# On solving coordinate problems in climate model output and other geospatial datasets

## TL;DR

This paper introduces methods to handle complex coordinate systems in climate model data, making it easier to use in standard geospatial tools.

## Contribution

The paper presents two post-processing methods using Python and CDO to handle RCM outputs with rotated grids.

## Key findings

- Two methods allow RCM outputs to be used without interpolation or reprojection.
- The methods enable resampling onto regular geographic grids.
- The approaches use Python and CDO to improve compatibility with GIS and analysis tools.

## Abstract

The output from Regional Climate Models (RCMs) can be difficult for non-specialists to handle. Standard geospatial analysis tools expect coordinate reference systems to be encoded inside file metadata. In addition to different metadata conventions, RCMs that are run over limited domains in the Arctic and Antarctic frequently have rotated longitude and latitude grids that add additional complexity compared to geographic datasets. In this article, we describe two post-processing methods that make RCM outputs easier to use for applications in the climate and related sciences. We demonstrate two different approaches that allow output from RCMs to be 1) read on the correct grid without interpolating or reprojecting the dataset, or 2) resampled onto a regular grid that includes geographic coordinates. These two approaches use the widely available and free software tools Python and Climate Data Operators (CDO). These transformations make outputs simple to use in Geographic Information Systems (GIS) and allow the full use of Python libraries, such as xarray, for plotting and analysis.

The datasets exist on grids with coordinates. The coordinates of one grid can be related to those of another using a coordinate reference system. Lack or poor encoding of coordinate reference systems can lead to incompatibilities, errors, approximations, and bottlenecks. We present ways to read the coordinate reference systems correctly and to recover them if they are missing, using two programming languages. We provide real case examples and discuss the implications of the presented methods.