Visualising climate data

With this project we want to make complex climate data accessible to people who want to start a permaculture project. We provide intuitive graphical representations of climate informations for a location of your choosing as well as projections on the evolution of the climate in the future. To render the data more accessible we provide a dashboard in addition to a Jupyter notebook.

What we learned/improved

• Create an app with a database using Dash, Plotly and SQL
• Manage netcdf and grib files (Xarray and Pandas)
• Write API requests
• Recognize bottlenecks and optimizig performance
• Project management with GitHub, code organization for better readability

As establishing a healthy and balanced ecosystem is a longterm endavour, and climate change is a rising issue, we included climate projections for the next 30 years. Here we, a biologist and a meteorologist, teamed up to provide a summary of the local climate, easy to understand without technical knowledge.

Datasets

The dataset used for the past climate data is the ERA5 monthly averaged data on single levels from 1940 to present. This dataset provides data on a number of different variables from 1940 to the present, offering a resolution of 0.25 degrees. Here we will present rainfall, temperature, humidity, wind diretion and speed and total cloud cover, as we think these variables are the more important when it comes to starting a permaculture project. As we take a box of 2x2 ° (roughly 250x250 km) around the selected location, the graphs we provide here will give you an idea of the general climate in your region.

For the projecton data we opted for the CMIP6 climate projections dataset coming from CDS. This dataset assumes a rise in temperature of 4.5 °C, as well as a shared socioeconomic pathway index of 2. We opted for those two options as they offer a scenario with intermediate challenges and are the middle pathway of all available scenarios.
For more information on the dataset and the criteria of the scenarios we recommend the paper: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. From this dataset we took projected rainfall, temperature, wind speed and direction.

Graphical representation of the data

Here we want to show one of our graphs as an example. This particular graph is made for Puebla de don Fadrique, Spain, as there is a starting permaculture project we provide the data for.

Dashboard creation

To make this program more user friendly, we decided to create two versions. One is a jupyter notebook where the code is executed cellwise, while the other is a dashboard. The idea behind the dashobard is that once all dependencies are installed, the climate informations are accessible to anyone, regardless of their python knowledge. The design is kept as minimalistic as possible. After entering a location the graphs are calculated and plottet, along with a description of the graph and a small introductory text for the selected location. While the accessibility problem was solved creating a dashboard, it left the challenge of long loading times.

Database management and performance optimization

In order to decrease loading times we implemented two things. First we downloaded a dataset that includes all of mainland Europe since the standard CDS API can be quite slow. This means that only one time consuming API request is needed to get the required data before using the dashboard for the first time.
At the same time this meant that the already slow process of loading the grib files beccomes even more tideous. We approaced this by moving the variables from grib files (for the past data) and nc files (for the prediction data) into a .db file, accessible using SQL querys, which substantially reduced loading times.
Another step we took to achive better performance is parallelization of the climatology calculations using dask.

Future steps and expansion

While our dashboard runs locally, we developed it in a way that it can be deployed online without much extra effort.

We would also like to include biological information specifically centered around permaculture. Meaning, in addition to the meteorological data provided here, the user gets a list of plants that grow in the selected area, and ideally also infromations on grwoth temperature, water needs, and so on. This was not realisable yet as this information is not accessible for plant species that are outside of the main agricoltural focus, but are really important for a local ecosystem and therefore permaculture.