Dust Aerosol Detection, Monitoring and Forecasting


Fig. 1 Impressions of dust storms (Source WMO)

What is the course about?

This course is a Python-based training that provides you a hands-on introduction to satellite-, ground- and model-based data used for dust monitoring and forecasting. The course is divided in three parts: Observations, Forecast Models and a Practical case study. The first two chapters provide you an overview of different data types and an example how to access, load and visualize the data. Both chapters serve as basis for the third chapter, which consists of guided exercises where you perform an analysis of a real-world dust event more in detail.

After completing the course, you will be able to:

  • find satellite-, model-base and ground-based data products for dust monitoring and forecasting,

  • use standard Python packages to handle, process and visualise atmospheric composition data, and

  • identify advantages and limitations of each data product.

Executable notebooks are available on a dedicated Jupyterhub-based course platform:

Why this course?

Dust storms are common meteorological hazards in arid and semi-arid regions. They are usually caused by thunderstorms, or strong pressure gradients associated with cyclones, that increase wind speed over a wide area. Monitoring, forecasting and early warning systems for airborne dust are crucial to evaluate impacts and developing products to guide preparedness, adaptation and mitigation policies.

Interaction of airborne dust with weather and climate:

  • Airborne dust functions in a manner similar to the greenhouse effect: it absorbs and scatters solar radiation entering Earth’s atmosphere, reducing the amount reaching the surface, and absorbs long-wave radiation bouncing back up from the surface, re-emitting it in all directions.

  • Dust particles, especially if coated by pollution, act as condensation nuclei for warm cloud formation and as efficient ice nuclei agents for cold cloud generation. Modification of the microphysical composition of clouds changes their ability to absorb solar radiation, which indirectly affects the energy reaching the Earth’s surface. Dust particles also influence the growth of cloud droplets and ice crystals, thus affecting the amount and location of precipitation.

Airborne dust has impacts on human health:

  • Airborne dust, depending on its size and level of inhalation penetration, can damage external organs, get trapped in the nose, mouth and upper respiratory tract, thus can be associated with respiratory disorders such as asthma, tracheitis, pneumonia, allergic rhinitis and silicosis. However, finer particles may penetrate the lower respiratory tract and enter the bloodstream, where they can affect all internal organs and be responsible for cardiovascular disorders.

  • Some infectious diseases can be transmitted by dust. For instance meningococcal meningitis, a bacterial infection of the thin tissue layer that surrounds the brain and spinal cord, can result in brain damage and, if untreated, death in 50% of cases.

Airborne dust has impacts on the environment:

  • Surface dust deposits are a source of micro-nutrients for both continental and maritime ecosystems. Saharan dust is thought to fertilize the Amazon rainforest) but dust also has many negative impacts on agriculture, including reducing crop yields by burying seedlings, loss of plant tissue, reducing photosynthetic activity and increasing soil erosion.

  • Indirect dust deposit impacts include filling irrigation canals, covering transportation routes and affecting river and stream water quality. Reductions in visibility due to airborne dust also have an impact on air and land transport. Poor visibility conditions are a danger during aircraft landing and taking off – landings may be diverted and departures delayed. Dust can also scour aircraft surfaces and damage engines.

  • Dust can impact on the output of solar power plants, especially those that rely on direct solar radiation. Dust deposits on solar panels are a main concern of plant operators. Keeping the solar collectors dust-free to prevent particles from blocking incoming radiation requires time and labour.

The efficiency of physical processes and their impact depends on the amount, exposure, type of dust size, shape and composition, which in turn depend on the nature of parent soils, emissions and transport processes.

What data is addressed?

The course features satellite and ground-based observations as well as model forecasts:


Remote Sensing - Satellites
The course provides an overview of six different data sets, from five satellite instruments and one multi-sensor product.

Level 1

Level 2

Level 3


True color RGB composite

Aqua/Terra MODIS

True color RGB composite

Aerosol Index

Sentinel-5P TROPOMI

Ultraviolet Aerosol Index

Metop-A/B/C GOME-2 and IASI

Absorbing Aerosol Index


Aerosol Optical Depth

Ground-based (remote sensing and in-situ)
Three different ground-based observations are introduced, covering three different measurment techniques.

Measurement at the surface

Columnar remote sensing

Lidar remote sensing

European Environment Agency (EEA) Air Quality Data

AErosol RObotic NETwork (AERONET)

EARLINET Lidar backscatter profiles

Forecast models

A total of three forecast model products are introduced, featuring one global dust forecast and two regional dust forecasts.

Global dust forecasts

Regional dust forecasts

CAMS global atmospheric composition forecasts

CAMS European air quality forecasts

WMO Barcelona Dust Regional Center

Who is the course for and what are the prerequisites?

The course is designed for researchers, scientists and Earth Observation practitioners that are interested in data related to dust monitoring and forecasting. There is no pre-requisite necessary to follow the course, however it will be easier if you have basic programming knowledge, preferably in Python.


If you have questions to this module or you have feedback, feel free to contact the EUMETSAT training team.