Exploring precipitation information in the GIRAFE dataset

This notebook can be run on free online platforms, such as Binder, Kaggle and Colab, or they can be accessed from GitHub. The links to run this notebook in these environments are provided here, but please note they are not supported by ECMWF.

Introduction

Anthropogenic activities and natural variations from years to decades shape the Earth’s climate. Water and energy cycles are central to the physics of climate change. Within the hydrological cycle, precipitation has been recognised as an Essential Climate Variable (ECV) as it is the main component of water transport from the atmosphere to the Earth’s surface. Precipitation varies strongly, depending on geographical location, season, synopsis, and other meteorological factors. The supply of freshwater through precipitation is vital for many subsystems of the climate and the environment, but there are also hazards related to extensive precipitation (floods) or to the lack of precipitation (droughts).

This notebook-tutorial provides an introduction to the use of the Global Interpolated RAinFall Estimation (GIRAFE) data record for climate studies.

The Precipitation (PRE) Essential Climate Variable (ECV) and the GIRAFE product are described below. Then, a first use case provides an analysis of the time averaged global and seasonal climatological distributions of the Precipitation field as well as the monthly mean climatology. The second use case presents the time series and trend analysis of Precipitation. Step-by-step instructions are provided on data preparation; the use cases are extensively documented and each line of code is explained.

The three figures below show some results from the use cases and illustrate the successful run of the code.

Learning objectives 🎯

In this Jupyter notebook tutorial, we present examples based on monthly mean Precipitation products, to illustrate the philosophy on the usage, visualisation, and analysis of the dataset. First you get to sort out data access and retrieval, and get all the right libraries in place for the computations. Then we take you through a short process of inspecting the retrieved data to see if it’s all ok for analysis. You then have a chance to visualise your data, before we take you through some climatology analyses that you could use in your work.

Prepare your environment

Before we begin we must prepare our environment. This includes installing the Earthkit package, and importing the various python libraries that we will need.

Set up CDSAPI and your credentials

The code below will ensure that the earthkit package is installed. If you have not setup your ~/.cdsapirc file with your credentials, you will be prompted to provide them when you request data from the CDS. You can find the credentials required on the how to api page (you will need to log in to see your credentials).

!pip install -q earthkit

(Install and) Import libraries

The data have been stored in files written in NetCDF format. We will use earthkit-data to download the data from the CDS, and open them with xarray. earthkit-data will handle the specifics of the file and download formats, so you can focus on inspecting the data contents.

We will also be using earthkit-plots, Matplotlib and Cartopy to visualise the data and statsmodels for some advanced data manipulation.

import os

# Import the earthkit data and plots libraries
import earthkit.plots as ekp
import earthkit.data as ekd

# # Libraries for working with multidimensional arrays
import xarray as xr
import numpy as np
import pandas as pd

# Import a sublibrary method for the seasonal decomposition of the time series
from statsmodels.tsa.seasonal import seasonal_decompose

# Libraries for plotting and visualising the data
import matplotlib.pyplot as plt
import cartopy.crs as ccrs

Explore data

The EUMETSAT Satellite Application Facility on Climate Monitoring has brokered the Global Interpolated RAinFall Estimation (GIRAFE) product, making it available to users through the Copernicus Climate Data Store (CDS). The product merges microwave (MW) sounder- and imager-based estimations of instantaneous surface precipitation (over land and ocean) with infrared (IR) observations from geostationary platforms along the equator. GIRAFE is a global 1° x 1° latitude-longitude data record that is produced at a daily temporal resolution, as well as on a monthly mean basis. It covers the time period January 2002 to December 2022.

You will find further information about the dataset (Algorithm Theoretical Basis Document, Product User Guide and Specification, etc.) as well as the data in the Climate Data Store catalogue entry Monthly and daily global interpolated rainfall estimation data from 2002 to 2022 derived from satellite measurements (see the link to the entry below), sections “Overview”, “Download data” and “Documentation”:

• Monthly and daily global interpolated rainfall estimation data from 2002 to 2022 derived from satellite measurements

Search for data

To search for data, we will visit the CDS website: https://cds.climate.copernicus.eu/. Here we can search for GIRAFE data using the search bar. The data we need for this use case is the Monthly and daily global interpolated rainfall estimation data from 2002 to 2022 derived from satellite measurements dataset. This catalogue entry provides daily and monthly accumulated precipitation amounts derived from the combination of MW imager and sounder retrievals over land and ocean surfaces with geostationary IR observations.

After selecting the correct catalogue entry, we will specify the time aggregation and temporal coverage we are interested in. These can all be selected in the “Download data” tab. In this tab a form appears in which we will select the following parameters to download:

Parameter of data to download

• Time aggregation: Monthly

• Year: all (use “Select all” button)

• Month: all (use “Select all” button)

• Day: 01 (or simply use “Select all” button)

At the end of the download form, select “Show API request”. This will reveal a block of code, which you can simply copy and paste into a cell of your Jupyter Notebook (see cell below)

Warning

Please remember to accept the terms and conditions of the dataset, at the bottom of the CDS download form!

Download data

Having copied the API request into the cell below, running this will retrieve and download the data we requested into our local directory. However, before we run the cell below, the terms and conditions of this particular dataset need to have been accepted in the CDS. The option to view and accept these conditions is given at the end of the download form, just above the “Show API request” option.

# Request the data
dataset = 'satellite-precipitation-microwave-infrared'
request = {
    'time_aggregation': 'monthly',
    'year': ['%04d' % (year) for year in range(2002, 2023)],
    'month': ['%02d' % (month) for month in range(1, 13)],
    'day': ['01'],
    'variable': 'all'
}
ek_ds = ekd.from_source(
    'cds',
    dataset,
    request,
)

2025-09-15 11:11:18,974 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.
2025-09-15 11:11:19,217 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.
2025-09-15 11:11:19,417 INFO Request ID is 977999e5-fc39-4794-b93f-82d593fa6574
2025-09-15 11:11:19,498 INFO status has been updated to accepted
2025-09-15 11:11:33,378 INFO status has been updated to running
2025-09-15 11:14:12,810 INFO status has been updated to successful

Inspect data

The data have been downloaded to your earthkit cache, we can now use earthkit to open this in xarray. Behind the scenes, earthkit is unpacking a zip file which contains many netCDF files and then lazily opening with xarray. “Lazily” means that you do not load all the data into RAM and it is therefore safer to process on your computer.

The output of the cell below shows the xarray.Dataset representation of the downloaded data. The dataset has ten variables including precipitation, and three dimension coordinates of time, lat and lon.

