SDS-WAS regional dust forecasts

This notebook provides an introduction to dust forecast data from the NMMB/MONARCH model. The notebook introduces you to the variable Dust Optical Depth and you will learn how the model has predicted the Saharan Dust event which occured over Europe in the first half of February 2021.

The WMO Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) is an international framework linking institutions involved in Sand and Dust Storm (SDS) research, operations and delivery of services.

The framework is organised in several regional centers, which aim to implement SDS-WAS objectives in a specific region. The Barcelona Supercomputing Center (BSC-CNS) and the Meteorological State Agency of Spain (AEMET) are hosting the SDS-WAS regional center for Northern Africa, Middle East and Europe.

One of the main activities of the SDS-WAS regional center is to provide daily operational dust forecasts for Northern Africa (north of the equator), Middle East and Europe. The BCS-CNS, in collaboration with NOAA’s National Centers for Environmental Prediction (NCEP), the NASA’s Goddard Institute for Space Studies and the International Research Institute for Climate Society (IRI), has developed NMMB/MONARCH, an online multi-scale atmospheric dust model intended to provide short and medium-range dust forecasts for both, regional and global domains.

The model provides forecast information up to 72 hours in advance (every 3 hours) of two parameters: Dust Optical Depth and Dust Surface Concentration.

NMMB/MONARCH Forecast data are available in netCDF format and are available for download here.

Basic facts

Spatial resolution: 0.1° x 0.1°
Spatial coverage: Northern Africa, Middle East and Europe
Temporal resolution: 3-hourly up to 72 hours in advance
Temporal coverage: since February 2012
Data format: NetCDF

How to access the data

Dust forecast data from the NMMB/MONARCH model are available for download via the website of the SDS-WAS Regional Center for Northern Africa, Middle East and Europe.

Every participant of the training school and workshop on dust monitoring can use the following credentials for downloading data:

  • User:

  • Password: BarcelonaDustRC

Load required libraries

import xarray as xr

import matplotlib.pyplot as plt
import matplotlib.colors
from import get_cmap
from matplotlib import animation
from matplotlib.axes import Axes

import as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import cartopy.feature as cfeature
from cartopy.mpl.geoaxes import GeoAxes
GeoAxes._pcolormesh_patched = Axes.pcolormesh

from IPython.display import HTML

import warnings
warnings.simplefilter(action = "ignore", category = RuntimeWarning)

Load helper functions

%run ../functions.ipynb

Load and browse NMMB/MONARCH dust forecast data

The first step is to load a NMMB/MONARCH forecast file to better understand its structure. The data is disseminated in the netCDF format on a daily basis, with the forecast initialisation at 12:00 UTC. Let us load the NMMB/MONARCH dust forecast of 5 February 2021. You can use the function open_dataset() from the xarray Python library, which makes reading a netCDF file very efficient and easy. The function loads the netCDF file as xarray.Dataset(), which is a data collection of multiple variables sharing the same coordinate information.

Below, you see that the NMMB/MONARCH data has three dimensions, lat, lon and time, and offers two data variables od550_dust and sconc_dust.

filepath = '../eodata/3_model/sds_was/'
file = xr.open_dataset(filepath)
Dimensions:     (lat: 825, lon: 1650, time: 25)
  * lat         (lat) float32 -11.0 -10.9 -10.8 -10.7 ... 71.1 71.2 71.3 71.4
  * lon         (lon) float32 -63.0 -62.9 -62.8 -62.7 ... 101.7 101.8 101.9
  * time        (time) datetime64[ns] 2021-02-05T12:00:00 ... 2021-02-08T12:0...
Data variables:
    od550_dust  (time, lat, lon) float32 ...
    sconc_dust  (time, lat, lon) float32 ...
    CDI:                        Climate Data Interface version 1.9.3 (http://...
    history:                    Sat Feb  6 00:15:10 2021: ncks -v sconc_dust,...
    Conventions:                CF-1.6
    comment:                    Generated on marenostrum4
    NCO:                        netCDF Operators version 4.9.5 (Homepage = ht...
    nco_openmp_thread_number:   1
    history_of_appended_files:  Fri Feb  5 23:32:43 2021: Appended file od550...
    cdo_openmp_thread_number:   4
    CDO:                        Climate Data Operators version 1.9.3 (http://...

