.DataArray 'tas' (time: 661, lat: 145, lon: 192)>
array([[[-32.70588303, -32.70588303, -32.70588303, ..., -32.70588303,
-32.70588303, -32.70588303],
[-30.99376678, -31.03758621, -31.08932686, ..., -30.84562302,
-30.89412689, -30.94400978],
[-30.0251503 , -30.14543724, -30.26419067, ..., -29.66037178,
-29.78108025, -29.90287781],
...,
[-37.72314072, -37.68549347, -37.65416718, ..., -37.82619858,
-37.79034424, -37.75682831],
[-38.27464676, -38.26372528, -38.25014496, ..., -38.29218292,
-38.29063797, -38.28456116],
[-38.74358749, -38.74358749, -38.74358749, ..., -38.74358749,
-38.74358749, -38.74358749]],

[[-54.29086304, -54.29086304, -54.29086304, ..., -54.29086304,
-54.29086304, -54.29086304],
[-51.11771393, -51.17523575, -51.23055267, ..., -50.93516541,
-50.99657059, -51.05614471],
[-50.31804657, -50.48666382, -50.64956665, ..., -49.79003143,
-49.97007751, -50.14521027],
...
[-12.34277439, -12.2246685 , -12.10663223, ..., -12.74492168,
-12.60908794, -12.47839165],
[-13.12640381, -13.0661087 , -13.00387573, ..., -13.306077  ,
-13.25871468, -13.19972038],
[-14.28846931, -14.28846931, -14.28846931, ..., -14.28846931,
-14.28846931, -14.28846931]],

[[-28.99049377, -28.99049377, -28.99049377, ..., -28.99049377,
-28.99049377, -28.99049377],
[-28.19291687, -28.22457886, -28.26130676, ..., -28.09593201,
-28.12599182, -28.15802002],
[-27.60740662, -27.7056427 , -27.80511475, ..., -27.31161499,
-27.41082764, -27.50836182],
...,
[-24.25627136, -24.14059448, -24.03753662, ..., -24.61853027,
-24.48849487, -24.36643982],
[-24.62901306, -24.61338806, -24.54986572, ..., -24.75204468,
-24.72160339, -24.66641235],
[-25.28923035, -25.28923035, -25.28923035, ..., -25.28923035,
-25.28923035, -25.28923035]]])
Coordinates:
* lat      (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0
* lon      (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1
height   float64 2.0
* time     (time) object 1850-01-01 00:00:00 ... 2015-01-01 00:00:00
Attributes:
operation:            temporal_avg
mode:                 group_average
freq:                 season
weighted:             True
dec_mode:             DJF
drop_incomplete_djf:  False

Notice that the season of each time coordinate is represented by its middle month.

• “DJF” is represented by month 1 (“J”/January)

• “MAM” is represented by month 4 (“A”/April)

• “JJA” is represented by month 7 (“J”/July)

• “SON” is represented by month 10 (“O”/October).

This is implementation design was used because datetime objects do not distinguish seasons, so the middle month is used instead.

[11]:

ds_season.time

[11]:

<xarray.DataArray 'time' (time: 661)>
array([cftime.DatetimeProlepticGregorian(1850, 1, 1, 0, 0, 0, 0, has_year_zero=True),
cftime.DatetimeProlepticGregorian(1850, 4, 1, 0, 0, 0, 0, has_year_zero=True),
cftime.DatetimeProlepticGregorian(1850, 7, 1, 0, 0, 0, 0, has_year_zero=True),
...,
cftime.DatetimeProlepticGregorian(2014, 7, 1, 0, 0, 0, 0, has_year_zero=True),
cftime.DatetimeProlepticGregorian(2014, 10, 1, 0, 0, 0, 0, has_year_zero=True),
cftime.DatetimeProlepticGregorian(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
dtype=object)
Coordinates:
height   float64 2.0
* time     (time) object 1850-01-01 00:00:00 ... 2015-01-01 00:00:00
Attributes:
bounds:         time_bnds
axis:           T
long_name:      time
standard_name:  time
_ChunkSizes:    1

### Monthly Averages#

Group time coordinates by year and month

For this example, we will be loading a subset of daily time series data for tas using OPeNDAP.

NOTE:

For OPeNDAP servers, the default file size request limit is 500MB in the TDS server configuration. Opening up a dataset over OPeNDAP also introduces an overhead compared to direct file access.

The workaround is to use Dask to request the data in manageable chunks, which overcomes file size limitations and can improve performance.