# Open the ek_ds as an xarray Dataset
ds_girafe = ek_ds.to_xarray()
ds_girafe

<xarray.Dataset> Size: 655MB
Dimensions:           (time: 252, nv: 2, lat: 180, lon: 360)
Coordinates:
  * time              (time) datetime64[ns] 2kB 2002-01-01 ... 2022-12-01
  * lat               (lat) float64 1kB -89.5 -88.5 -87.5 ... 87.5 88.5 89.5
  * lon               (lon) float64 3kB -179.5 -178.5 -177.5 ... 178.5 179.5
Dimensions without coordinates: nv
Data variables:
    time_bnds         (time, nv) datetime64[ns] 4kB dask.array<chunksize=(1, 2), meta=np.ndarray>
    lat_bnds          (time, lat, nv) float64 726kB dask.array<chunksize=(1, 180, 2), meta=np.ndarray>
    lon_bnds          (time, lon, nv) float64 1MB dask.array<chunksize=(1, 360, 2), meta=np.ndarray>
    record_status     (time) int8 252B dask.array<chunksize=(1,), meta=np.ndarray>
    precipitation     (time, lat, lon) float32 65MB dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
    num_obs_fraction  (time, lat, lon) float64 131MB dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
    num_obs_rate      (time, lat, lon) float64 131MB dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
    num_days          (time, lat, lon) float64 131MB dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
    quality_flag      (time, lat, lon) float32 65MB dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
    num_days_snow     (time, lat, lon) float64 131MB dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
Attributes: (12/32)
    title:                      Global Interpolated RAinFall Estimation (GIRAFE)
    summary:                    This file contains data from the Precipitatio...
    id:                         DOI:10.5676/EUM_SAF_CM/GIRAFE/V001
    product_version:            1.0
    creator_name:               DE/DWD
    creator_email:              contact.cmsaf@dwd.de
    ...                         ...
    platform_vocabulary:        GCMD Platforms, Version 16.5
    instrument:                 SSM/I > Special Sensor Microwave/Imager,TMI >...
    instrument_vocabulary:      GCMD Instruments, Version 16.5
    variable_id:                precipitation
    CMSAF_processor:            girafe-app-v1.5.5
    license:                    The CM SAF data are owned by EUMETSAT and are...

xarray.Dataset

• Dimensions:
  • time: 252
  • nv: 2
  • lat: 180
  • lon: 360
• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    2002-01-01 ... 2022-12-01

    long_name :

    Product dataset time given as seconds since 2000-01-01T00:00:00

    standard_name :

    time

    axis :

    T

    bounds :

    time_bnds

    array(['2002-01-01T00:00:00.000000000', '2002-02-01T00:00:00.000000000',
           '2002-03-01T00:00:00.000000000', ..., '2022-10-01T00:00:00.000000000',
           '2022-11-01T00:00:00.000000000', '2022-12-01T00:00:00.000000000'],
          shape=(252,), dtype='datetime64[ns]')

  • lat
    (lat)
    float64
    -89.5 -88.5 -87.5 ... 88.5 89.5

    units :

    degrees_north

    long_name :

    Latitude

    standard_name :

    latitude

    valid_range :

    [-90. 90.]

    reference_datum :

    geographical coordinates, WGS84 projection

    axis :

    Y

    bounds :

    lat_bnds

    array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
           -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
           -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
           -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
           -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
           -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
           -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
            -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
             0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
            10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
            20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
            30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
            40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
            50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
            60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
            70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])

  • lon
    (lon)
    float64
    -179.5 -178.5 ... 178.5 179.5

    units :

    degrees_east

    long_name :

    Longitude

    standard_name :

    longitude

    valid_range :

    [-180. 180.]

    reference_datum :

    geographical coordinates, WGS84 projection

    axis :

    X

    bounds :

    lon_bnds

    array([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5], shape=(360,))

• Data variables: (10)
  • time_bnds
    (time, nv)
    datetime64[ns]
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    long_name :

    Time cell boundaries

    comment :

    Contains the start and end times for the time period that the data represent.

    Array Chunk Bytes 3.94 kiB 16 B Shape (252, 2) (1, 2) Dask graph 252 chunks in 505 graph layers Data type datetime64[ns] numpy.ndarray

  • lat_bnds
    (time, lat, nv)
    float64
    dask.array<chunksize=(1, 180, 2), meta=np.ndarray>

    long_name :

    Latitude cell boundaries

    valid_range :

    [-90. 90.]

    reference_datum :

    geographical coordinates, WGS84 projection

    comment :

    Contains the northern and southern boundaries of the grid cells.

    Array Chunk Bytes 708.75 kiB 2.81 kiB Shape (252, 180, 2) (1, 180, 2) Dask graph 252 chunks in 757 graph layers Data type float64 numpy.ndarray

  • lon_bnds
    (time, lon, nv)
    float64
    dask.array<chunksize=(1, 360, 2), meta=np.ndarray>

    long_name :

    Longitude cell boundaries

    valid_range :

    [-180. 180.]

    reference_datum :

    geographical coordinates, WGS84 projection

    comment :

    Contains the eastern and western boundaries of the grid cells.

    Array Chunk Bytes 1.38 MiB 5.62 kiB Shape (252, 360, 2) (1, 360, 2) Dask graph 252 chunks in 757 graph layers Data type float64 numpy.ndarray

  • record_status
    (time)
    int8
    dask.array<chunksize=(1,), meta=np.ndarray>

    long_name :

    Record Status

    comment :

    Overall status of each record (timestamp) in this file. If a record is flagged as not ok, it is recommended not to use it.

    flag_values :

    [0 1 2]

    flag_meanings :

    ok void bad_quality

    Array Chunk Bytes 252 B 1 B Shape (252,) (1,) Dask graph 252 chunks in 505 graph layers Data type int8 numpy.ndarray

  • precipitation
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 180, 360), meta=np.ndarray>

    standard_name :

    lwe_thickness_of_precipitation_amount

    cell_method :

    time: mean

    units :

    mm

    long_name :

    monthly mean of daily accumulated precipitation

    Array Chunk Bytes 62.29 MiB 253.12 kiB Shape (252, 180, 360) (1, 180, 360) Dask graph 252 chunks in 505 graph layers Data type float32 numpy.ndarray

  • num_obs_fraction
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 180, 360), meta=np.ndarray>

    long_name :

    Number of observations available for derivation of precip fraction

    standard_name :

    number_of_observations

    units :

    1

    cell_method :

    time: sum

    Array Chunk Bytes 124.58 MiB 506.25 kiB Shape (252, 180, 360) (1, 180, 360) Dask graph 252 chunks in 505 graph layers Data type float64 numpy.ndarray

