Skip to content

GitLab

Commit 46d2de5d authored by Julia Wagemann's avatar Julia Wagemann
Browse files

Rename data discovery to data exploration

parent 1c3308ef
Hide whitespace changes
Inline Side-by-side
00_index.ipynb
View file @ 46d2de5d
%% Cell type:markdown id: tags:
<img src='./img/EU-Copernicus-EUM_3Logos.png' alt='Logo EU Copernicus EUMETSAT' align='right' width='50%'></img>
%% Cell type:markdown id: tags:
<br>
%% Cell type:markdown id: tags:
# LTPy - Learning Tool for Python on Atmospheric Composition Data
%% Cell type:markdown id: tags:
<br>
%% Cell type:markdown id: tags:
**LTPy - Learning tool for Python on Atmospheric Composition Data** is a Python-based training course on Atmospheric Composition Data. The training course covers [10 - DATA ACCESS](#data_access), [20 - DATA DISCOVERY](#data_discovery), [30 - CASE STUDIES](#case_studies) and [40 - EXERCISES](#exercises) of satellite- and model-based data on Atmospheric Composition.
**LTPy - Learning tool for Python on Atmospheric Composition Data** is a Python-based training course on Atmospheric Composition Data. The training course covers [10 - DATA ACCESS](#data_access), [20 - DATA EXPLORATION](#data_exploration), [30 - CASE STUDIES](#case_studies) and [40 - EXERCISES](#exercises) of satellite- and model-based data on Atmospheric Composition.
The course is based on [Jupyter notebooks](https://jupyter.org/), which allow for a high-level of interactive learning, as code, text description and visualisation is combined in one place. If you have not worked with `Jupyter Notebooks` before, you can look at the module [01 - Python and Project Jupyter 101](./01_Python_and_Jupyter_101.ipynb) to get a short introduction to Jupyter notebooks and their benefits.
%% Cell type:markdown id: tags:
<hr>
%% Cell type:markdown id: tags:
## Data on Atmospheric Composition
%% Cell type:markdown id: tags:
This course features the following **satellite** data:
* `Metop-A/B/C GOME-2 Level 2` data
* `Metop-A/B/C GOME-2 Level 3` reprocessed and regridded data
* `Polar Multi-Sensor Aerosol Optical Properties (PMAp) Level 2` data
* `Metop-A/B/C IASI Level 2` data
* `Copernicus Sentinel-5P TROPOMI Level 2` data
* `Copernicus Sentinel-3 OLCI Level 1B` data
* `Copernicus Sentinel-3 SLSTR NRT FRP Level 2` data
* `Copernicus Sentinel-3 SLSTR NRT AOD Level 2` data
And the following **model-based** data:
* `Copernicus Atmosphere Monitoring Service (CAMS) Global Reanalysis (EAC4)` data
* `Copernicus Atmosphere Monitoring Service (CAMS) Global Fire Assimilation System (GFAS)` data
* `Coperncus Emergency Management Service (CEMS) Global ECMWF Fire Forecast (GEFF)` data
%% Cell type:markdown id: tags:
<hr>
%% Cell type:markdown id: tags:
## Course material
%% Cell type:markdown id: tags:
The course follows a modular approach and offers modules on:
- [10 - DATA ACCESS](#data_access)
- [20 - DATA DISCOVERY](#data_discovery)
- [20 - DATA EXPLORATION](#data_exploration)
- [30 - CASE STUDIES](#case_studies)
- [40 - EXERCISES](#exercises)
<br>
<div class="alert alert-block alert-info">
<b><a id='data_access'></a>10 - DATA ACCESS</b>
</div>
* [11 - Atmospheric Composition data overview and acccess](./10_data_access/11_ac_data_access_overview.ipynb)
* [12 - WEkEO Harmonized Data Access API](./10_data_access/12_WEkEO_harmonized_data_access_api.ipynb)
<br>
<div class="alert alert-block alert-success">
<b><a id='data_discovery'></a>20 - DATA DISCOVERY</b>
<b><a id='data_exploration'></a>20 - DATA EXPLORATION</b>
