Ensemble mean and spread for 10m wind speed¶

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-10ws-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 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=True)

In [4]:

parameters = ['10u','10v']
filename = 'medium-10ws-mean-spread.grib'
filename

Out[4]:

'medium-10ws-mean-spread.grib'

To calculate the mean and spread of the 10m wind speed, we need to retrieve all the ensemble members at the given time.
We need to put both cf and pf as type type to download all 50 ensemble members as well as the control forecast.

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=72,
 stream="enfo",
 type=['cf', 'pf'],
 levtype="sfc",
 param=parameters,
 target=filename
)

20241223000000-72h-enfo-ef.grib2:   0%|          | 0.00/83.8M [00:00<?, ?B/s]

Out[5]:

<ecmwf.opendata.client.Result at 0x1130bac50>

Reading and processing the data¶

Now we can use ecmwf.data 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
10u	heightAboveGround	10	20241223	0	72	0,1,...	165	od	enfo	cf,pf	0001
10v	heightAboveGround	10	20241223	0	72	0,1,...	166	od	enfo	cf,pf	0001

We can use ls() function to list all the fields in the file we downloaded.
Note that this fieldset has all fields randomly arranged. You can see that if you look at the number column. These are ensemble member numbers.

In [8]:

data.ls()

Out[8]:

	centre	shortName	typeOfLevel	level	dataDate	dataTime	stepRange	dataType	number	gridType
Message
0	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	24	regular_ll
1	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	14	regular_ll
2	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	41	regular_ll
3	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	42	regular_ll
4	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	50	regular_ll
5	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	23	regular_ll
6	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	15	regular_ll
7	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	6	regular_ll
8	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	33	regular_ll
9	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	32	regular_ll
10	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	24	regular_ll
11	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	42	regular_ll
12	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	33	regular_ll
13	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	15	regular_ll
14	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	34	regular_ll
15	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	25	regular_ll
16	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	6	regular_ll
17	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	7	regular_ll
18	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	43	regular_ll
19	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	17	regular_ll
20	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	35	regular_ll
21	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	26	regular_ll
22	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	44	regular_ll
23	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	16	regular_ll
24	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	34	regular_ll
25	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	25	regular_ll
26	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	8	regular_ll
27	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	7	regular_ll
28	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	43	regular_ll
29	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	27	regular_ll
30	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	35	regular_ll
31	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	16	regular_ll
32	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	9	regular_ll
33	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	17	regular_ll
34	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	44	regular_ll
35	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	26	regular_ll
36	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	18	regular_ll
37	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	45	regular_ll
38	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	36	regular_ll
39	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	8	regular_ll
40	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	9	regular_ll
41	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	27	regular_ll
42	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	45	regular_ll
43	ecmf	10u	heightAboveGround	10	20241223	0	72	cf	0	regular_ll
44	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	36	regular_ll
45	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	37	regular_ll
46	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	1	regular_ll
47	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	18	regular_ll
48	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	28	regular_ll
49	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	19	regular_ll
50	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	46	regular_ll
51	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	37	regular_ll
52	ecmf	10v	heightAboveGround	10	20241223	0	72	cf	0	regular_ll
53	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	1	regular_ll
54	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	28	regular_ll
55	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	38	regular_ll
56	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	10	regular_ll
57	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	29	regular_ll
58	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	46	regular_ll
59	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	2	regular_ll
60	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	19	regular_ll
61	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	47	regular_ll
62	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	3	regular_ll
63	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	29	regular_ll
64	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	38	regular_ll
65	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	10	regular_ll
66	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	20	regular_ll
67	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	48	regular_ll
68	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	47	regular_ll
69	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	39	regular_ll
70	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	21	regular_ll
71	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	11	regular_ll
72	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	2	regular_ll
73	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	30	regular_ll
74	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	3	regular_ll
75	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	49	regular_ll
76	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	12	regular_ll
77	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	4	regular_ll
78	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	20	regular_ll
79	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	21	regular_ll
80	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	48	regular_ll
81	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	39	regular_ll
82	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	11	regular_ll
83	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	5	regular_ll
84	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	13	regular_ll
85	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	40	regular_ll
86	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	31	regular_ll
87	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	12	regular_ll
88	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	4	regular_ll
89	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	30	regular_ll
90	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	22	regular_ll
91	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	49	regular_ll
92	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	23	regular_ll
93	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	14	regular_ll
94	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	41	regular_ll
95	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	50	regular_ll
96	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	22	regular_ll
97	ecmf	10u	heightAboveGround	10	20241223	0	72	pf	32	regular_ll
98	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	31	regular_ll
99	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	5	regular_ll
100	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	13	regular_ll
101	ecmf	10v	heightAboveGround	10	20241223	0	72	pf	40	regular_ll

ecmwf.data library has a built in function to calculate the speed from u and v components. It takes u/v fieldsets as input and gives back speed intensity.
Note that we can give a fieldset on N u/v components, not just one pair.

In this case we will calculate all 51 ensemble members at the same time.
First we need to filter out u and v and sort the fieldsets.

In [9]:

u = data.select(shortName= '10u').sort()
v = data.select(shortName= '10v').sort()

And then we calculate the wind speed. Metview has builti in function for this.

In [10]:

speed = mv.speed(u,v)
speed.describe()

Out[10]:

parameter	typeOfLevel	level	date	time	step	number	paramId	class	stream	type	experimentVersionNumber
10si	heightAboveGround	10	20241223	0	72	0,1,...	207	od	enfo	cf,pf	0001

In the end we still need to calculate the mean and spread (as standard deviation) of the wind speed.
ecmwf.data has built in functions to calculate mean and standard deviation of a given fieldset.
Since our data has only one date,time and step, mean() and stdev() will be calculated over the 51 ensemble members.

In [11]:

ws_mean = mv.mean(speed)
ws_spread = mv.stdev(speed)

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
speed_mean_contour = mv.mcont(legend = "on",
    contour_highlight_colour = 'black',
    contour_highlight_thickness = 4,
    contour_interval = 5,
    contour_label = 'on',
    contour_label_frequency = 1,
    contour_label_height = 0.4,
    contour_level_selection_type = 'interval',
    contour_line_colour = 'black',
    contour_line_thickness = 2)

speed_spread_shade = mv.mcont(legend= "on",
    contour_automatics_settings = "style_name",
    contour_style_name = "sh_blu_f02t30")

title = mv.mtext(
    text_lines=["Ensemble mean and spread for 10m wind speed",
                "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, ws_spread, speed_spread_shade, ws_mean, speed_mean_contour, 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-10ws-mean-spread",   # specify relative or full path
      output_title = "medium-10ws-mean-spread",    # title used by a viewer
      output_width = 1000,                 # set width in pixels
)
mv.setoutput(png)
mv.plot(view, ws_spread, speed_spread_shade, ws_mean, speed_mean_contour, 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.