  • num_obs_rate
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 180, 360), meta=np.ndarray>

    long_name :

    Number of observations available for derivation of precip rate

    standard_name :

    number_of_observations

    units :

    1

    cell_method :

    time: sum

    Array Chunk Bytes 124.58 MiB 506.25 kiB Shape (252, 180, 360) (1, 180, 360) Dask graph 252 chunks in 505 graph layers Data type float64 numpy.ndarray

  • num_days
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 180, 360), meta=np.ndarray>

    long_name :

    Number of days contributing to monthly mean

    units :

    1

    Array Chunk Bytes 124.58 MiB 506.25 kiB Shape (252, 180, 360) (1, 180, 360) Dask graph 252 chunks in 505 graph layers Data type float64 numpy.ndarray

  • quality_flag
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 180, 360), meta=np.ndarray>

    long_name :

    Averaging quality control flag

    flag_values :

    [0 1 2 3]

    flag_meanings :

    ok more_than_10_days_missing more_than_4_consecutive_days_missing more_than_10_days_and_more_than_4_consecutive_days_missing

    Array Chunk Bytes 62.29 MiB 253.12 kiB Shape (252, 180, 360) (1, 180, 360) Dask graph 252 chunks in 505 graph layers Data type float32 numpy.ndarray

  • num_days_snow
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 180, 360), meta=np.ndarray>

    long_name :

    Number of days in month with snow or sea ice present

    units :

    1

    Array Chunk Bytes 124.58 MiB 506.25 kiB Shape (252, 180, 360) (1, 180, 360) Dask graph 252 chunks in 505 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2002-01-01', '2002-02-01', '2002-03-01', '2002-04-01',
                   '2002-05-01', '2002-06-01', '2002-07-01', '2002-08-01',
                   '2002-09-01', '2002-10-01',
                   ...
                   '2022-03-01', '2022-04-01', '2022-05-01', '2022-06-01',
                   '2022-07-01', '2022-08-01', '2022-09-01', '2022-10-01',
                   '2022-11-01', '2022-12-01'],
                  dtype='datetime64[ns]', name='time', length=252, freq=None))

  • lat
    PandasIndex

    PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           ...
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],
          dtype='float64', name='lat', length=180))

  • lon
    PandasIndex

    PandasIndex(Index([-179.5, -178.5, -177.5, -176.5, -175.5, -174.5, -173.5, -172.5, -171.5,
           -170.5,
           ...
            170.5,  171.5,  172.5,  173.5,  174.5,  175.5,  176.5,  177.5,  178.5,
            179.5],
          dtype='float64', name='lon', length=360))

• Attributes: (32)

  title :

  Global Interpolated RAinFall Estimation (GIRAFE)

  summary :

  This file contains data from the Precipitation Climate Data Record v1 based on passive microwave observations by polar-orbiting satellites and passive infrared observations by geostationary satellites produced by the Satellite Application Facility on Climate Monitoring (CM SAF).

  id :

  DOI:10.5676/EUM_SAF_CM/GIRAFE/V001

  product_version :

  1.0

  creator_name :

  DE/DWD

  creator_email :

  contact.cmsaf@dwd.de

  creator_url :

  http://www.cmsaf.eu/

  institution :

  EUMETSAT/CMSAF

  project :

  Satellite Application Facility on Climate Monitoring (CM SAF)

  references :

  https://doi.org/10.5676/EUM_SAF_CM/GIRAFE/V001

  keywords_vocabulary :

  GCMD Science Keywords, Version 16.5

  keywords :

  EARTH SCIENCE > ATMOSPHERE > PRECIPITATION > PRECIPITATION AMOUNT > 24 HOUR PRECIPITATION AMOUNT

  Conventions :

  CF-1.7,ACDD-1.3

  standard_name_vocabulary :

  Standard Name Table (v77, 19 January 2021)

  date_created :

  2024-01-24T13:02:18Z

  time_coverage_start :

  2002-01-01T00:00:00Z

  time_coverage_end :

  2002-02-01T00:00:00Z

  geospatial_lat_units :

  degrees_north

  geospatial_lat_min :

  -90.0

  geospatial_lat_max :

  90.0

  geospatial_lat_resolution :

  1.0 degree

  geospatial_lon_units :

  degrees_east

  geospatial_lon_min :

  -180.0

  geospatial_lon_max :

  180.0

  geospatial_lon_resolution :

  1.0 degree

  platform :

  DMSP 5D-2/F13 > Defense Meteorological Satellite Program-F13,DMSP 5D-2/F14 > Defense Meteorological Satellite Program-F14,DMSP 5D-2/F15 > Defense Meteorological Satellite Program-F15,TRMM > Tropical Rainfall Measuring Mission,NOAA-16 > National Oceanic & Atmospheric Administration-16,Meteosat > METEOSAT-5 > Meteosat Operational Programme 2 (MOP-2),GOES-10 > Geostationary Operational Environmental Satellite 10,GOES-8 > Geostationary Operational Environmental Satellite 8,Meteosat > METEOSAT-7 > METEOSAT-7

  platform_vocabulary :

  GCMD Platforms, Version 16.5

  instrument :

  SSM/I > Special Sensor Microwave/Imager,TMI > TRMM Microwave Imager,AMSU-B > Advanced Microwave Sounding Unit-B,MVIRI > Meteosat Visible Infra-Red Imager,GOES-10 Imager > Geostationary Operational Environmental Satellite 10-Imager,GOES-8 Imager > GOES-8 Imager

  instrument_vocabulary :

  GCMD Instruments, Version 16.5

  variable_id :

  precipitation

  CMSAF_processor :

  girafe-app-v1.5.5

  license :

  The CM SAF data are owned by EUMETSAT and are available to all users free of charge and with no conditions to use. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "Copyright (c) (2024) EUMETSAT" under/in each of these SAF Products used in a project or shown in a publication or website. Please follow the citation guidelines given at https://doi.org/10.5676/EUM_SAF_CM/GIRAFE/V001 and also register as a user at http://cm-saf.eumetsat.int/ to receive latest information on CM SAF services and to get access to the CM SAF User Help Desk.

While an xarray.Dataset may contain multiple variables, an Xarray xarray.DataArray holds a single multi-dimensional variable and its coordinates. The cell below extracts the precipitation data into an xarray.DataArray to make the processing easier.

