Commit a05b1af2 authored by Julia Wagemann's avatar Julia Wagemann
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ESA training course modules added

parent f9a94f60
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<img src='../img/EU-Copernicus-EUM_3Logos.png' alt='Logo EU Copernicus EUMETSAT' align='right' width='50%'></img>
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#### 11th ESA Training Course on Earth Observation 2021 | 22-26 March 2021
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# Practical 1 - Discover satellite data for Dust and NO<sub>2</sub> Monitoring
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This practical session gives you an overview of different satellite data for dust and NO<sub>2</sub> monitoring and provides you hands-on practical example how to retrieve, load, process and visualize the data.
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## Notebook overview and featured data
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#### The following satellite datasets for dust monitoring are featured:
* [AC SAF GOME-2 Level 3 Absorbing Aerosol Index](https://acsaf.org/index.html)
* [Polar Multi-Sensor Aerosol Optical Properties](https://navigator.eumetsat.int/product/EO:EUM:DAT:METOP:PMAP)
* [CAMS Global Near-Real-Time Forecast](https://apps.ecmwf.int/datasets/data/cams-nrealtime/levtype=sfc/)
* [CAMS European Air Quality Forecasts and Analyses](https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-forecasts?tab=overview)
#### The following satellite datasets for NO<sub>2</sub> monitoring are featured:
* [Metop-AB GOME-2 Tropospheric Nitrogen Dioxide data records (L3)](https://acsaf.org/datarecords/no2_vcd.php)
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#### The following notebooks are available:
* [01 - Overview - Atmospheric Composition Data Retrieve](./01_overview_atmospheric_composition_data_retrieve.ipynb)
* [02 - Metop-ABC GOME-2 Absorbing Aerosol Index Level 3 - Load and browse](./02_Metop-ABC_GOME-2_AAI_L3_load_browse.ipynb)
* [03 - PMAp Aerosol Optical Depth Level 2 - Load and browse](./03_PMAp_AOD_L2_load_browse.ipynb)
* [04 - CAMS global near-real-time forecast - Dust Aerosol Optical Depth - Load and browse](./04_CAMS_global_forecast_duaod_load_browse.ipynb)
* [05 - CAMS European Air Quality Forecasts and Analyses - Dust concentration - Load and browse](./05_CAMS_European_air_quality_forecast_dust_concentration_load_browse.ipynb)
* [11 - Metop-AB GOME-2 Tropospheric Nitrogen Dioxide Level 3 - Load and browse](./11_Metop-AB_GOME-2_NO2Tropo_L3_load_browse.ipynb)
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**NOTE:** Throughout the course, general functions to `load`, `re-shape`, `process` and `visualize` the datasets are defined. These functions are re-used when applicable. The [functions notebook](./functions.ipynb) gives you an overview of all the functions defined and used for the course.
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<hr>
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## Learning outcomes
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The course is designed for `beginners and medium-level users`, who have basic Python knowledge.
After the course, you should have:
* an idea about **satellite and assimilated data products for dust monitoring**,
* knowledge about the most useful **Python packages** to handle, process and visualise atmospheric composition data
* an idea about how the **data can help to detect and monitor dust events**
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<hr>
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## Access to the training platform
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The course material is made available on a JupyterLab training platform, a pre-defined environment that gives learners direct access to the data and Python packages required for following the course.
The `JupyterLab` can be accessed as follows:
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* Web address: [https://training.ltpy.adamplatform.eu](https://training.ltpy.adamplatform.eu)
* Create an account: [https://login.ltpy.adamplatform.eu](https://login.ltpy.adamplatform.eu)
* Log into the `JupyterLab` with your account created.
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<hr>
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<img src='../img/copernicus_logo.png' alt='Logo EU Copernicus' align='right' width='20%'><br><br><br>
<p style="text-align:right;">This project is licensed under the <a href="./LICENSE">MIT License</a> and is developed under a Copernicus contract.
