Ensemble mean and spread for 500 hPa geopotential¶

This notebook will provide you guidance how to explore and plot ECMWF open dataset to produce the map from the ECMWF open charts web product.
The original product can be found on this link: https://charts.ecmwf.int/products/medium-t500-mean-spread

The full list of available Open data products can be found here, and more information can be found in the User documentation.

Access to ECMWF Open data is governed by the Creative Commons CC-BY-4.0 licence and associated Terms of Use.

_{In applying this licence, ECMWF does not waive the privileges and immunities granted to it by virtue of its status as an intergovernmental organisation nor does it submit to any jurisdiction}

To find out how to obtain the access to the full forecast dataset at higher resolution please visit our Access page.

Retrieve Data¶

This product takes in input 1 parameter:

Geopotential height

In this example, we will use:

ecmwf.opendata Client to download the data
Metview library to read, process and plot the data

First we need to install them in the current Jupyter kernel:

Note: If you are running the notebook on MyBinder or already have the libraries installed, go directly to importing the libraries.

Note: If you don't have these libraries installed, click on three dots below, uncomment the code and run the next cell.

In [1]:

#!pip install ecmwf-opendata metview metview-python

In [1]:

import metview as mv
from ecmwf.opendata import Client

In [2]:

client = Client("ecmwf", beta=True)

For the geopotential height at 500 hPa, ensemble mean and spread are available for download.

Ensemble mean and ensemble spread have different type in the request.
One data request can have only one type, so we need to have two requests to download this datasets.

In [3]:

parameters = ['gh']
em_filename = 'medium-t500-mean-spread_em.grib'
es_filename = 'medium-t500-mean-spread_es.grib'

Setting date to 0 will download today's data. Removing date and time altogether from the request will download the latest data.
Try commenting out date and time to download latest forecast!

In [4]:

client.retrieve(
    date=-1,
    time=0,
    step=144,
    stream="enfo",
    type="em",
    levtype="pl",
    levelist=[500],
    param=parameters,
    target=em_filename
)

20250107000000-240h-enfo-ep.grib2:   0%|          | 0.00/447k [00:00<?, ?B/s]

Out[4]:

<ecmwf.opendata.client.Result at 0x18080a3d0>

In [5]:

client.retrieve(
    date=-1,
    time=0,
    step=144,
    stream="enfo",
    type="es",
    levtype="pl",
    levelist=[500],
    param=parameters,
    target=es_filename
)

20250107000000-240h-enfo-ep.grib2:   0%|          | 0.00/458k [00:00<?, ?B/s]

Out[5]:

<ecmwf.opendata.client.Result at 0x180de1290>

Reading and processing the data¶

Now we can use ecmwf.data to read the file.

In [6]:

gh_em = mv.read(em_filename)

In [7]:

gh_es = mv.read(es_filename)

The describe() function will give us the overview of the dataset.

In [8]:

gh_em.describe('gh')

Out[8]:

shortName	gh
name	Geopotential height
paramId	156
units	gpm
typeOfLevel	isobaricInhPa
level	500
date	20250107
time	0
step	144
number	None
class	od
stream	enfo
type	em
experimentVersionNumber	0001

In [9]:

gh_es.describe()

Out[9]:

parameter	typeOfLevel	level	date	time	step	number	paramId	class	stream	type	experimentVersionNumber
gh	isobaricInhPa	500	20250107	0	144	None	156	od	enfo	es	0001

We can use ls() function to list all the fields in the file we downloaded.

In [10]:

gh_es.ls()

Out[10]:

	centre	shortName	typeOfLevel	level	dataDate	dataTime	stepRange	dataType	number	gridType
Message
0	ecmf	gh	isobaricInhPa	500	20250107	0	144	es	None	regular_ll

Geopotential height has units gpm (geopotential meters), but on the ECMWF Open charts it is plotted in geopotential decameters. To reproduce the plot we need to divide by 10.

In [11]:

gh_em /= 10

In [12]:

gh_es = gh_es/10

Plotting the data¶

And finally, we can plot the data on the map.

In [13]:

# define coastlines
coast = mv.mcoast(
    map_coastline_colour="charcoal",
    map_coastline_resolution="medium",
    map_coastline_land_shade="on",
    map_coastline_land_shade_colour="cream",
    map_coastline_sea_shade="off",
    map_boundaries="on",
    map_boundaries_colour= "charcoal",
    map_boundaries_thickness = 1,
    map_disputed_boundaries = "off",
    map_grid_colour="tan",
    map_label_height=0.35,
)

# define view
view = mv.geoview(
    area_mode="name",
    area_name="europe",
    coastlines=coast
)

#define styles
gh_es_shade = mv.mcont(legend= "on",
                contour_automatics_settings = "style_name",
                contour_style_name = "sh_blu_f02t50")

gh_em_shade = mv.mcont(legend= "on",
                contour_automatics_settings = "style_name",
                contour_style_name = "ct_red_i5_t2")

title = mv.mtext(
    text_lines=["Ensemble mean and spread for 300 hPa geopotential", 
                "START TIME: <grib_info key='base-date' format='%a %d %B %Y %H'/> ",
                "VALID TIME: <grib_info key='valid-date' format='%a %d %B %Y %H'/>, STEP: <grib_info key='step' />"],
    text_font_size=0.4,
    text_colour         = 'charcoal')

ecmwf_text = mv.mtext(    
    text_lines          = ["© European Centre for Medium-Range Weather Forecasts (ECMWF)",
                           "Source: www.ecmwf.int Licence: CC-BY-4.0 and ECMWF Terms of Use",
                            "https://apps.ecmwf.int/datasets/licences/general/"],
    text_justification  = 'center',
    text_font_size      = 0.3,
    text_mode           = "positional",
    text_box_x_position = 6.,
    text_box_y_position = -0.2,
    text_box_x_length   = 8,
    text_box_y_length   = 2,
    text_colour         = 'charcoal')

# generate plot
mv.setoutput('jupyter', plot_widget=False)
mv.plot(view, gh_es, gh_es_shade, gh_em, gh_em_shade, title, ecmwf_text)

Out[13]:

To generate the png file you can run the following cell.

In [14]:

png = mv.png_output(
      output_name = "medium-t500-mean-spread",   # specify relative or full path
      output_title = "medium-t500-mean-spread",    # title used by a viewer
      output_width = 1000,                 # set width in pixels
)
mv.setoutput(png)
mv.plot(view, gh_es, gh_es_shade, gh_em, gh_em_shade, title, ecmwf_text)

Note that plot produced using open data dataset will slightly differ from one from Open Charts. This is due to different resolution of the data.
Open data is on 0.25x0.25 resolution, while high resolution data is 0.1x0.1 grid.