The xarray.DataArray representation below displays the variable metadata, and also provides a graphical representation of the “chunks” used the read the data, i.e. the quanta of data that are read.

da_precip = ds_girafe['precipitation']
da_precip

<xarray.DataArray 'precipitation' (time: 252, lat: 180, lon: 360)> Size: 65MB
dask.array<concatenate, shape=(252, 180, 360), dtype=float32, chunksize=(1, 180, 360), chunktype=numpy.ndarray>
Coordinates:
  * time     (time) datetime64[ns] 2kB 2002-01-01 2002-02-01 ... 2022-12-01
  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
  * lon      (lon) float64 3kB -179.5 -178.5 -177.5 -176.5 ... 177.5 178.5 179.5
Attributes:
    standard_name:  lwe_thickness_of_precipitation_amount
    cell_method:    time: mean
    units:          mm
    long_name:      monthly mean of daily accumulated precipitation

xarray.DataArray

'precipitation'

• time: 252
• lat: 180
• lon: 360

• dask.array<chunksize=(1, 180, 360), meta=np.ndarray>

  Array Chunk Bytes 62.29 MiB 253.12 kiB Shape (252, 180, 360) (1, 180, 360) Dask graph 252 chunks in 505 graph layers Data type float32 numpy.ndarray

• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    2002-01-01 ... 2022-12-01

    long_name :

    Product dataset time given as seconds since 2000-01-01T00:00:00

    standard_name :

    time

    axis :

    T

    bounds :

    time_bnds

    array(['2002-01-01T00:00:00.000000000', '2002-02-01T00:00:00.000000000',
           '2002-03-01T00:00:00.000000000', ..., '2022-10-01T00:00:00.000000000',
           '2022-11-01T00:00:00.000000000', '2022-12-01T00:00:00.000000000'],
          shape=(252,), dtype='datetime64[ns]')

  • lat
    (lat)
    float64
    -89.5 -88.5 -87.5 ... 88.5 89.5

    units :

    degrees_north

    long_name :

    Latitude

    standard_name :

    latitude

    valid_range :

    [-90. 90.]

    reference_datum :

    geographical coordinates, WGS84 projection

    axis :

    Y

    bounds :

    lat_bnds

    array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
           -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
           -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
           -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
           -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
           -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
           -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
            -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
             0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
            10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
            20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
            30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
            40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
            50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
            60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
            70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])

  • lon
    (lon)
    float64
    -179.5 -178.5 ... 178.5 179.5

    units :

    degrees_east

    long_name :

    Longitude

    standard_name :

    longitude

    valid_range :

    [-180. 180.]

    reference_datum :

    geographical coordinates, WGS84 projection

    axis :

    X

    bounds :

    lon_bnds

    array([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5], shape=(360,))

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2002-01-01', '2002-02-01', '2002-03-01', '2002-04-01',
                   '2002-05-01', '2002-06-01', '2002-07-01', '2002-08-01',
                   '2002-09-01', '2002-10-01',
                   ...
                   '2022-03-01', '2022-04-01', '2022-05-01', '2022-06-01',
                   '2022-07-01', '2022-08-01', '2022-09-01', '2022-10-01',
                   '2022-11-01', '2022-12-01'],
                  dtype='datetime64[ns]', name='time', length=252, freq=None))

  • lat
    PandasIndex

    PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           ...
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],
          dtype='float64', name='lat', length=180))

  • lon
    PandasIndex

    PandasIndex(Index([-179.5, -178.5, -177.5, -176.5, -175.5, -174.5, -173.5, -172.5, -171.5,
           -170.5,
           ...
            170.5,  171.5,  172.5,  173.5,  174.5,  175.5,  176.5,  177.5,  178.5,
            179.5],
          dtype='float64', name='lon', length=360))

• Attributes: (4)

  standard_name :

  lwe_thickness_of_precipitation_amount

  cell_method :

  time: mean

  units :

  mm

  long_name :

  monthly mean of daily accumulated precipitation

Plot the first time-step

For a quick demonstration, we will use earthkit plots to produce a map of the first time-step in the file. We are just using the quickplot method, with out-of-the-box settings.

ekp.quickplot(da_precip.isel(time=0))

<earthkit.plots.components.figures.Figure at 0x12cd916a0>

Figure 1. Monthly mean of daily accumulated precipitation (in mm) from data acquired over January 2002.

Climatology of precipitations

In this section we will analyse the time averaged global and seasonal climatological distributions of GIRAFE precipitation as well as the monthly mean climatology.

Time averaged global climatological distribution of precipitations

Let us calculate the mean climatology of precipitations for the time period January 2002 to December 2022.

First, we have to select the specific time range by using the xarray.sel method. This method indexes the data and dimensions by the appropriate indexers. Then, we apply the method mean to calculate the mean along the time dimension.

# Select the precip data for the whole time period
precip = da_precip.sel(time=slice('2002-01-01', '2022-12-31'))

# Calculate the mean along the time dimension
precip_mean = precip.mean(dim='time').assign_attrs(da_precip.attrs)
precip_mean

<xarray.DataArray 'precipitation' (lat: 180, lon: 360)> Size: 259kB
dask.array<mean_agg-aggregate, shape=(180, 360), dtype=float32, chunksize=(180, 360), chunktype=numpy.ndarray>
Coordinates:
  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
  * lon      (lon) float64 3kB -179.5 -178.5 -177.5 -176.5 ... 177.5 178.5 179.5
Attributes:
    standard_name:  lwe_thickness_of_precipitation_amount
    cell_method:    time: mean
    units:          mm
    long_name:      monthly mean of daily accumulated precipitation

xarray.DataArray

'precipitation'

• lat: 180
• lon: 360

• dask.array<chunksize=(180, 360), meta=np.ndarray>

  Array Chunk Bytes 253.12 kiB 253.12 kiB Shape (180, 360) (180, 360) Dask graph 1 chunks in 510 graph layers Data type float32 numpy.ndarray

• Coordinates: (2)
  • lat
    (lat)
    float64
    -89.5 -88.5 -87.5 ... 88.5 89.5

    units :

    degrees_north

    long_name :

    Latitude

    standard_name :

    latitude

    valid_range :

    [-90. 90.]

    reference_datum :

    geographical coordinates, WGS84 projection

    axis :

    Y

    bounds :

    lat_bnds

    array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
           -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
           -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
           -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
           -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
           -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
           -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
            -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
             0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
            10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
            20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
            30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
            40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
            50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
            60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
            70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])

  • lon
    (lon)
    float64
    -179.5 -178.5 ... 178.5 179.5

    units :

    degrees_east

    long_name :

    Longitude

    standard_name :

    longitude

    valid_range :

    [-180. 180.]

    reference_datum :

    geographical coordinates, WGS84 projection

    axis :

    X

    bounds :

    lon_bnds

    array([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5], shape=(360,))

• Indexes: (2)
  • lat
    PandasIndex

    PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           ...
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],
          dtype='float64', name='lat', length=180))

  • lon
    PandasIndex

    PandasIndex(Index([-179.5, -178.5, -177.5, -176.5, -175.5, -174.5, -173.5, -172.5, -171.5,
           -170.5,
           ...
            170.5,  171.5,  172.5,  173.5,  174.5,  175.5,  176.5,  177.5,  178.5,
            179.5],
          dtype='float64', name='lon', length=360))

• Attributes: (4)

  standard_name :

  lwe_thickness_of_precipitation_amount

  cell_method :

  time: mean

  units :

  mm

  long_name :

  monthly mean of daily accumulated precipitation

We can now visualize the global mean climatological distribution of precipitations for the period January 2002 - December 2022.

