500-1000 hPa thickness 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-thickness-mslp

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 2 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=False)

Retrieve requests¶

This product uses parameters at different vertical levels.
Mean sea level pressure belongs to the surface parameters, while geopotential height that we use to calculate thicknes is a pressure level parameter.
The two types of levels can not be in the same request, so we need to make two requests for them.

In [4]:

sfc_parameters = 'msl'
pl_parameters = 'gh'
sfc_filename = 'medium-thickness-mslp-sfc.grib'
pl_filename = 'medium-thickness-mslp-pl.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="sfc",
    param=sfc_parameters,
    target=sfc_filename
)

20241224000000-12h-oper-fc.grib2:   0%|          | 0.00/494k [00:00<?, ?B/s]

Out[5]:

<ecmwf.opendata.client.Result at 0x176e87590>

In [6]:

client.retrieve(
    date=0,
    time=0,
    step=12,
    stream="oper",
    type="fc",
    levtype="pl",
    levelist=[500,1000],
    param=pl_parameters,
    target=pl_filename
)

20241224000000-12h-oper-fc.grib2:   0%|          | 0.00/906k [00:00<?, ?B/s]

Out[6]:

<ecmwf.opendata.client.Result at 0x186d2c2d0>

Reading and processing the data¶

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

In [7]:

gh = mv.read(pl_filename)
msl = mv.read(sfc_filename)

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

In [8]:

msl.describe()

Out[8]:

parameter	typeOfLevel	level	date	time	step	number	paramId	class	stream	type	experimentVersionNumber
msl	meanSea	0	20241224	0	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 [9]:

msl.describe('msl')

Out[9]:

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

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

In [10]:

gh.ls()

Out[10]:

	centre	shortName	typeOfLevel	level	dataDate	dataTime	stepRange	dataType	number	gridType
Message
0	ecmf	gh	isobaricInhPa	1000	20241224	0	12	fc	None	regular_ll
1	ecmf	gh	isobaricInhPa	500	20241224	0	12	fc	None	regular_ll

The grib file contains all the parameters, and we will use the select() function to filter what we need.
Feel free to use describe() to inspect the other filtered fields.

In [11]:

gh500 = gh.select(shortName= 'gh', level=500)
gh1000 = gh.select(shortName= 'gh', level=1000)

gh1000.describe()

Out[11]:

parameter	typeOfLevel	level	date	time	step	number	paramId	class	stream	type	experimentVersionNumber
gh	isobaricInhPa	1000	20241224	0	12	None	156	od	oper	fc	0001

Thickness is calculated by taking difference between two geopotential heights.
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 [12]:

thickness = (gh500 - gh1000) / 10

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

In [13]:

msl /= 100

Plotting the data¶

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",
    coastlines=coast
)

#define styles
thickness_shade = mv.mcont(legend= "on",
                contour_automatics_settings = "style_name",
                contour_style_name = "sh_blured_fM16t50_thickness")

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

title = mv.mtext(
    text_lines=["500-1000 hPa thickness 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, thickness, thickness_shade, msl, msl_shade, 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-thickness-mslp",   # specify relative or full path
      output_title = "medium-thickness-mslp",    # title used by a viewer
      output_width = 1000,                 # set width in pixels
)
mv.setoutput(png)
mv.plot(view, thickness, thickness_shade, msl, msl_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.