Let us now have a closer look at the dimensions of the data. Let us first inspect the two coordinate dimensions lat and lon. You can simply access the dimension’s xarray.DataArray by specifying the name of the dimension. Below you see that the data has a 0.1 x 0.1 degrees resolution and have the following geographical bounds:

  • Longitude: [-63., 101.9]

  • Latitude: [-11., 71.4]

latitude =
longitude = file.lon
latitude, longitude
(<xarray.DataArray 'lat' (lat: 825)>
 array([-11. , -10.9, -10.8, ...,  71.2,  71.3,  71.4], dtype=float32)
   * lat      (lat) float32 -11.0 -10.9 -10.8 -10.7 -10.6 ... 71.1 71.2 71.3 71.4
     standard_name:  latitude
     long_name:      latitude
     units:          degrees_north
     axis:           Y,
 <xarray.DataArray 'lon' (lon: 1650)>
 array([-63. , -62.9, -62.8, ..., 101.7, 101.8, 101.9], dtype=float32)
   * lon      (lon) float32 -63.0 -62.9 -62.8 -62.7 ... 101.6 101.7 101.8 101.9
     standard_name:  longitude
     long_name:      longitude
     units:          degrees_east
     axis:           X)

Now, let us also inspect the time dimension. You see that one daily forecast file has 25 time steps, with three hourly forecast information up to 72 hours (3 days) in advance.

<xarray.DataArray 'time' (time: 25)>
array(['2021-02-05T12:00:00.000000000', '2021-02-05T15:00:00.000000000',
       '2021-02-05T18:00:00.000000000', '2021-02-05T21:00:00.000000000',
       '2021-02-06T00:00:00.000000000', '2021-02-06T03:00:00.000000000',
       '2021-02-06T06:00:00.000000000', '2021-02-06T09:00:00.000000000',
       '2021-02-06T12:00:00.000000000', '2021-02-06T15:00:00.000000000',
       '2021-02-06T18:00:00.000000000', '2021-02-06T21:00:00.000000000',
       '2021-02-07T00:00:00.000000000', '2021-02-07T03:00:00.000000000',
       '2021-02-07T06:00:00.000000000', '2021-02-07T09:00:00.000000000',
       '2021-02-07T12:00:00.000000000', '2021-02-07T15:00:00.000000000',
       '2021-02-07T18:00:00.000000000', '2021-02-07T21:00:00.000000000',
       '2021-02-08T00:00:00.000000000', '2021-02-08T03:00:00.000000000',
       '2021-02-08T06:00:00.000000000', '2021-02-08T09:00:00.000000000',
       '2021-02-08T12:00:00.000000000'], dtype='datetime64[ns]')
  * time     (time) datetime64[ns] 2021-02-05T12:00:00 ... 2021-02-08T12:00:00
    standard_name:  time
    long_name:      time
    axis:           T

Load data variables as data arrays

A xarray.Dataset is a collection of multiple variables and offers a general overview of the data, but does not offer direct access to the data arrays. For this reason, you want to load a data variable as xarray.DataArray. You can access the data array information by simply specifying the name of the variable after the name of the xarray.Dataset

Let us load both variables as xarray.DataArray, od550_dust and sconc_dust. Below you see that, once the data array is loaded, that each variable is disseminated with additional attributes, e.g. long_name and units. These attributes give us more information about the data. od550_dust e.g. is Aerosol Optical Depth which does not have a unit and sconc_dust is the surface dust concentration measured in kg / m3.