This time, we will use some of the advanced earthkit-plots options and projection information from Cartopy to create a better quality figure.

# Create the figure panel and the map using the Cartopy PlateCarree projection
chart = ekp.Map(crs=ccrs.PlateCarree())

# Define the style for the plot
precip_style = ekp.styles.Style(
    colors="Blues",
    levels=range(15),
    units="mm",
    extend="max",
)
chart.quickplot(precip_mean, style=precip_style)

chart.title(
    'Climatology of Precipitations from GIRAFE (January, 2002 to December, 2022)'
)
chart.coastlines(color='black')
chart.gridlines()

chart.legend()

chart.show()

Figure 2. The global climatological distribution of precipitations for the time period of January 2002 - December 2022 over ocean and land.

The global climatological field of precipitations has a dominant structure along the equator, over water masses and rainforests like Amazonia. Most of the evaporation or evapotranspiration takes place in the tropics. Indeed, Solar irradiance and its relative position on the Earth’s surface (solar zenith angle) controls evaporation, leading to the transition of water from the liquid phase to the gaseous phase. This is, subsequently advected to higher altitudes and then condensed into precipitation. This mechanism is the main driver of the Inter-Tropical Convergence Zone (ITCZ) along the equator.

There are also moderately high values of daily accumulated precipitation amounts in the region of the Gulf Stream extension (East of North America) and the Kuroshio (East of Japan), associated with storm track regions.

Time averaged seasonal climatological distribution of precipitations

Now, let us have a look at the seasonal climatological distribution of GIRAFE precipitations.

For this purpose, we will use the groupby() method, with 'time.season' as an argument, to split the data according to seasons, and then average them over the years.

Seasons are defined as follows:

• NH spring: March, April, May

• NH summer: June, July, August

• NH autumn: September, October, November

• NH winter: December, January, February

# Split data array precip by season
precip_seasonal_climatology = precip.groupby('time.season').mean('time')

# Assign season names to the coordinates
precip_seasonal_climatology = precip_seasonal_climatology.assign_coords({"season": ["Winter", "Spring", "Summer", "Autumn"]})

precip_seasonal_climatology

<xarray.DataArray 'precipitation' (season: 4, lat: 180, lon: 360)> Size: 1MB
dask.array<stack, shape=(4, 180, 360), dtype=float32, chunksize=(1, 180, 360), chunktype=numpy.ndarray>
Coordinates:
  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
  * lon      (lon) float64 3kB -179.5 -178.5 -177.5 -176.5 ... 177.5 178.5 179.5
  * season   (season) <U6 96B 'Winter' 'Spring' 'Summer' 'Autumn'
Attributes:
    standard_name:  lwe_thickness_of_precipitation_amount
    cell_method:    time: mean
    units:          mm
    long_name:      monthly mean of daily accumulated precipitation

xarray.DataArray

'precipitation'

• season: 4
• lat: 180
• lon: 360

• dask.array<chunksize=(1, 180, 360), meta=np.ndarray>

  Array Chunk Bytes 0.99 MiB 253.12 kiB Shape (4, 180, 360) (1, 180, 360) Dask graph 4 chunks in 526 graph layers Data type float32 numpy.ndarray

• Coordinates: (3)
  • lat
    (lat)
    float64
    -89.5 -88.5 -87.5 ... 88.5 89.5

    units :

    degrees_north

    long_name :

    Latitude

    standard_name :

    latitude

    valid_range :

    [-90. 90.]

    reference_datum :

    geographical coordinates, WGS84 projection

    axis :

    Y

    bounds :

    lat_bnds

    array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
           -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
           -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
           -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
           -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
           -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
           -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
            -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
             0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
            10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
            20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
            30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
            40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
            50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
            60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
            70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])

  • lon
    (lon)
    float64
    -179.5 -178.5 ... 178.5 179.5

    units :

    degrees_east

    long_name :

    Longitude

    standard_name :

    longitude

    valid_range :

    [-180. 180.]

    reference_datum :

    geographical coordinates, WGS84 projection

    axis :

    X

    bounds :

    lon_bnds

    array([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5], shape=(360,))

  • season
    (season)
    <U6
    'Winter' 'Spring' 'Summer' 'Autumn'

    array(['Winter', 'Spring', 'Summer', 'Autumn'], dtype='<U6')

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           ...
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],
          dtype='float64', name='lat', length=180))

  • lon
    PandasIndex

    PandasIndex(Index([-179.5, -178.5, -177.5, -176.5, -175.5, -174.5, -173.5, -172.5, -171.5,
           -170.5,
           ...
            170.5,  171.5,  172.5,  173.5,  174.5,  175.5,  176.5,  177.5,  178.5,
            179.5],
          dtype='float64', name='lon', length=360))

  • season
    PandasIndex

    PandasIndex(Index(['Winter', 'Spring', 'Summer', 'Autumn'], dtype='object', name='season'))

• Attributes: (4)

  standard_name :

  lwe_thickness_of_precipitation_amount

  cell_method :

  time: mean

  units :

  mm

  long_name :

  monthly mean of daily accumulated precipitation

The xarray DataArray “precip_seasonal_climatology” has four entries in the time dimension (one for each season). The climatological distribution of precipitations can now be plotted for each season.

figure = ekp.Figure(crs=ccrs.PlateCarree(), rows=2, columns=2, size=(10, 7))

for i in range(2):
    for j in range(2):
        figure.add_map(i, j)

figure.quickplot(precip_seasonal_climatology, style=precip_style)

figure.coastlines()
figure.gridlines()

figure.subplot_titles('Seasonal climatology of Precipitation - NH {season}')
figure.title(
    "Seasonal climatology of Precipitation (January 2002 - December 2022)"
)
figure.legend()
figure.show()

Figure 3. The seasonal mean climatology of the precipitation field from Northern Hemisphere spring to winter (top left to bottom right panels) derived from GIRAFE.