</div>
#### *Metop-A/B/C GOME-2 Level 2 and Level 3 data*
* [211 - Metop-A GOME-2 - Tropospheric NO<sub>2</sub> - Level 2 - Load and browse](./20_data_discovery/211_Metop-A_GOME-2_NO2Tropo_L2_load_browse.ipynb)
* [212 - Metop-A/B GOME-2 - Tropospheric NO<sub>2</sub> - Level 2 - Pre-process](./20_data_discovery/212_Metop-AB_GOME-2_NO2Tropo_L2_preprocess.ipynb)
* [213 - Metop-A/B GOME-2 - Tropospheric NO<sub>2</sub> - Level 3 - Load and browse](./20_data_discovery/213_Metop-AB_GOME-2_NO2Tropo_L3_load_browse.ipynb)
* [214 - Metop-A/B/C GOME-2 - Absorbing Aerosol Index - Level 3 - Load and browse](./20_data_discovery/214_Metop-ABC_GOME-2_AAI_L3_load_browse.ipynb)
* [215 - Metop-A/B/C GOME-2 - Absorbing Aerosol Height - Level 2 - Load and browse](./20_data_discovery/215_Metop-B_GOME-2_AAH_L2_load_browse.ipynb)
* [211 - Metop-A GOME-2 - Tropospheric NO<sub>2</sub> - Level 2 - Load and browse](./20_data_exploration/211_Metop-A_GOME-2_NO2Tropo_L2_load_browse.ipynb)
* [212 - Metop-A/B GOME-2 - Tropospheric NO<sub>2</sub> - Level 2 - Pre-process](./20_data_exploration/212_Metop-AB_GOME-2_NO2Tropo_L2_preprocess.ipynb)
* [213 - Metop-A/B GOME-2 - Tropospheric NO<sub>2</sub> - Level 3 - Load and browse](./20_data_exploration/213_Metop-AB_GOME-2_NO2Tropo_L3_load_browse.ipynb)
* [214 - Metop-A/B/C GOME-2 - Absorbing Aerosol Index - Level 3 - Load and browse](./20_data_exploration/214_Metop-ABC_GOME-2_AAI_L3_load_browse.ipynb)
* [215 - Metop-A/B/C GOME-2 - Absorbing Aerosol Height - Level 2 - Load and browse](./20_data_exploration/215_Metop-B_GOME-2_AAH_L2_load_browse.ipynb)
#### *Polar Multi-Sensor Aerosol Optical Properties (PMAp) Level 2 data*
* [221 - Polar Multi-Sensor Aerosol Optical Properties (PMAp) - Aerosol Optical Depth - Level 2 - Load and browse](./20_data_discovery/221_PMAp_AOD_L2_load_browse.ipynb)
* [221 - Polar Multi-Sensor Aerosol Optical Properties (PMAp) - Aerosol Optical Depth - Level 2 - Load and browse](./20_data_exploration/221_PMAp_AOD_L2_load_browse.ipynb)
#### *Metop-A/B IASI Level 2 data*
* [231 - Metop-A/B IASI - Ammonia (NH<sub>3</sub>) - Level 2 - Load and browse](./20_data_discovery/231_Metop-AB_IASI_NH3_L2_load_browse.ipynb)
* [232 - Metop-A/B IASI - Carbon Monoxide - Level 2 - Load and browse](./20_data_discovery/232_Metop-AB_IASI_CO_L2_load_browse.ipynb)
* [231 - Metop-A/B IASI - Ammonia (NH<sub>3</sub>) - Level 2 - Load and browse](./20_data_exploration/231_Metop-AB_IASI_NH3_L2_load_browse.ipynb)
* [232 - Metop-A/B IASI - Carbon Monoxide - Level 2 - Load and browse](./20_data_exploration/232_Metop-AB_IASI_CO_L2_load_browse.ipynb)
#### *Sentinel-5P TROPOMI Level 2 data*
* [241 - Sentinel-5P TROPOMI - Carbon Monoxide - Level 2 - Load and browse](./20_data_discovery/241_Sentinel-5P_TROPOMI_CO_L2_load_browse.ipynb)
* [242 - Sentinel-5P TROPOMI - Ultraviolet Aerosol Index - Level 2 - Load and browse](./20_data_discovery/242_Sentinel-5P_TROPOMI_UVAI_L2_load_browse.ipynb)
* [241 - Sentinel-5P TROPOMI - Carbon Monoxide - Level 2 - Load and browse](./20_data_exploration/241_Sentinel-5P_TROPOMI_CO_L2_load_browse.ipynb)
* [242 - Sentinel-5P TROPOMI - Ultraviolet Aerosol Index - Level 2 - Load and browse](./20_data_exploration/242_Sentinel-5P_TROPOMI_UVAI_L2_load_browse.ipynb)
#### *Sentinel-3 data*
* [251 - Sentinel-3 OLCI - Radiances - Level 1 - Load and browse](./20_data_discovery/251_Sentinel-3_OLCI_radiance_L1_load_browse.ipynb)
