Plot example added.

README.md

@@ -27,5 +27,8 @@ Modify the time, parameters, levels etc. to match your specific needs.
 The folder ["mars_retrieve_total"](mars_retrieve_total/) contains python scripts for retrieving all datasets from the Copernicus Climate Change Service regional reanalysis for Europe reanalysis via the ECMWF MARS system.<br />
 The scripts are provided as a complete base showing all available data. Please note that this means a lot of data. Modify the time, parameters, levels etc. to match your specific needs.
 
+## CDS retrieval and plotting example
+The folder ["retrieve_and_plot_example"](retrieve_and_plot_example/) contains 2 scripts. One to retrieve some data from Copernicus Climate Change Service regional reanalysis for Europe via the CDS API. The other script plots the data on a map and optionally creates an animation of the resulting images.
+
 ## Create forcing data for NEMO-Nordic
 The folder ["create_forcing_for_NEMO"](create_forcing_for_NEMO/) contains a script system, which can be used to prepare forcing data for NEMO-Nordic (a regional ocean model). The scripts retrieve hourly data from MARS and mix analyses and forecasts. Then, the data is post-processed for the usage with NEMO-Nordic. For instance, relative humidity is transferred to specific humidity. At the end, the data is stored in netcdf-format as needed by NEMO.

retrieve_and_plot_example/README.md

+# Retrieve and plot UERRA data
+These scripts are an example of how to retrieve UERRA data from CDS API and plot GRIB or netCDF data with Python.
+
+## The objectives of the scripts
+These scripts will:
+
+1. Retrieve data with CDS API.
+2. Plot GRIB or netCDF data.
+3. Make an animation.
+
+## Instructions
+
+1. Set the dates your are interested in.
+2. Define directory for the downloaded file.
+3. Set GRIB or netCDF and whether to animate or not.
+4. Run retrive_UERRA.py to retrieve data.
+5. Run plot_UERRA.py to plot the data.
+
+## Good to know
+The scripts need some time if you are downloading a lot of data.
+
+## Technical details
+The scripts were developed and tested with Python 2.7.9 in Linux.
+
+Used modules:  
+pygrib version : 2.0.0  
+netCDF4 version : 1.1.3  
+matplotlib version : 1.4.2  
+pylab  
+datetime  
+cdsapi  
+
+The plot script use the [CDO program](http://mpimet.mpg.de/cdo) and it's python bindings to transform from GRIB to netCDF.  
+If you don't need to transform you don't need CDO.  
+  
+Below is info from the cdo -V command:  
+Climate Data Operators version 1.7.0 (http://mpimet.mpg.de/cdo)  
+Features: DATA PTHREADS OpenMP4 HDF5 NC4/HDF5 OPeNDAP Z PROJ.4 SSE2  
+Libraries: HDF5/1.8.14 proj/4.7  
+Filetypes: srv ext ieg grb grb2 nc nc2 nc4 nc4c  
+CDI library version : 1.7.0  
+CGRIBEX library version : 1.7.3  
+GRIB_API library version : 1.14.0  
+netCDF library version : 4.3.2  
+HDF5 library version : 1.8.14  
+
+Moreover, to fetch data from CDS, you will need a account at [Copernicus Climate Data Store](https://cds.climate.copernicus.eu/).
+
+## General information
+This project contains example code for users of the Copernicus Climate Change Service regional reanalysis for Europe (C3S_322_Lot1) data.
+More information about the service can be found at the [Copernicus Climate Change Service regional reanalysis for Europe website](https://climate.copernicus.eu/copernicus-regional-reanalysis-europe-cerra).
+
+The reanalysis data were initially produced within the FP7 pre-operational UERRA project.
+More information about the reanalyses systems and data can be found at the [UERRA website](http://www.uerra.eu/).
+These scripts belong to a lesson at [Copernicus user lerning service (ULS)](https://uls.climate.copernicus.eu/).

