Dust Aerosol Detection, Monitoring and Forecasting#
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.
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.
After completing the course, you will be able to:
find, access and download satellite-, model-base and ground-based data products for dust monitoring and forecasting,
use relevant Python packages to load, process and visualise these data, and
assess advantages and limitations of each data product.
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:
Observations#
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 |
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|---|---|---|---|
MSG SEVIRI |
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Aqua/Terra MODIS |
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Sentinel-5P TROPOMI |
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Metop-A/B/C GOME-2 and IASI |
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Multi-Sensor |
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 |
|---|---|---|
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 |
|---|---|
How to use this content?#
This online module is the entry point for you to engage with the content in a browsable, static manner.
There are two options to engage with the content interactively:
1. Access the Jupyterhub-based training platform
2. Clone this repository and setup the required environment on your local machine
The first option is for those who are not familiar with Python and conda environments. The second option could be of interest for those who are more experienced in setting up Python environments.
1. Access the Jupyterhub-based training platform#
All notebooks from the online module are available as executable notebooks on a dedicated Jupyterhub-based training platform. This platform already has the required Python environment and the required datasets available so that you can go through the notebooks and execute the code. The platform requires a registration, which is free. After registration, you receive an email that asks you to confirm your registration. After you confirmed it, you can access the platform with your username and password.
Step 1: Register
Step 2: Access the notebooks on the Jupyterhub-based training platform
2. Clone this repository and setup the environment on your local machine#
Another option is to reproduce the environment on your own machine by following these steps:
Step 1: clone this repository via
git clone git@gitlab.eumetsat.int:eumetlab/atmosphere/dust-monitoring.git(Note: Git needs to be installed. See here for more information how to install Git.)Step 2: Set up a virtual environment for Python (version 3.11) using Anaconda - follow the steps outlined on this website
Step 3: Install relevant Python packages with
conda env create -f environment.ymlStep 4: Access and download required data
You can access and download all data required for the training course as archived tar files from the Jupyter-hub training platform under the folder
./eodata. Alternatively, each notebook gives you instructions on how to download the relevant data files and you can download them manually, too.
Step 5: Launch Jupyterlab inside your virtual conda environment via
jupyter lab(Note: you might need to adjust the data paths with the actual path to the data on your system)
Who has developed the course?#
This course has been developed by Dr. Julia Wagemann, MEEO (Lead developer) and Sabrina Szeto, MEEO (Contributor) in collaboration with the following entities:
the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) as part of Copernicus
the WMO SDS-WAS Regional Center for Northern Africa, Middle East and Europe
the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS)
Contact & Support#
Please get in touch with the EUMETSAT Training team via training@eumetsat.int for:
Questions: please reach out for any questions to this module.
Reporting issues or problems: did you notice a bug or you ran into a problem, please share it with us so we can improve the module.
Feedback: please share any feedback you have, e.g. how you like the module, your learning experience or you have an idea how to improve it? We’d like to hear from you.
License#
This code is licensed under GPL-3.0-only.