* [252 - Sentinel-3 SLSTR NRT - Fire Radiative Power (FRP) - Level 2 - Load and browse](./20_data_discovery/252_Sentinel-3_SLSTR_NRT_FRP_L2_load_browse.ipynb)
* [253 - Sentinel-3 SLSTR NRT - Aerosol Optical Depth (AOD) - Level 2 - Load and browse](./20_data_discovery/253_Sentinel-3_SLSTR_NRT_AOD_L2_load_browse.ipynb)
* [251 - Sentinel-3 OLCI - Radiances - Level 1 - Load and browse](./20_data_exploration/251_Sentinel-3_OLCI_radiance_L1_load_browse.ipynb)
* [252 - Sentinel-3 SLSTR NRT - Fire Radiative Power (FRP) - Level 2 - Load and browse](./20_data_exploration/252_Sentinel-3_SLSTR_NRT_FRP_L2_load_browse.ipynb)
* [253 - Sentinel-3 SLSTR NRT - Aerosol Optical Depth (AOD) - Level 2 - Load and browse](./20_data_exploration/253_Sentinel-3_SLSTR_NRT_AOD_L2_load_browse.ipynb)
#### *Copernicus Atmosphere Monitoring Service (CAMS) data*
* [261 - CAMS Global reanalysis (EAC4) - Organic Matter Aerosol Optical Depth - Load and browse](./20_data_discovery/261_CAMS_EAC4_OMAOD_load_browse.ipynb)
* [262 - CAMS Global Fire Assimilation System (GFAS) - Fire Radiative Power - Load and browse](./20_data_discovery/262_CAMS_GFAS_FRPFIRE_load_browse.ipynb)
* [263 - CAMS Global Forecast - Dust Aerosol Optical Depth - Load and browse](./20_data_discovery/263_CAMS_global_forecast_duaod_load_browse.ipynb)
* [264 - CAMS European air quality forecast - Dust Concentration - Load and browse](./20_data_discovery/264_CAMS_European_air_quality_forecast_dust_concentration_load_browse.ipynb)
* [265 - European air quality forecast - Nitrogen Dioxide - Load and browse](./20_data_discovery/265_CAMS_European_air_quality_forecast_NO2_load_browse.ipynb)
* [261 - CAMS Global reanalysis (EAC4) - Organic Matter Aerosol Optical Depth - Load and browse](./20_data_exploration/261_CAMS_EAC4_OMAOD_load_browse.ipynb)
* [262 - CAMS Global Fire Assimilation System (GFAS) - Fire Radiative Power - Load and browse](./20_data_exploration/262_CAMS_GFAS_FRPFIRE_load_browse.ipynb)
* [263 - CAMS Global Forecast - Dust Aerosol Optical Depth - Load and browse](./20_data_exploration/263_CAMS_global_forecast_duaod_load_browse.ipynb)
* [264 - CAMS European air quality forecast - Dust Concentration - Load and browse](./20_data_exploration/264_CAMS_European_air_quality_forecast_dust_concentration_load_browse.ipynb)
* [265 - European air quality forecast - Nitrogen Dioxide - Load and browse](./20_data_exploration/265_CAMS_European_air_quality_forecast_NO2_load_browse.ipynb)
#### *Copernicus Emergency Management Service (CEMS) data*
* [271 - CEMS Global ECMWF Fire Forecast - Fire Weather Index - Load and browse](./20_data_discovery/271_CEMS_GEFF_FWI_load_browse.ipynb)
* [272 - CEMS Global ECMWF Fire Forecast - Fire Weather Index - Harmonized Danger Classes](./20_data_discovery/272_CEMS_GEFF_FWI_harmonized_danger_classes.ipynb)
* [273 - CEMS Global ECMWF Fire Forecast - Fire Weather Index - Custom Danger Classes](./20_data_discovery/273_CEMS_GEFF_FWI_custom_danger_classes.ipynb)
* [271 - CEMS Global ECMWF Fire Forecast - Fire Weather Index - Load and browse](./20_data_exploration/271_CEMS_GEFF_FWI_load_browse.ipynb)
* [272 - CEMS Global ECMWF Fire Forecast - Fire Weather Index - Harmonized Danger Classes](./20_data_exploration/272_CEMS_GEFF_FWI_harmonized_danger_classes.ipynb)
* [273 - CEMS Global ECMWF Fire Forecast - Fire Weather Index - Custom Danger Classes](./20_data_exploration/273_CEMS_GEFF_FWI_custom_danger_classes.ipynb)
<br>
<div class="alert alert-block alert-warning">
<b><a id='case_studies'></a>30 - CASE STUDIES</b>
</div>
