StaGE: The Standard Gridding Engine

In the first part of this talk, we highlight the importance of data standardization, both within an organisation and across organisations. As both physics and machine‑learning bases weather and climate models advance rapidly, the diversity of available datasets, metadata conventions, and grid geometries grow with it. While this expansion offers new scientific advancements, it also creates challenges for comparing outputs across different models and organisations. Differences in vertical coordinate definitions, horizontal resolutions, and variable naming can hinder consistent analysis, reduce reproducibility, and introduce ambiguity when comparing datasets. 

The rise of machine‑learning (ML) workflows further increases the need for reliable, standardised input data. ML systems are highly sensitive to inconsistencies, and even minor metadata issues can degrade performance and take time to resolve. By embedding data standardisation directly into preprocessing, we can reduce manual manipulation of the data, lower the risk of silent errors, and support the creation of high‑quality datasets suitable for training and evaluation. 

In the second part of this talk, we discuss how the Met Office handles standardization: StaGE, The Standard Gridding Engine. This a python library designed to streamline the preparation and standardisation of meteorological datasets. StaGE provides a unified framework for harmonising metadata, enforcing naming conventions, and ensuring that key attributes such as units, coordinates etc, are clear and consistent across different datasets. This allows us to output standardised data for customers, as well as other departments within the Met Office.

Of the various submodules in StaGE, a key component is its robust regridding functionality, which enables model datasets, which can often have model specific vertical levels and sit on staggered horizontal grids, to be mapped onto a common, predefined set of vertical levels (height and pressure for example) and unstaggered horizontal grids. As models increasingly adopt diverse geometries and resolutions, this functionality is essential for meaningful comparisons.

Overall, StaGE provides an efficient and scientifically robust foundation for working with the expanding range of meteorological data, enhancing interoperability and enabling clearer, more reliable analysis across models and methods.