#!/usr/bin/env python
# coding: utf-8
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# Note: If you are running the notebook on MyBinder or already have the libraries installed, go directly to importing the libraries.
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# Note: If you don't have these libraries installed, click on three dots below, uncomment the code and run the next cell.
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# 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)
# In[4]:
parameters = ['vo']
filename = 'medium-vorticity.grib'
filename
# 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="pl",
levelist=[1000,925,850,700,500,300,250,200,50],
param=parameters,
target=filename
)
# ## Reading and processing the data
# Now we can use **Metview's read() function** 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()
# And an overview of one parameter
# In[8]:
data.describe('vo')
# We can use **ls()** function to list all the fields in the file we downloaded.
# In[9]:
data.ls()
# The grib file contains all the levels, and we will use the **select()** method to filter one of them.
# In[10]:
vo700 = data.select(level = 700)
vo700.describe()
# In order to match the units in the Open charts plot, we need to multiply the data with 100000.
#
# In[11]:
vo700 *= 100000
# ## 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
vo_shade = mv.mcont(legend= "on",
contour_automatics_settings = "style_name",
contour_style_name = "sh_blured_fM50t50lst_cell")
title = mv.mtext(
text_lines=["Vorticity at various pressure levels, level hPa ",
"START TIME: ",
" VALID TIME: , 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, vo700, vo_shade, title, ecmwf_text)
# To generate the png file you can run the following cell.
# In[13]:
png = mv.png_output(
output_name = "medium-vorticity", # specify relative or full path
output_title = "medium-vorticity", # title used by a viewer
output_width = 1000, # set width in pixels
)
mv.setoutput(png)
mv.plot(view, vo700, vo_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.