#### *Fires*
* [310 - Siberian fires 2021](./30_case_studies/310_fire_siberia_2021.ipynb)
* [311 - Amazon fires 2019](./30_case_studies/311_fire_amazon_2019.ipynb)
* [312 - Siberian fires 2019](./30_case_studies/312_fire_siberia_2019.ipynb)
* [313 - Californian fires 2020](./30_case_studies/313_fire_california_2020.ipynb)
* [314 - Chernobly fires 2020 - Sentinel-3 SLSTR NRT - Fire Radiative Power](./30_case_studies/314_fire_chernobyl_2020_Sentinel-3_SLSTR_NRT_FRP_L2.ipynb)
* [315 - Californian fires 2020 - Sentinel-3 SLSTR NRT - Fire Radiative Power](./30_case_studies/315_fire_california_2020_Sentinel-3_SLSTR_NRT_FRP_L2.ipynb)
* [316 - Californian fires 2020 - Sentinel-3 SLSTR NRT - Aerosol Optical Depth](./30_case_studies/316_fire_california_2020_Sentinel-3_SLSTR_NRT_AOD_L2.ipynb)
* [317 - Indonesian fires 2015](./30_case_studies/317_fire_indonesia_2015.ipynb)
* [318 - Indonesian fires 2020](./30_case_studies/318_fire_indonesia_2020.ipynb)
* [319 - Portugal fires 2020](./30_case_studies/319_fire_portugal_2020.ipynb)
#### *Air pollution*
* [321 - Map and time-series analysis - Metop-A/B GOME-2 - Tropospheric NO<sub>2</sub>](./30_case_studies/321_air_pollution_map_time-series_Metop-AB_GOME-2_NO2Tropo_L3.ipynb)
* [322 - Produce gridded dataset - Metop-A/B GOME-2 - Tropospheric NO<sub>2</sub>](./30_case_studies/322_air_pollution_produce_gridded_Metop-AB_GOME-2_NO2Tropo_L2.ipynb)
* [323 - Create an anomaly map - Europe - Metop-A/B GOME-2 - Tropospheric NO<sub>2</sub>](./30_case_studies/323_air_pollution_map_europe_2020_Metop-AB_GOME-2_NO2Tropo_L2.ipynb)
* [324 - Time-series analysis - Europe - Metop-A/B GOME-2 - Tropospheric NO<sub>2</sub>](./30_case_studies/324_air_pollution_time-series_europe_2020_Metop-AB_GOME-2_NO2Tropo_L2.ipynb)
* [325 - Create an anomaly map - Europe - Sentinel-5P TROPOMI - Tropospheric NO<sub>2</sub>](./30_case_studies/325_air_pollution_map_europe_2020_Sentinel-5P_TROPOMI_NO2Tropo_L2.ipynb)
* [326 - Time-series analysis - Europe - Sentinel-5P TROPOMI - Tropospheric NO<sub>2</sub>](./30_case_studies/326_air_pollution_time-series_europe_2020_Sentinel-5P_TROPOMI_NO2Tropo_L2.ipynb)
#### *Stratospheric Ozone*
* [331 - Antarctic ozone hole 2019 - Multi-data](./30_case_studies/331_stratospheric_ozone_Antarctic_2019.ipynb)
* [332 - Antarctic ozone hole 2019 - CAMS animation](./30_case_studies/332_stratospheric_ozone_Antarctic_2019_CAMS_EAC4_animation.ipynb)
* [333 - Antarctic ozone hole 2020 - Metop-A/B/C GOME-2 Level 2](./30_case_studies/333_stratospheric_ozone_Antarctic_2020_Metop-ABC_GOME-2_O3_L2.ipynb)
* [334 - Arctic ozone hole 2020 - Metop-A/B/C IASI Level 2](./30_case_studies/334_stratospheric_ozone_Arctic_2020_Metop-ABC_IASI_O3_L2.ipynb)
* [335 - Arctic ozone hole 2020 - CAMS Reanalysis (EAC4)](./30_case_studies/335_stratospheric_ozone_Arctic_2020_CAMS_EAC4_O3.ipynb)
<br>
<div class="alert alert-block alert-danger">
<b><a id='exercises'></a>40 - EXERCISES</b>
</div>
#### *Sentinel-5P TROPOMI*
* [411 - Sentinel-5P TROPOMI - Carbon Monoxide - Level 2](./40_exercises/411_Sentinel-5P_TROPOMI_CO_L2_exercise.ipynb)
#### *Sentinel-3*
* [421 - Sentinel-3 OLCI - Radiances - Level 1](./40_exercises/421_Sentinel-3_OLCI_radiance_L1_exercise.ipynb)
* [422 - Sentinel-3 SLSTR NRT - Fire Radiative Power](./40_exercises/422_Sentinel-3_SLSTR_NRT_FRP_L2_exercise.ipynb)
* [423 - Sentinel-3 SLSTR NRT - Aerosol Optical Depth](./40_exercises/423_Sentinel-3_SLSTR_NRT_AOD_L2_exercise.ipynb)