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<img src='../img/EU-Copernicus-EUM_3Logos.png' alt='Logo EU Copernicus EUMETSAT' align='right' width='50%'></img>
<br>
%% Cell type:markdown id: tags:
<br>
%% Cell type:markdown id: tags:
<br>
%% Cell type:markdown id: tags:
#### 11th ESA Training Course on Earth Observation 2021 | 22-26 March 2021
%% Cell type:markdown id: tags:
# Practical 1 - Discover satellite data for Dust and NO<sub>2</sub> Monitoring
%% Cell type:markdown id: tags:
This practical session gives you an overview of different satellite data for dust and NO<sub>2</sub> monitoring and provides you hands-on practical example how to retrieve, load, process and visualize the data.
%% Cell type:markdown id: tags:
## Notebook overview and featured data
%% Cell type:markdown id: tags:
#### The following satellite datasets for dust monitoring are featured:
* [AC SAF GOME-2 Level 3 Absorbing Aerosol Index](https://acsaf.org/index.html)
* [Polar Multi-Sensor Aerosol Optical Properties](https://navigator.eumetsat.int/product/EO:EUM:DAT:METOP:PMAP)
* [CAMS Global Near-Real-Time Forecast](https://apps.ecmwf.int/datasets/data/cams-nrealtime/levtype=sfc/)
* [CAMS European Air Quality Forecasts and Analyses](https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-forecasts?tab=overview)
#### The following satellite datasets for NO<sub>2</sub> monitoring are featured:
* [Metop-AB GOME-2 Tropospheric Nitrogen Dioxide data records (L3)](https://acsaf.org/datarecords/no2_vcd.php)
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<br>
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#### The following notebooks are available:
* [01 - Overview - Atmospheric Composition Data Retrieve](./01_overview_atmospheric_composition_data_retrieve.ipynb)
* [02 - Metop-ABC GOME-2 Absorbing Aerosol Index Level 3 - Load and browse](./02_Metop-ABC_GOME-2_AAI_L3_load_browse.ipynb)
* [03 - PMAp Aerosol Optical Depth Level 2 - Load and browse](./03_PMAp_AOD_L2_load_browse.ipynb)
* [04 - CAMS global near-real-time forecast - Dust Aerosol Optical Depth - Load and browse](./04_CAMS_global_forecast_duaod_load_browse.ipynb)
* [05 - CAMS European Air Quality Forecasts and Analyses - Dust concentration - Load and browse](./05_CAMS_European_air_quality_forecast_dust_concentration_load_browse.ipynb)
* [11 - Metop-AB GOME-2 Tropospheric Nitrogen Dioxide Level 3 - Load and browse](./11_Metop-AB_GOME-2_NO2Tropo_L3_load_browse.ipynb)
%% Cell type:markdown id: tags:
<br>
%% Cell type:markdown id: tags:
**NOTE:** Throughout the course, general functions to `load`, `re-shape`, `process` and `visualize` the datasets are defined. These functions are re-used when applicable. The [functions notebook](./functions.ipynb) gives you an overview of all the functions defined and used for the course.
%% Cell type:markdown id: tags:
<hr>
%% Cell type:markdown id: tags:
## Learning outcomes
%% Cell type:markdown id: tags:
The course is designed for `beginners and medium-level users`, who have basic Python knowledge.
After the course, you should have:
* an idea about **satellite and assimilated data products for dust monitoring**,
* knowledge about the most useful **Python packages** to handle, process and visualise atmospheric composition data
* an idea about how the **data can help to detect and monitor dust events**
%% Cell type:markdown id: tags:
<hr>
%% Cell type:markdown id: tags:
## Access to the training platform
%% Cell type:markdown id: tags:
The course material is made available on a JupyterLab training platform, a pre-defined environment that gives learners direct access to the data and Python packages required for following the course.
The `JupyterLab` can be accessed as follows:
%% Cell type:markdown id: tags:
* Web address: [https://training.ltpy.adamplatform.eu](https://training.ltpy.adamplatform.eu)
* Create an account: [https://login.ltpy.adamplatform.eu](https://login.ltpy.adamplatform.eu)
* Log into the `JupyterLab` with your account created.
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<hr>
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<img src='../img/copernicus_logo.png' alt='Logo EU Copernicus' align='right' width='20%'><br><br><br>
<p style="text-align:right;">This project is licensed under the <a href="./LICENSE">MIT License</a> and is developed under a Copernicus contract.
MIT License
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