The general pattern is overall the same as in Figure 1 and Figure 2 with higher values in the tropics and lower values in the extra tropics. The band of high levels of precipitation (particularly noticeable between South-East Asia and North Australia) oscillates around the equator, reaching its highest values in the Summer, which confirms the dominant influence of the solar radiation over the course of the inter-tropical convergence zone.

Zonally averaged monthly mean climatology of precipitations

Let us now calculate the zonally averaged monthly mean climatology of precipitations over the time period January 2002 - December 2022, and visualise it in the form of a Hovmöller diagram.
A Hovmöller diagram is a common way of plotting meteorological or climatological data to depict their changes over time as a function of latitude or longitude. In our case, time will be recorded along the abscissa and latitude along the ordinate.

First, we will apply the groupby() method to group the “precip” DataArray by month, and then we will compute the average for each monthly group and longitude band.

The resulting data array is the zonally averaged monthly mean climatology for GIRAFE precipitations based on reference January 2002 - December 2022.

precip_clim_month = precip.groupby('time.month').mean("time")
precip_clim_month

<xarray.DataArray 'precipitation' (month: 12, lat: 180, lon: 360)> Size: 3MB
dask.array<stack, shape=(12, 180, 360), dtype=float32, chunksize=(1, 180, 360), chunktype=numpy.ndarray>
Coordinates:
  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
  * lon      (lon) float64 3kB -179.5 -178.5 -177.5 -176.5 ... 177.5 178.5 179.5
  * month    (month) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12
Attributes:
    standard_name:  lwe_thickness_of_precipitation_amount
    cell_method:    time: mean
    units:          mm
    long_name:      monthly mean of daily accumulated precipitation

xarray.DataArray

'precipitation'

• month: 12
• lat: 180
• lon: 360

• dask.array<chunksize=(1, 180, 360), meta=np.ndarray>

  Array Chunk Bytes 2.97 MiB 253.12 kiB Shape (12, 180, 360) (1, 180, 360) Dask graph 12 chunks in 566 graph layers Data type float32 numpy.ndarray

• Coordinates: (3)
  • lat
    (lat)
    float64
    -89.5 -88.5 -87.5 ... 88.5 89.5

    units :

    degrees_north

    long_name :

    Latitude

    standard_name :

    latitude

    valid_range :

    [-90. 90.]

    reference_datum :

    geographical coordinates, WGS84 projection

    axis :

    Y

    bounds :

    lat_bnds

    array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
           -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
           -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
           -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
           -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
           -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
           -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
            -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
             0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
            10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
            20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
            30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
            40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
            50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
            60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
            70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])

  • lon
    (lon)
    float64
    -179.5 -178.5 ... 178.5 179.5

    units :

    degrees_east

    long_name :

    Longitude

    standard_name :

    longitude

    valid_range :

    [-180. 180.]

    reference_datum :

    geographical coordinates, WGS84 projection

    axis :

    X

    bounds :

    lon_bnds

    array([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5], shape=(360,))

  • month
    (month)
    int64
    1 2 3 4 5 6 7 8 9 10 11 12

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           ...
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],
          dtype='float64', name='lat', length=180))

  • lon
    PandasIndex

    PandasIndex(Index([-179.5, -178.5, -177.5, -176.5, -175.5, -174.5, -173.5, -172.5, -171.5,
           -170.5,
           ...
            170.5,  171.5,  172.5,  173.5,  174.5,  175.5,  176.5,  177.5,  178.5,
            179.5],
          dtype='float64', name='lon', length=360))

  • month
    PandasIndex

    PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='month'))

• Attributes: (4)

  standard_name :

  lwe_thickness_of_precipitation_amount

  cell_method :

  time: mean

  units :

  mm

  long_name :

  monthly mean of daily accumulated precipitation

Let us view the zonal monthly climatology of the precipitation field. To do this, we will average across the longitude bands with the mean() method, align the time dimension coordinate with the x axis and the lat dimension coordinate along the y axis using the method transpose()…

precip_zonal_clim_month = precip_clim_month.mean(dim="lon").transpose()
precip_zonal_clim_month

<xarray.DataArray 'precipitation' (lat: 180, month: 12)> Size: 9kB
dask.array<transpose, shape=(180, 12), dtype=float32, chunksize=(180, 1), chunktype=numpy.ndarray>
Coordinates:
  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
  * month    (month) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12

xarray.DataArray

'precipitation'

• lat: 180
• month: 12

• dask.array<chunksize=(180, 1), meta=np.ndarray>

  Array Chunk Bytes 8.44 kiB 720 B Shape (180, 12) (180, 1) Dask graph 12 chunks in 569 graph layers Data type float32 numpy.ndarray

• Coordinates: (2)
  • lat
    (lat)
    float64
    -89.5 -88.5 -87.5 ... 88.5 89.5

    units :

    degrees_north

    long_name :

    Latitude

    standard_name :

    latitude

    valid_range :

    [-90. 90.]

    reference_datum :

    geographical coordinates, WGS84 projection

    axis :

    Y

    bounds :

    lat_bnds

    array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
           -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
           -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
           -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
           -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
           -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
           -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
            -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
             0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
            10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
            20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
            30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
            40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
            50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
            60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
            70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])

  • month
    (month)
    int64
    1 2 3 4 5 6 7 8 9 10 11 12

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])

• Indexes: (2)
  • lat
    PandasIndex

    PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           ...
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],
          dtype='float64', name='lat', length=180))

  • month
    PandasIndex

    PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='month'))

• Attributes: (0)

Now we can plot our data. Before we do this however, we define the min, max and contour step that we will use in a contour plot.

vdiv = 0.1
vmin = 0
vmax = 20
clevs = np.arange(vmin, vmax, vdiv)

# Define the figure and specify size
fig3, ax3 = plt.subplots(1, 1, figsize=(16, 8))

# Configure the axes and figure title
ax3.set_xlabel('Month')
ax3.set_ylabel('Latitude [ $^o$ ]')
ax3.set_yticks(np.arange(-90, 91, 30))
ax3.set_title(
    'Zonally averaged monthly climatology of Precipitations (January 2002 - December 2022)',
    fontsize=20,
    pad=20)

# As the months (12) are much less than the latitudes (180),
# we need to ensure the plot fits into the size of the figure.
ax3.set_aspect('auto')

# Plot the data as a contour plot
contour = ax3.contourf(precip_zonal_clim_month.month,
                       precip_zonal_clim_month.lat,
                       precip_zonal_clim_month,
                       levels=clevs,
                       cmap='Blues',
                       extend='both')

# Specify the colorbar
cbar = plt.colorbar(contour, fraction=0.025, pad=0.05)
cbar.set_label(['mm/day'])
plt.show()

Figure 4. The seasonal motion of the precipitation band crossing the equator from boreal summer to austral summer, associated with the ITCZ and the Rain Season.

