Vorticity at various pressure levels¶

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-vorticity

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 :

Vorticity (relative)

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 [2]:

import metview as mv
from ecmwf.opendata import Client

In [3]:

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

In [4]:

parameters = ['vo']
filename = 'medium-vorticity.grib'
filename

Out[4]:

'medium-vorticity.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 [5]:

client.retrieve(
    date=0,
    time=0,
    step=12,
    stream="oper",
    type="fc",
    levtype="pl",
    levelist=[1000,925,850,700,500,300,250,200,50],
    param=parameters,
    target=filename
)

20241224000000-12h-oper-fc.grib2:   0%|          | 0.00/11.5M [00:00<?, ?B/s]

Out[5]:

<ecmwf.opendata.client.Result at 0x1826cf210>

Reading and processing the data¶

Now we can use Metview's read() function to read the file.

In [6]:

data = mv.read(filename)

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

In [7]:

data.describe()

Out[7]:

parameter	typeOfLevel	level	date	time	step	number	paramId	class	stream	type	experimentVersionNumber
vo	isobaricInhPa	50,200,...	20241224	0	12	None	138	od	oper	fc	0001

And an overview of one parameter

In [8]:

data.describe('vo')

Out[8]:

shortName	vo
name	Vorticity (relative)
paramId	138
units	s**-1
typeOfLevel	isobaricInhPa
level	50,200,250,300,500,700,850,925,1000
date	20241224
time	0
step	12
number	None
class	od
stream	oper
type	fc
experimentVersionNumber	0001

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

In [9]:

data.ls()

Out[9]:

	centre	shortName	typeOfLevel	level	dataDate	dataTime	stepRange	dataType	number	gridType
Message
0	ecmf	vo	isobaricInhPa	250	20241224	0	12	fc	None	regular_ll
1	ecmf	vo	isobaricInhPa	500	20241224	0	12	fc	None	regular_ll
2	ecmf	vo	isobaricInhPa	200	20241224	0	12	fc	None	regular_ll
3	ecmf	vo	isobaricInhPa	50	20241224	0	12	fc	None	regular_ll
4	ecmf	vo	isobaricInhPa	925	20241224	0	12	fc	None	regular_ll
5	ecmf	vo	isobaricInhPa	850	20241224	0	12	fc	None	regular_ll
6	ecmf	vo	isobaricInhPa	700	20241224	0	12	fc	None	regular_ll
7	ecmf	vo	isobaricInhPa	300	20241224	0	12	fc	None	regular_ll
8	ecmf	vo	isobaricInhPa	1000	20241224	0	12	fc	None	regular_ll

The grib file contains all the levels, and we will use the select() method to filter one of them.

In [10]:

vo700 = data.select(level = 700)
vo700.describe()

Out[10]:

parameter	typeOfLevel	level	date	time	step	number	paramId	class	stream	type	experimentVersionNumber
vo	isobaricInhPa	700	20241224	0	12	None	138	od	oper	fc	0001

In order to match the units in the Open charts plot, we need to multiply the data with 100000.

In [11]:

vo700 *= 100000

Plotting the data¶

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

In [12]:

# 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
vo_shade = mv.mcont(legend= "on",
                contour_automatics_settings = "style_name",
                contour_style_name = "sh_blured_fM50t50lst_cell")

title = mv.mtext(
    text_lines=["Vorticity at various pressure levels, level <grib_info key='level' /> hPa ",
          "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, vo700, vo_shade, title, ecmwf_text)

Out[12]:

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

In [13]:

png = mv.png_output(
      output_name = "medium-vorticity",   # specify relative or full path
      output_title = "medium-vorticity",    # title used by a viewer
      output_width = 1000,                 # set width in pixels
)
mv.setoutput(png)
mv.plot(view, vo700, vo_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.