Simulated images - Water vapour¶

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-simulated-wv

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 parameter:

Cloudy brightness temperature WV 6.2 µm (300 hPa) and 7.3 µm (500 hPa)

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, uncomment the code and run the next cell.

In [1]:

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

In [1]:

import metview as mv
from ecmwfapi import *
import requests

In [2]:

filename = "medium-simulated-wv.grib"

If you already have the access to the ECMWF archived forecast data, you can use the next cell to download data from the MARS archive:

In [ ]:

server = ECMWFService("mars")
server.execute(
    {
    "class": "od",
    "date": "-1",
    "expver": "1",
    "levtype": "sfc",
    "param": "260510",
    "step": "12",
    "stream": "oper",
    "time": "00",
    "type": "ssd",
    "channel":"5/6",
    "ident":"57",
    "grid": "0.25/0.25"
    },
    filename)

Alternatevly we have prepared 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 ecmwf.data to read the file.

In [3]:

data = mv.read(filename)

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

In [4]:

data.describe()

Out[4]:

parameter	typeOfLevel	level	date	time	step	number	paramId	class	stream	type	experimentVersionNumber
clbt	None		20220306	0	12	None	260510	od	oper	ssd	0001

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

In [5]:

data.describe('clbt')

Out[5]:

shortName	clbt
name	Cloudy brightness temperature
paramId	260510
units	K
typeOfLevel	None
level
date	20220306
time	0
step	12
number	None
class	od
stream	oper
type	ssd
experimentVersionNumber	0001

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

In [6]:

data.ls()

Out[6]:

	centre	shortName	typeOfLevel	level	dataDate	dataTime	stepRange	dataType	number	gridType
Message
0	ecmf	clbt	None	<NA>	20220306	0	12	ssd	None	regular_ll
1	ecmf	clbt	None	<NA>	20220306	0	12	ssd	None	regular_ll

In order to be able to use the visual style from open charts, we need to convert the brightness temperature to Censius.

In [7]:

clbt_5 = data.select(channel= 5) - 273.15
clbt_6 = data.select(channel= 6) - 273.15

Plotting the data¶

And finally, we can plot the data on the map.
First we will plot the data on 500 hPa (channel 6).

In [8]:

# 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
clbt_6_shade = mv.mcont(legend= "on",
                contour_automatics_settings = "style_name",
                contour_style_name = "sim_image_wv_500")

title = mv.mtext(
    text_lines=["Simulated images - Water vapour channel 6", 
           "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, clbt_6, clbt_6_shade, title, ecmwf_text)

Out[8]:

And in the end we plot the Brightness temperature at level 300 hPa (channel 5).

In [9]:

# 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
clbt_5_shade = mv.mcont(legend= "on",
                contour_automatics_settings = "style_name",
                contour_style_name = "sim_image_wv_500")

title = mv.mtext(
    text_lines=["Simulated images - Water vapour channel 5", 
           "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' 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, clbt_5, clbt_5_shade, title, ecmwf_text)

Out[9]:

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

In [10]:

png = mv.png_output(
      output_name = "medium-simulated-wv-ch6",   # specify relative or full path
      output_title = "medium-simulated-wv-ch6",    # title used by a viewer
      output_width = 1000,                 # set width in pixels
)
mv.setoutput(png)
mv.plot(view, clbt_6, clbt_6_shade, title, ecmwf_text)

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

In [11]:

png = mv.png_output(
      output_name = "medium-simulated-wv-ch5",   # specify relative or full path
      output_title = "medium-simulated-wv-ch5",    # title used by a viewer
      output_width = 1000,                 # set width in pixels
)
mv.setoutput(png)
mv.plot(view, clbt_5, clbt_5_shade, 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.