od_dust = file.od550_dust
<xarray.DataArray 'od550_dust' (time: 25, lat: 825, lon: 1650)>
[34031250 values with dtype=float32]
  * lat      (lat) float32 -11.0 -10.9 -10.8 -10.7 -10.6 ... 71.1 71.2 71.3 71.4
  * lon      (lon) float32 -63.0 -62.9 -62.8 -62.7 ... 101.6 101.7 101.8 101.9
  * time     (time) datetime64[ns] 2021-02-05T12:00:00 ... 2021-02-08T12:00:00
    long_name:  Aerosol optical depth
    units:      -
sconc_dust = file.sconc_dust
<xarray.DataArray 'sconc_dust' (time: 25, lat: 825, lon: 1650)>
[34031250 values with dtype=float32]
  * lat      (lat) float32 -11.0 -10.9 -10.8 -10.7 -10.6 ... 71.1 71.2 71.3 71.4
  * lon      (lon) float32 -63.0 -62.9 -62.8 -62.7 ... 101.6 101.7 101.8 101.9
  * time     (time) datetime64[ns] 2021-02-05T12:00:00 ... 2021-02-08T12:00:00
    long_name:  TRACERS_015
    units:      kgm-3

You can define variables for the attributes of a variable. This can be helpful during data visualization, as the attributes long_name and units can be added as additional information to the plot. From the xarray.DataArray, you simply specify the name of the attribute of interest.

units= od_dust.units

Visualize dust AOD at 550 nm

Now we have loaded all necessary information to be able to visualize the dust Aerosol Optical Depth for one specific time during the forecast run. Let us use the function visualize_pcolormesh() to visualize the data with the help of the plotting library matplotlib and Cartopy.

You have to specify the following keyword arguments:

  • data_array: the

  • longitude, latitude: longitude and latitude variables of the data variable

  • projection: one of Cartopy’s projection options

  • color_scale: one of matplotlib’s colormaps

  • units: unit of the data parameter, preferably taken from the data array’s attributes

  • long_name: longname of the data parameter is taken as title of the plot, preferably taken from the data array’s attributes

  • vmin, vmax: minimum and maximum bounds of the color range

  • set_global: False, if data is not global

  • lonmin, lonmax, latmin, latmax: kwargs have to be specified, if set_global=False

Note: in order to have the time information as part of the title, we add the string of the datetime information to the long_name variable: long_name + ' ' + str(od_dust[##,:,:]

forecast_step = 6
                     long_name=long_name + ' ' + str(od_dust[forecast_step,:,:],
(<Figure size 1440x720 with 2 Axes>,
 <GeoAxesSubplot:title={'center':'Aerosol optical depth 2021-02-06T06:00:00.000000000'}>)

Animate dust AOD forecasts

In the last step, you can animate the Dust Aerosol Optical Depth forecasts in order to see how the trace gas develops over a period of 3 days, from 5th Feb 12 UTC to 8th February 2021.

You can do animations with matplotlib’s function animation. Jupyter’s function HTML can then be used to display HTML and video content.

The animation function consists of 4 parts:

  • Setting the initial state:
    Here, you define the general plot your animation shall use to initialise the animation. You can also define the number of frames (time steps) your animation shall have.

  • Functions to animate:
    An animation consists of three functions: draw(), init() and animate(). draw() is the function where individual frames are passed on and the figure is returned as image. In this example, the function redraws the plot for each time step. init() returns the figure you defined for the initial state. animate() returns the draw() function and animates the function over the given number of frames (time steps).

  • Create a animate.FuncAnimation object:
    The functions defined before are now combined to build an animate.FuncAnimation object.

  • Play the animation as video:
    As a final step, you can integrate the animation into the notebook with the HTML class. You take the generate animation object and convert it to a HTML5 video with the to_html5_video function

# Setting the initial state:
# 1. Define figure for initial plot
fig, ax = visualize_pcolormesh(data_array=od_dust[0,:,:],
                     long_name=long_name + ' ' + str(od_dust[0,:,:],

frames = 24

def draw(i):
    img = plt.pcolormesh(od_dust.lon, 
    ax.set_title(long_name + ' '+ str(od_dust.time[i].data), fontsize=20, pad=20.0)
    return img

def init():
    return fig

def animate(i):
    return draw(i)

ani = animation.FuncAnimation(fig, animate, frames, interval=800, blit=False,
                              init_func=init, repeat=True)