We have a few ways to chunk our request:

1. Specify chunks with "auto" to let Dask determine the chunksize.

2. Specify a specify the file size to chunk on (e.g., "100MB") or number of chunks as an integer (100 for 100 chunks).

[12]:

# The size of this file is approximately 1.45 GB, so we will be chunking our
# request using Dask to avoid hitting the OPeNDAP file size request limit for
# this ESGF node.
ds2 = xcdat.open_dataset(
"https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/3hr/tas/gn/v20200605/tas_3hr_ACCESS-ESM1-5_historical_r10i1p1f1_gn_201001010300-201501010000.nc",
chunks={"time": "auto"},
)

# Unit adjust (-273.15, K to C)
ds2["tas"] = ds2.tas - 273.15

ds2

[12]:

<xarray.Dataset>
Dimensions:    (time: 14608, lat: 145, bnds: 2, lon: 192)
Coordinates:
* time       (time) datetime64[ns] 2010-01-01T03:00:00 ... 2015-01-01
* lat        (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0
* lon        (lon) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1
height     float64 ...
Dimensions without coordinates: bnds
Data variables:
lat_bnds   (lat, bnds) float64 dask.array<chunksize=(145, 2), meta=np.ndarray>
lon_bnds   (lon, bnds) float64 dask.array<chunksize=(192, 2), meta=np.ndarray>
tas        (time, lat, lon) float32 dask.array<chunksize=(913, 145, 192), meta=np.ndarray>
time_bnds  (time, bnds) datetime64[ns] 2010-01-01T01:30:00 ... 2015-01-01...
Attributes: (12/48)
Conventions:                     CF-1.7 CMIP-6.2
activity_id:                     CMIP
branch_method:                   standard
branch_time_in_child:            0.0
branch_time_in_parent:           87658.0
creation_date:                   2020-06-05T04:54:56Z
...                              ...
variant_label:                   r10i1p1f1
version:                         v20200605
license:                         CMIP6 model data produced by CSIRO is li...
cmor_version:                    3.4.0
tracking_id:                     hdl:21.14100/b79e6a05-c482-46cf-b3b8-83b...
DODS_EXTRA.Unlimited_Dimension:  time
[13]:

ds2_monthly_avg = ds2.temporal.group_average("tas", freq="month", weighted=True)

[14]:

ds2_monthly_avg.tas

[14]:

<xarray.DataArray 'tas' (time: 61, lat: 145, lon: 192)>
dask.array<truediv, shape=(61, 145, 192), dtype=float64, chunksize=(1, 145, 192), chunktype=numpy.ndarray>
Coordinates:
* lat      (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0
* lon      (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1
height   float64 ...
* time     (time) object 2010-01-01 00:00:00 ... 2015-01-01 00:00:00
Attributes:
operation:  temporal_avg
mode:       group_average
freq:       month
weighted:   True

### Daily Averages#

Group time coordinates by year, month, and day

For this example, we will be opening a subset of 3hr time series data for tas using OPeNDAP.

[15]:

# The size of this file is approximately 1.17 GB, so we will be chunking our
# request using Dask to avoid hitting the OPeNDAP file size request limit for
# this ESGF node.
ds3 = xcdat.open_dataset(
"https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/3hr/tas/gn/v20200605/tas_3hr_ACCESS-ESM1-5_historical_r10i1p1f1_gn_201001010300-201501010000.nc",
chunks={"time": "auto"},
)

# Unit adjust (-273.15, K to C)
ds3["tas"] = ds3.tas - 273.15

[16]:

ds3.tas

[16]:

<xarray.DataArray 'tas' (time: 14608, lat: 145, lon: 192)>
dask.array<sub, shape=(14608, 145, 192), dtype=float32, chunksize=(913, 145, 192), chunktype=numpy.ndarray>
Coordinates:
* time     (time) datetime64[ns] 2010-01-01T03:00:00 ... 2015-01-01
* lat      (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0
* lon      (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1
height   float64 ...
[17]:

ds3_day_avg = ds3.temporal.group_average("tas", freq="day", weighted=True)

[18]:

ds3_day_avg.tas

[18]:

<xarray.DataArray 'tas' (time: 1827, lat: 145, lon: 192)>
dask.array<truediv, shape=(1827, 145, 192), dtype=float64, chunksize=(1, 145, 192), chunktype=numpy.ndarray>
Coordinates:
* lat      (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0
* lon      (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1
height   float64 ...
* time     (time) object 2010-01-01 00:00:00 ... 2015-01-01 00:00:00
Attributes:
operation:  temporal_avg
mode:       group_average
freq:       day
weighted:   True