WeatherXM cell modelling

Introduction

This is a CLI tool used to identify the optimum location of WeatherXM stations distribution around the world.

Earth’s surface is not uniform, each area has its own meteorological peculiarities, from nearly uniform pieces of land that extend many kilometres, to small islands that include coastlines and hills and urban areas in a few square kilometres, to cities where skyscrapers are next to parks, lakes and sea.

WeatherXM's mission involves the development of an all-encompassing weather network from the ground up. This initiative seeks to not only cater to the needs of its clients but also to contribute significantly to the scientific domains of meteorology and climatology. As a general rule, the design of a weather network should ensure that the collected data are representative and sufficient, and can be used to derive the analysis required from the measurements. Hence, our network aims to: a. meet the needs of various purposes, which may conflict together, b. be robust in cases that one or more sensors fail to measure, c. take into account different needs, purposes and users with different temporal and spatial scales.

To locate the areas needing meteorological coverage via weather stations:

• We created a grid of hexagonal cells (size 7 of H3 grid system) that cover the whole Earth.
• We used topographic and urban land use data to identify each cell’s characteristics.
• We broke down each cell into zones, and used the zones to calculate the number of stations required in each cell (aka “cell capacity”).

See more details at: https://docs.google.com/document/d/1Cdr2sJ7cKMgymt6SI_5UwHs855FI8yizdjb-mJyFJnE/edit?usp=sharing

Installation

Prerequisites

The tool requires

• gdal library
• python 3.10+

Application

The recomended way to install cellxm is with poetry. For Debian or Ubuntu it can be installed using:

sudo apt-get install libgdal-dev

You also need to get the version of the installed gdal (x.y.z):

gdal-config --version

1. Install Python 3.10+
2. Install poetry pip install poetry
3. Navigate into the source directory
4. Run poetry add gdal==x.y.z
5. Run poetry install
6. Spawn a shell with the virtual environment poetry shell

Usage

python cellxm/main.py locate [OPTIONS] [COUNTRY_CODE] [CONFIG]

Example:
python cellxm/main.py locate GR --out-folder ./data/outputs/GR

The secondary command combine can be used to concatenate multiple results in a single file per output type

python cellxm/main.py combine [IN_FOLDER] [OUT_FOLDER]

Example:
python cellxm/main.py combine --in-folder ./data/outputs/countries --out-folder ./data/outputs/countries-all

Arguments:

• [COUNTRY_CODE]: The country code to run the application. Should follow the ISO 3166-1 alpha-2 specification [default: GR]
• [CONFIG]: The location of the config file. The required options are marked inside the file [default: ./config.toml]

Options:

Directly passing an option overrides the corresponding value in the config file

• --h3-resolution INTEGER: The resolution for the H3 grid. Should be between 0 and 15
• --elev-diff-thresh FLOAT: The elevation difference inside a cell to trigger the heuristic
• --cell-buffer FLOAT: Buffer distance around a cell where stations cannot be placed.
• --min-area-green FLOAT: The minimum area to consider a green area as individual element
• --min-area-urban FLOAT: The minimum area to consider an urban area as individual element
• --min-aspect-zone-area-cell-perc FLOAT : The minimum area of an aspect zone as a percentage of the average h3 cell area"
• --largest-urban : Boolean to specify which urban areas to keep. If True only the largest urban area for each cell will be exported else all
• --largest-green: Boolean to specify which green areas to keep. If True only the largest green area for each urban region will be exported else all
• --include-buildings : Use buildings to infer urban areas. This reduces the likelihood of not accurately capturing urban areas due to missing information in OSM. However this comes at increased computational cost (~1.5 longer processing time)
• --export-zones: Boolean to specify whether the zones for each area should be exported or not
• --out-folder TEXT: Location to export the results [default: ./data/outputs]
• --ncpus INTEGER: The number of cpus to be used
• --help: Show this message and exit.

Outputs

All files are in .gpkg format (QGIS is the easiest tool to open them). More information about the output is described in the methodology.

• cells.gpkg
  • The cells covering the analysed country (only above land)
• zones.gpkg
  • The zones which can host a station
  • characterised by 8 types:
    • topo (required by topography)
      1. High (topo-h)
      2. North (topo-n)
      3. East (topo-e)
      4. South (topo-s)
      5. West (topo-w)
    • landuse (required by land use)
      1. Urban (lu-urban)
      2. Green (lu-green)
      3. Coastline (lu-coast)
• stations.gpkg
  • Indicative points (usually centroids) within the above zones
  • Same types as for zones

Notes

Recommended specs (for a complete world run):

• 8 CPU cores
• 32GB RAM

If you experience problems (e.g. unexpected crashes), reduce the ncpus parameter

The process:

• Downloads OSM data from the geofabrik website.
• Uses the Copernicus GLO-90 Digital Elevation Model.
• Creates a ./.cache folder to store OSM and DEM data. The folder can be safely deleted but subsuquent runs will be delayed. As long as OSM data are available in the ./.cache/osm folder no check for more recent OSM data will take place.
• The run.sh is a script to automate the exection of the process for multiple countries. In this file the complete list of country codes is also available
• Has not been tested on Windows and is very likely to not work on this platform. However, it has been tested with Linux and MacOS for both x86 and arm64 architectures.