In the next use case, we will analyse the temporal evolution of the precipitation amount and its annual seasonal variation.

Time series and trend analysis of precipitations

After looking at the time averaged global distribution of precipitations, we further investigate the dataset. The GIRAFE product spans over 20 years of satellite observations, and another useful way of analysing and visualizing the temporal evolution of rainfall accumulated precipitation amounts is through the use of the time series. We will calculate global time series, plot it, and discuss most important features.

Global time series of precipitations

We first create a temporal subset for the period January 2002 to December 2021.

# Select time period
precip_girafe = da_precip.sel(time=slice('2002-01-01', '2022-12-31'))
precip_girafe

<xarray.DataArray 'precipitation' (time: 252, lat: 180, lon: 360)> Size: 65MB
dask.array<concatenate, shape=(252, 180, 360), dtype=float32, chunksize=(1, 180, 360), chunktype=numpy.ndarray>
Coordinates:
  * time     (time) datetime64[ns] 2kB 2002-01-01 2002-02-01 ... 2022-12-01
  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
  * lon      (lon) float64 3kB -179.5 -178.5 -177.5 -176.5 ... 177.5 178.5 179.5
Attributes:
    standard_name:  lwe_thickness_of_precipitation_amount
    cell_method:    time: mean
    units:          mm
    long_name:      monthly mean of daily accumulated precipitation

xarray.DataArray

'precipitation'

• time: 252
• lat: 180
• lon: 360

• dask.array<chunksize=(1, 180, 360), meta=np.ndarray>

  Array Chunk Bytes 62.29 MiB 253.12 kiB Shape (252, 180, 360) (1, 180, 360) Dask graph 252 chunks in 505 graph layers Data type float32 numpy.ndarray

• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    2002-01-01 ... 2022-12-01

    long_name :

    Product dataset time given as seconds since 2000-01-01T00:00:00

    standard_name :

    time

    axis :

    T

    bounds :

    time_bnds

    array(['2002-01-01T00:00:00.000000000', '2002-02-01T00:00:00.000000000',
           '2002-03-01T00:00:00.000000000', ..., '2022-10-01T00:00:00.000000000',
           '2022-11-01T00:00:00.000000000', '2022-12-01T00:00:00.000000000'],
          shape=(252,), dtype='datetime64[ns]')

  • lat
    (lat)
    float64
    -89.5 -88.5 -87.5 ... 88.5 89.5

    units :

    degrees_north

    long_name :

    Latitude

    standard_name :

    latitude

    valid_range :

    [-90. 90.]

    reference_datum :

    geographical coordinates, WGS84 projection

    axis :

    Y

    bounds :

    lat_bnds

    array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
           -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
           -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
           -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
           -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
           -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
           -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
            -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
             0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
            10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
            20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
            30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
            40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
            50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
            60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
            70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])

  • lon
    (lon)
    float64
    -179.5 -178.5 ... 178.5 179.5

    units :

    degrees_east

    long_name :

    Longitude

    standard_name :

    longitude

    valid_range :

    [-180. 180.]

    reference_datum :

    geographical coordinates, WGS84 projection

    axis :

    X

    bounds :

    lon_bnds

    array([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5], shape=(360,))

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2002-01-01', '2002-02-01', '2002-03-01', '2002-04-01',
                   '2002-05-01', '2002-06-01', '2002-07-01', '2002-08-01',
                   '2002-09-01', '2002-10-01',
                   ...
                   '2022-03-01', '2022-04-01', '2022-05-01', '2022-06-01',
                   '2022-07-01', '2022-08-01', '2022-09-01', '2022-10-01',
                   '2022-11-01', '2022-12-01'],
                  dtype='datetime64[ns]', name='time', length=252, freq=None))

  • lat
    PandasIndex

    PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           ...
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],
          dtype='float64', name='lat', length=180))

  • lon
    PandasIndex

    PandasIndex(Index([-179.5, -178.5, -177.5, -176.5, -175.5, -174.5, -173.5, -172.5, -171.5,
           -170.5,
           ...
            170.5,  171.5,  172.5,  173.5,  174.5,  175.5,  176.5,  177.5,  178.5,
            179.5],
          dtype='float64', name='lon', length=360))

• Attributes: (4)

  standard_name :

  lwe_thickness_of_precipitation_amount

  cell_method :

  time: mean

  units :

  mm

  long_name :

  monthly mean of daily accumulated precipitation

Spatial aggregation

We would like to visualise these data, not in maps, but as a one dimensional time series of global average values. To do this, we will first need to aggregate the data spatially to create a single global average at each time step. In order to aggregate over the latitudinal dimension, we need to take into account the variation in area as a function of latitude. We will do this by using the cosine of the latitude as a proxy:

weights = np.cos(np.deg2rad(precip_girafe.lat))
weights.name = "weights"
precip_girafe_weighted = precip_girafe.weighted(weights)

The next step is to compute the mean across the latitude and longitude dimensions of the weighted data array with the mean() method.

precip_weighted_mean = precip_girafe_weighted.mean(dim=("lat", "lon"))

Plot data

Now we can plot the time series of globally averaged GIRAFE precipitation data over time using the plot() method.

# Define the figure and specify size
fig4, ax4 = plt.subplots(1, 1, figsize=(16, 8))

# Configure the axes and figure title
ax4.set_xlabel('Year')
ax4.set_ylabel('mm/day')
ax4.grid(linewidth=1, color='gray', alpha=0.5, linestyle='--')
ax4.set_title('Global time series of daily accumulated precipitation amount', fontsize=20, pad=20)

# Plot the data
ax4.plot(precip_weighted_mean.time, precip_weighted_mean)

plt.show()

Figure 5. The time series of the daily accumulated precipitation amount. From the time series, we can infer the seasonal pattern along with a (decreasing) trend in the global space and time averaged global interpolated rainfall estimation.

We will now proceed to the seasonal decomposition of the total column water vapour to study this trend in more details.

Trend analysis and seasonal cycle of precipitation

The time series can be further analysed by extracting the trend, or the running annual mean, and the seasonal cycle.