retrieve_and_plot_example/plot_UERRA.py

+#!/usr/bin/env python
+
+# GRIB
+import pygrib
+
+# Date
+from datetime import datetime
+
+# Plot
+import matplotlib
+import pylab as plt
+import matplotlib.colors as clr
+from mpl_toolkits.basemap import Basemap
+
+# CDO
+from cdo import *
+cdo   = Cdo()
+
+# NetCDF
+import netCDF4
+from netCDF4 import Dataset
+
+# path to data
+data_path = ''
+# file name
+data_file = 'download.grib'
+
+# select dates to plot
+year = [2005]
+month = [1]
+day = [8,9]
+hour = [0,6,12,18]
+
+# plot GRIB or netCDF and animate or not
+grib = True
+netcdf = False
+animate = False
+
+def select_date(data):
+    """
+    a function to compare dates and return an array of locations to select datest
+    """
+    sel_data = []
+    i = 0
+    for d in data:
+        if d.year in year and d.month in month and d.day in day and d.hour in hour:
+            sel_data.append(i)
+        i = i+1
+ 
+    return sel_data
+
+def plotting(lon, lat, wind, MSLP, title=None, savename=None, levels=None, cmap=None):
+    """
+    A function to plot wind and pressure data
+    """
+    plt.figure(figsize=(10,10))
+
+    m = Basemap(llcrnrlon=-15, llcrnrlat=40, urcrnrlon=25, urcrnrlat=75, resolution='l')
+    x, y = m(lon, lat) # koordinater grid
+
+    draw_extras(m) # draw coastlines, countries, rivers
+
+    # Wind
+    CS_wind = m.contourf(x, y, wind, extend='max', levels=levels, cmap=cmap)
+    cbar = m.colorbar(CS_wind) 
+    cbar.ax.set_title('[m/s]')
+
+    # MSLP
+    CS_mslp = m.contour(x, y, MSLP/100, levels=range(950,1050,5), colors='k', linewidths=1)
+    plt.clabel(CS_mslp, inline=1, forntsize=10, fmt='%i', zoder=0)
+
+    if title:
+        plt.title(title)  
+
+    if savename:
+        plt.savefig(savename)   
+    
+    plt.show()
+
+def draw_extras(m):
+    """
+    A function to draw coastlines, countries, rivers
+    """
+    m.drawcountries(linewidth=0.4, color='k', zorder=4)
+    m.drawcoastlines(linewidth=0.4, color='k', zorder=4)
+
+def to_netcdf(data):
+    """
+    A function to transform data to netCDF-file
+    Return data as netCDF
+    """
+    nc_data = cdo.setgridtype('curvilinear', input=data, options='-f nc')
+
+    return nc_data
+
+def read_netcdf(data, variable):
+    """
+    A function to read data from netCDF-file
+    Return time, longitude, latitude and value of 'variable'
+    """
+    nc_fid = Dataset(data, 'r')
+
+    time_var = nc_fid.variables['time']
+    dtime = netCDF4.num2date(time_var[:],time_var.units)
+    lats = nc_fid.variables['lat'][:]
+    lons = nc_fid.variables['lon'][:]
+    data = nc_fid.variables[variable][:].squeeze()
+
+    return dtime, lons, lats, data
+
+if __name__ == '__main__':
+    
+    if grib:
+        grb_data = pygrib.open(data_path+data_file) # read GRIB
+
+        wind = grb_data.select(name='10 metre wind speed') # select wind data
+        MSLP = grb_data.select(name='Mean sea level pressure') # select pressure data
+
+        sel_data = select_date(wind) # select time
+
+        for i in sel_data:
+            lats, lons = wind[i].latlons() # read longitude and lattitude from GRIB
+            
+            values_wind = wind[i].values        
+            values_MSLP = MSLP[i].values
+            
+            time = datetime(wind[i].year, wind[i].month, wind[i].day, wind[i].hour)          
+            title = 'Storm ' + time.strftime('%Y-%b-%d %H:%M')
+            savename = data_path + 'Storm_' + time.strftime('%Y%m%d_%H%M') + '.png'
+            
+            levels = [0,0.3,1.6,3.4,5.5,8,10.8,13.9,17.2,20.8,24.5,28.5,32.7]
+            colors = ['#5EBC32','#ffff00','#AF111A','#764285']
+            cmap = clr.LinearSegmentedColormap.from_list('wind speed', colors, N=256)
+            plotting(lons, lats, values_wind, values_MSLP, title=title, savename=savename, levels=levels, cmap=cmap) #plotting GRIB-data
+
+    if netcdf:
+        nc_data = to_netcdf(data_path+data_file) # transform GRIB to netCDF
+  
+        [time, lons, lats, wind] = read_netcdf(nc_data, '10si') # read wind data from netCDF
+        [time, lons, lats, MSLP] = read_netcdf(nc_data, 'msl') # read pressure data from netCDF
+        
+        sel_data = select_date(time) # select time
+
+        for i in sel_data: 
+            values_wind = wind[i,:,:]
+            values_MSLP = MSLP[i,:,:]
+           
+            title = 'Storm ' + str(time[i].strftime('%Y-%b-%d %H:%M'))
+            savename = data_path + 'Storm_' + str(time[i].strftime('%Y%m%d_%H%M')) + '.png'
+
+            levels = [0,0.3,1.6,3.4,5.5,8,10.8,13.9,17.2,20.8,24.5,28.5,32.7]
+            colors = ['#5EBC32','#ffff00','#AF111A','#764285']
+            cmap = clr.LinearSegmentedColormap.from_list('wind speed', colors, N=256)
+            plotting(lons, lats, values_wind, values_MSLP, title=title, savename=savename, levels=levels, cmap=cmap) #plotting NetCDF-data
+
+    if animate:
+        figurename = data_path + 'Storm_*.png'
+        animationname = data_path + 'stormanimation.gif'
+        subprocess.call('convert -delay 100 ' + figurename + ' ' + animationname, shell=True)

retrieve_and_plot_example/retrieve_UERRA.py

+#!/usr/bin/env python
+import cdsapi
+
+# Change the variables for year, month and day to a time as you wish
+year = ['2005']
+month = ['01']
+day = ['6','7','8','9','10']
+
+c = cdsapi.Client()
+
+def uerra_request(reqYear, reqMonth, reqDates):
+    """
+        A UERRA request for the variables in the array 'variables'.
+        Origin uerra_harmonie, single levels, analysis fields.
+        Request cost per day is 8 fields, ca 5 Mbytes.
+    """
+
+    c.retrieve(
+        'reanalysis-uerra-europe-single-levels',
+        {
+            'origin': 'uerra_harmonie',
+            'variable': ['10m_wind_speed','mean_sea_level_pressure'],
+            'year': reqYear,
+            'month': reqMonth,
+            'day': reqDates,
+            'time': ['00:00','06:00','12:00','18:00'],
+            'format': 'grib',
+        },
+        'download.grib')
+
+if __name__ == '__main__':
+    uerra_request(year, month, day)
\ No newline at end of file