#### *Copernicus Atmosphere Monitoring Service*
* [431 - CAMS Global Reanalysis (EAC4) - Total Column Carbon Monoxide](./40_exercises/431_CAMS_EAC4_tcco_exercise.ipynb)
#### *Metop-A/B/C GOME-2 and IASI*
* [441 - Metop-A/B/C GOME-2 - Ozone](./40_exercises/441_Metop-ABC_GOME-2_O3_L2_exercise.ipynb)
* [442 - Metop-A/B/C IASI - Ozone](./40_exercises/442_Metop-ABC_IASI_O3_L2_exercise.ipynb)
<br>
**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.
If a notebook makes use of these functions, they are loaded as **helper functions** at the beginning of the notebook. With `?function_name`, you can load the function's docstring to see what it does and which keyword arguments the function requires.
See the example to load the docstring of the function [visualize_pcolormesh](./functions.ipynb#visualize_pcolormesh):
%% Cell type:code id: tags:
``` python
%run ./functions.ipynb
```
%% Cell type:code id: tags:
``` python
?visualize_pcolormesh
```
%% Cell type:markdown id: tags:
<hr>
%% Cell type:markdown id: tags:
## Learning outcomes
%% Cell type:markdown id: tags:
The course is designed for `medium-level users`, who have basic Python knowledge and understanding of Atmospheric composition data.
After the course, you should have:
* an idea about the **different datasets on Atmospheric Composition data**,
* knowledge about the most useful **Python packages** to handle, process and visualise large volumes of Earth Observation data
* an idea about different **data application areas**
%% Cell type:markdown id: tags:
<hr>
%% Cell type:markdown id: tags:
## Access to the `LTPy JupyterHub`
%% Cell type:markdown id: tags:
The course material is made available on a JupyterHub instance, a pre-defined environment that give learners direct access to the data and Python packages required for following the course.
The `JupyterHub` can be accessed as follows:
%% Cell type:markdown id: tags:
* Web address: [https://ltpy.adamplatform.eu](https://ltpy.adamplatform.eu)
* Create an account: [https://login.ltpy.adamplatform.eu/](https://login.ltpy.adamplatform.eu/)
* Log into the `JupyterHub` with your account created.
%% Cell type:markdown id: tags:
<hr>
%% Cell type:markdown id: tags:
## Reproduce LTPy on Atmospheric Compostion data locally
%% Cell type:markdown id: tags:
In case you wish to reproduce the course modules on your local setup, the following Python version and Python packages will be required:
* Python version: **Python3.8**
* Python version: **Python3.10.4**
* Python packages: see [requirements.txt](./requirements.txt)
Python packages can be installed as follows: `pip install -r requirements.txt`.
%% Cell type:markdown id: tags:
The `eodata` folder with all the data required for the training course can be accessed and downloaded from [https://sftp.eumetsat.int](https://sftp.eumetsat.int/login). Find the user name and password in order to be able to login [here](https://gitlab.eumetsat.int/eumetlab/atmosphere/atmosphere/-/blob/master/sftp_login.txt).
%% Cell type:markdown id: tags:
<hr>
%% Cell type:markdown id: tags:
<p><img src='./img/copernicus_logo.png' align='left' alt='Logo EU Copernicus' width='25%'></img></p>
<br clear=left>
<p style="text-align:left;">This project is licensed under the <a href="./LICENSE">MIT License</a> <span style="float:right;"><a href="https://gitlab.eumetsat.int/eumetlab/atmosphere/atmosphere">View on GitLab</a> | <a href="https://training.eumetsat.int/">EUMETSAT Training</a>
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment