10m wind and mean sea level pressure¶

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-wind-10m

The full list of available Open data products can be found here, and more information can be found in the User documentation.
This product takes in input 3 parameters :

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=True)

In [4]:

parameters = ['msl', '10u', '10v']
filename = 'medium-wind-10m.grib'
filename

Out[4]:

'medium-wind-10m.grib'

In [5]:

client.retrieve(
    date=0,
    time=0,
    step=12,
    stream="oper",
    type="fc",
    levtype="sfc",
    param=parameters,
    target=filename
)

20241224000000-12h-oper-fc.grib2:   0%|          | 0.00/2.12M [00:00<?, ?B/s]

Out[5]:

<ecmwf.opendata.client.Result at 0x10f2cfc10>

Reading and processing the data¶

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

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	step	number	paramId	class	stream	type	experimentVersionNumber
10u	heightAboveGround	10	20241224	12	None	165	od	oper	fc	0001
10v	heightAboveGround	10	20241224	12	None	166	od	oper	fc	0001
msl	meanSea	0	20241224	12	None	151	od	oper	fc	0001

And an overview of one parameter, where we can see more information, such as units or type of level.

In [8]:

data.describe('msl')

Out[8]:

shortName	msl
name	Mean sea level pressure
paramId	151
units	Pa
typeOfLevel	meanSea
level	0
date	20241224
time	0
step	12
number	None
class	od
stream	oper
type	fc
experimentVersionNumber	0001

In [9]:

msl = data.select(shortName = 'msl')
msl.describe()

Out[9]:

parameter	typeOfLevel	level	date	time	step	number	paramId	class	stream	type	experimentVersionNumber
msl	meanSea	0	20241224	0	12	None	151	od	oper	fc	0001

Mean sea level pressure data has unites Pa, but we want to plot it in hPa, therefore we need to convert it.

In [10]:

msl /= 100

We can calculate wind speed using u and v component:

In [11]:

u = data.select(shortName='10u')
v = data.select(shortName='10v')
speed = mv.speed(u,v)
speed.describe()

Out[11]:

parameter	typeOfLevel	level	date	time	step	number	paramId	class	stream	type	experimentVersionNumber
10si	heightAboveGround	10	20241224	0	12	None	207	od	oper	fc	0001

We need to create a wind vector field for the visualisation, as well.

In [12]:

wind_vector = mv.grib_vectors(u_component = u, v_component = v)

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

In [14]:

# 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",
    subpage_clipping = "off",
    coastlines = coast
)

#define styles
speed_shade = mv.mcont(legend= "on",
                contour_automatics_settings = "style_name",
                contour_style_name = "sh_grn_f10t100lst")

msl_shade = mv.mcont(legend= "off",
                contour_automatics_settings = "style_name",
                contour_style_name = "ct_blk_i5_t2")

wind_arrows = mv.mwind(
    wind_thinning_factor=5, wind_arrow_colour="black"
)

title = mv.mtext(
    text_lines=["10m wind and mean sea level pressure", 
                "START TIME: <grib_info key='base-date' format='%a %d %B %Y %H' where='shortName=msl'/>", 
                "VALID TIME: <grib_info key='valid-date' format='%a %d %B %Y %H' where='shortName=msl'/>, STEP: <grib_info key='step' where='shortName=msl'/>"],
    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, speed, speed_shade, msl, msl_shade, wind_vector, wind_arrows, title, ecmwf_text)

Out[14]:

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

In [15]:

png = mv.png_output(
      output_name = "medium-wind-10m",   # specify relative or full path
      output_title = "medium-wind-10m",    # title used by a viewer
      output_width = 1000,                 # set width in pixels
)
mv.setoutput(png)
mv.plot(view, speed, speed_shade, msl, msl_shade, wind_vector, wind_arrows, 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.