ESA training course modules added

50_workshops/202103_ESA_training_course/01_discover_satellite_data_for_dust_monitoring/.ipynb_checkpoints/00_index-checkpoint.ipynb

+%% Cell type:markdown id: tags:
+
+<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)
+
+%% Cell type:markdown id: tags:
+
+<br>
+
+%% Cell type:markdown id: tags:
+
+#### 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.
+
+%% Cell type:markdown id: tags:
+
+<hr>
+
+%% Cell type:markdown id: tags:
+
+<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.

50_workshops/202103_ESA_training_course/01_discover_satellite_data_for_dust_monitoring/00_index.ipynb

+%% Cell type:markdown id: tags:
+
+<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)
+
+%% Cell type:markdown id: tags:
+
+<br>
+
+%% Cell type:markdown id: tags:
+
+#### 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.
+
+%% Cell type:markdown id: tags:
+
+<hr>
+
+%% Cell type:markdown id: tags:
+
+<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.

50_workshops/202103_ESA_training_course/01_discover_satellite_data_for_dust_monitoring/LICENSE

+MIT License
+
+Copyright (c) 2021 EUMETSAT
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.