#!/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.
#
# 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-rain-rate.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](https://confluence.ecmwf.int/display/UDOC/MARS+user+documentation):
# In[ ]:
server = ECMWFService("mars")
server.execute(
{
"class": "od",
"date": "-1",
"expver": "1",
"levtype": "sfc",
"param": "260048/228219/228218/228220/228221",
"step": "12",
"stream": "oper",
"time": "00",
"type": "fc",
"grid": "0.25/0.25"
},
filename)
# Alternatevly we have prepared small sample dataset to explore
# In[3]:
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[4]:
data = mv.read(filename)
# The **describe()** function will give us the overview of the dataset.
# In[5]:
data.describe()
# And an overview of one parameter, where we can see more information, such as units or type of level.
# In[6]:
data.describe('tprate')
# We can use **ls()** function to list all the fields in the file we downloaded.
# In[7]:
data.ls()
# The grib file contains all the parameters, and we will use the select() function to filter what we need.
# In[8]:
tprate = data.select(shortName= 'tprate')
lsrr = data.select(shortName= 'lsrr')
crr = data.select(shortName= 'crr')
csfr = data.select(shortName= 'csfr')
lssfr = data.select(shortName= 'lssfr')
# The data has units kg m-2 s-1, but we need to plot it in mm/hr.
# To convert to mm we need to multiply by 3600.
# In[9]:
tprate = tprate * 3600
lsrr = lsrr * 3600
crr = crr * 3600
# We can calculate total snowfall rate by adding the Large scale snowfall rate and Convective snowfall rate. In the end we also need to convert it too mm/hr.
# In[10]:
sfr = (csfr + lssfr) * 3600
# ## Plotting the data
# And finally, we can plot the data on the map.
# First we plot Total precipitation rate.
# In[11]:
# 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
tprate_shade = mv.mcont(legend= "on",
contour_automatics_settings = "style_name",
contour_style_name = "sh_prate_radarlike_grided")
title = mv.mtext(
text_lines=["Precipitation rate - Total",
"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, tprate, tprate_shade, title, ecmwf_text)
# Next we plot Large scale precipitation rate.
# 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
lsrr_shade = mv.mcont(legend= "on",
contour_automatics_settings = "style_name",
contour_style_name = "sh_prate_radarlike_grided")
title = mv.mtext(
text_lines=["Precipitation rate - Large scale",
"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, lsrr, lsrr_shade, title, ecmwf_text)
# Next we plot Convective precipitation rate.
# In[13]:
# 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
crr_shade = mv.mcont(legend= "on",
contour_automatics_settings = "style_name",
contour_style_name = "sh_prate_radarlike_grided")
title = mv.mtext(
text_lines=["Precipitation rate - Convective",
"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, crr, crr_shade, title, ecmwf_text)
# And last we plot snowfall rate.
# 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
sfr_shade = mv.mcont(legend= "on",
contour_automatics_settings = "style_name",
contour_style_name = "sh_prate_radarlike_grided")
title = mv.mtext(
text_lines=["Precipitation rate - Snowfall",
"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, sfr, sfr_shade, title, ecmwf_text)
# To generate the png files you can run the following four cells.
# In[15]:
png = mv.png_output(
output_name = "medium-rain-rate-tprate", # specify relative or full path
output_title = "medium-rain-rate-tprate", # title used by a viewer
output_width = 1000, # set width in pixels
)
mv.setoutput(png)
mv.plot(view, tprate, tprate_shade, title, ecmwf_text)
# In[16]:
png = mv.png_output(
output_name = "medium-rain-rate-lsrr", # specify relative or full path
output_title = "medium-rain-rate-lsrr", # title used by a viewer
output_width = 1000, # set width in pixels
)
mv.setoutput(png)
mv.plot(view, lsrr, lsrr_shade, title, ecmwf_text)
# In[17]:
png = mv.png_output(
output_name = "medium-rain-rate-crr", # specify relative or full path
output_title = "medium-rain-rate-crr", # title used by a viewer
output_width = 1000, # set width in pixels
)
mv.setoutput(png)
mv.plot(view, crr, crr_shade, title, ecmwf_text)
# In[18]:
png = mv.png_output(
output_name = "medium-rain-rate-sfr", # specify relative or full path
output_title = "medium-rain-rate-sfr", # title used by a viewer
output_width = 1000, # set width in pixels
)
mv.setoutput(png)
mv.plot(view, sfr, sfr_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 charts show the data on original grid of the model.