Windsea: Significant wave height and mean direction¶

This notebook will provide you guidance how to explore ECMWF data 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-wwsh-mwd

Some ECMWF real-time products are governed by the ECMWF Standard Licence Agreement and are not free or open. Access to these products depends on your use case. To find out which licence applies to you, please visit: Use cases and licence conditions page.

To find out how to obtain the access to the ECMWF forecast data please visit our Access page.

Retrieve Data¶

This product takes in input 3 parameters :

In this example, we will use:

ecmwf-api-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 and run the next cell.

In [1]:

#!pip install ecmwf-api-client metview metview-python

In [2]:

import metview as mv
from ecmwfapi import *
import requests

In [3]:

parameters = ["140234/140235"]
filename = 'medium-wwsh-mwd.grib'
filename

Out[3]:

'medium-wwsh-mwd.grib'

In [ ]:

server = ECMWFService("mars")
server.execute(
    {
    "class": "od",
    "date": "-1",
    "expver": "1",
    "levtype": "sfc",
    "param": parameters,
    "step": "12",
    "stream": "wave",
    "time": "00",
    "type": "fc",
    "grid": "0.25/0.25"
    },
    filename)

Alternatively we have prepared a small sample dataset to explore

In [5]:

url = f"https://get.ecmwf.int/repository/opencharts-sample-data/{filename}"
r = requests.get(url)
with open(filename, 'wb') as file:
    file.write(r.content)

Reading and processing the data¶

Now we can use Metview's read() function 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	time	step	paramId	class	stream	type	experimentVersionNumber
mdww	meanSea	0	20220414	0	12	140235	od	wave	fc	0001
shww	meanSea	0	20220414	0	12	140234	od	wave	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('mdww')

Out[8]:

shortName	mdww
name	Mean direction of wind waves
paramId	140235
units	degrees
typeOfLevel	meanSea
level	0
date	20220414
time	0
step	12
class	od
stream	wave
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	gridType
Message
0	ecmf	shww	meanSea	0	20220414	0	12	fc	regular_ll
1	ecmf	mdww	meanSea	0	20220414	0	12	fc	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 [10]:

shww = data.select(shortName= "shww")
mdww = data.select(shortName= "mdww")

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

In [11]:

wave_vector = mv.grib_vectors(type='polar_field', intensity = shww, direction = mdww)

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

In [16]:

# 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="north_atlantic",
    subpage_clipping="on",
    coastlines=coast
)

#define styles
shww_shade = mv.mcont(legend= "on",
                contour_automatics_settings = "style_name",
                contour_style_name = "sh_all_f0t20lst")

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

title = mv.mtext(
    text_lines=["Windsea: Significant wave height and mean direction", 
                "START TIME: <grib_info key='base-date' format='%a %d %B %Y %H' where='shortName=shww'/> ",
                "VALID TIME: <grib_info key='valid-date' format='%a %d %B %Y %H' where='shortName=shww'/>, STEP: <grib_info key='step' where='shortName=shww' />"],
    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, shww, shww_shade, wave_vector, wave_arrows, title, ecmwf_text)

Out[16]:

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

In [17]:

png = mv.png_output(
      output_name = "medium-wwsh-mwd",   # specify relative or full path
      output_title = "medium-wwsh-mwd",    # title used by a viewer
      output_width = 1000,                 # set width in pixels
)
mv.setoutput(png)
mv.plot(view, shww, shww_shade, wave_vector, wave_arrows, title, ecmwf_text)

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