To this end, we will convert the Xarray DataArray into a time series with the pandas library. Then we will decompose it into the trend, the seasonal cycle and the residuals by using the seasonal_decompose() method. Finally, we will visualize the results.

# Convert the Xarray data array precip_weighted_mean into a time series
precip_weighted_mean_series = pd.Series(precip_weighted_mean)
# Define the time dimension of tcwv_weighted_mean as the index of the time series
precip_weighted_mean_series.index = precip_weighted_mean.time.to_dataframe().index

# Decomposition of the time series into the trend, the seasonal cycle and the residuals
precip_seasonal_decomposition = seasonal_decompose(precip_weighted_mean_series,
                                                   model='additive',
                                                   period=12)

# Plot the resulting seasonal decomposition
precip_seasonal_decomposition.plot()
plt.xlabel("Time")
plt.show()

Figure 6. The decomposition of the GIRAFE daily accumulated precipitation time series into the trend (2nd panel) and the seasonal cycle (3rd panel), from which are derived the anomalies or de-trended and de-seasonalised time series that have the characteristics of uncorrelated noise pattern (4th panel).

The trend analysis presents two peaks around the years 2010 and 2016, which can be related respectively to a moderate “El Niño” event in 2009-10 and a strong one in 2015-16. The combination of strong “El Niño” events and higher overall trends might lead to extremes in the precipitation amount in the future, and the decreasing trend shall be further examined too.

Regional time series and trend analysis

We can also use the GIRAFE dataset to visualise and analyse the temporal evolution of precipitation over some regions of interest that have a specific regime, like, for example, the Amazon rainforest in Brasil, the semi-arid region of Sahel in Africa, India for Monsoon, or Indonesia that is located in the Warm Pool.

For this purpose, we will re-use the code from the previous section, but we will need to add one additional step: select the corresponding regions by their respective coordinates from the original global dataset.

# Select the specific regions
precip_amazon = precip_girafe.sel(lat=slice(-15, 0), lon=slice(-75, -60))
precip_sahel = precip_girafe.sel(lat=slice(10, 20), lon=slice(-15, 30))
precip_india = precip_girafe.sel(lat=slice(0, 30), lon=slice(60, 90))
precip_indonesia = precip_girafe.sel(lat=slice(-15, 15), lon=slice(90, 150))

# Spatial aggregation
# Apply weights
precip_amazon_weighted = precip_amazon.weighted(weights)
precip_sahel_weighted = precip_sahel.weighted(weights)
precip_india_weighted = precip_india.weighted(weights)
precip_indonesia_weighted = precip_indonesia.weighted(weights)
# Compute weighted mean
precip_amazon_wmean = precip_amazon_weighted.mean(dim=('lat', 'lon'))
precip_sahel_wmean = precip_sahel_weighted.mean(dim=('lat', 'lon'))
precip_india_wmean = precip_india_weighted.mean(dim=('lat', 'lon'))
precip_indonesia_wmean = precip_indonesia_weighted.mean(dim=('lat', 'lon'))

This time, to analyse and visualise the trend over the time period of the GIRAFE dataset, we will compute the running annual mean by using the rolling method on each region.

# Compute the rolling mean for each region
precip_amazon_rmean = precip_amazon_wmean.rolling(time=12, center=True).mean()
precip_sahel_rmean = precip_sahel_wmean.rolling(time=12, center=True).mean()
precip_india_rmean = precip_india_wmean.rolling(time=12, center=True).mean()
precip_indonesia_rmean = precip_indonesia_wmean.rolling(time=12, center=True).mean()

# Creation of a dataset with the rolling mean for each region
precip_region_rmean = xr.Dataset(data_vars=dict(BRZL=precip_amazon_rmean,
                                                SAHEL=precip_sahel_rmean,
                                                IND=precip_india_rmean,
                                                IDSA=precip_indonesia_rmean
                                               ),
                                 coords=dict(time=precip_amazon_rmean.time),
                                )

We can now plot the resulting time series of precipitation for each region.

# Create a list of the regions of interest:
regions = ['BRZL', 'SAHEL', 'IND', 'IDSA']

# We use the "subplots" to place multiple plots according to our needs. 
# In this case, we want 4 plots in a 2x2 format.
# For this "nrows" = 2 and "ncols" = 2, the projection and size are defined as well
fig5, ax5 = plt.subplots(nrows=2,
                         ncols=2,
                         figsize=(16,8))

# Define a dictionary of subtitles, each one corresponding to a season
subtitles = {'BRZL': 'Brazil-Amazon', 'SAHEL': 'Sahel', 'IND': 'India', 'IDSA': 'Indonesia'}

# Configure the axes and subplot titles
for i_region, c_region in enumerate(regions):
    # convert i_season index into (row, col) index
    row = i_region // 2
    col = i_region % 2
    ax5[row][col].set_xlabel('Year')
    ax5[row][col].set_ylabel('mm/day')
    ax5[row][col].grid(linewidth=1, color='gray', alpha=0.5, linestyle='--')
    ax5[row][col].set_title(subtitles[c_region], fontsize=16)
    # Plot the data
    ax5[row][col].plot(precip_region_rmean.time, precip_region_rmean[c_region])
    
# Place the subplots
fig5.subplots_adjust(bottom=0.0, top=0.9, left=0.05, right=0.95, wspace=0.1, hspace=0.5)

# Define an overall title
fig5.suptitle('Regional time series of daily accumulated precipitation amount',
              fontsize=20)

plt.show()

Figure 7. The temporal evolution of precipitations over Brazil, Sahel, India and Indonesia for the time period 2002-2022.

No trend can be inferred in the precipitation amount in the Amazon rainforest due to the lack of months (and even years) of GIRAFE observations over this region of Brazil.

Over the Sahel, no significant trend is observed during the first decade of GIRAFE TCDR. Then, after a period of drought of ~4 years between 2014 and 2018, the amount of precipitation increases abruptly up until December 2022.

In the last decade, extreme values of precipitation that could lead to droughts or floods tend to increase over India or Indonesia (especially).

Take home messages 📌

• The use cases here illustrate how the global interpolated rainfall estimation (GIRAFE) Precipitation product can be used to study, analyse and visualise this Essential Climate Variable.

• The high values found along the equator can be explained by the Inter-Tropical Convergence Zone.

• The moderately high values observed in the region of the Gulf Stream extension and Kurushio are associated to the storm track regions.

• Comparisons with other independent datasets to further investigate trends (like more frequent episodes of floods and droughts) that can be attributed to global climate change, El-Niño events, etc.