Scripts to Preprocess CLIMATE_GRID Dataset

This repository contains a collection of Python scripts to:

1. Download the CLIMATE_GRID dataset from the RMI Oracle database as vector data and store it as CSV.
2. Convert the dataset to NetCDF format with the necessary attributes.
3. Remap the dataset to a self-defined lat-lon grid.

(c) Inne Vanderkelen, Bert van Schaeybroeck, Nicolas Ghilain June 2024

Overview of Files

Scripts

• preprocess_CLIMATE_GRID.py:
  • Connects to the RMI Oracle database and downloads the CLIMATE_GRID data.
  • Converts and saves the data as a NetCDF file.
• remap_CLIMATE_GRID_to_latlon.py:
  • Remaps the projected (raw) CLIMATE_GRID NetCDF data to a lat/lon grid.
  • The target grid can be user-defined within the script.

Data Files

• grid_5kmx5km.csv:
  • Contains pixel lat and lon variables to transpose to a user-defined grid.
  • Used by preprocess_CLIMATE_GRID.py.
• CLIMATE_GRID_meta.csv:
  • Contains the variable names and units for the CLIMATE_GRID dataset.
  • Used to write the NetCDF file by preprocess_CLIMATE_GRID.py.
• lambert_coordinates_full_climate_grid.csv:
  • Lists pixel IDs and corresponding x, y coordinates in LAMBERT2008.
  • Used by preprocess_CLIMATE_GRID.py.

Usage

0. Requirements

Installed version of python (eg through a conda installation, possible with miniconda or Anaconda Navigator )

The environment with packages used by the script is included in the environment.yml file and can be installed using conda as follows:

# Create a conda environment based on environment.yml
conda env create -f environment.yml

# Activate the environment
conda activate env_climategrid

1. Download and Preprocess the Data:

   python preprocess_CLIMATE_GRID.py

2. Remapping the netcdf files Open remap_CLIMATE_GRID_to_latlon.py and do user adjustments (grid, metadata etc). Then execute the script:

   python remap_CLIMATE_GRID_to_latlon.py