Experiences using Python libraries as a meteorological service provider

In recent years, Python has become an integral component of operational data processing pipelines in meteorology. Its growing ecosystem of scientific and domain-specific libraries has enabled flexible, scalable, and maintainable solutions for handling diverse data types, including in situ observations, remote sensing products, and numerical weather prediction (NWP) outputs. For meteorological service providers, Python offers a powerful framework to bridge research developments and operational applications.

In this contribution, we present practical experiences from integrating Python-based tools into the workflows of providers of meteorological data services. We highlight how a combination of widely used scientific libraries and community-driven meteorological packages has been employed to address key challenges such as data ingestion, format harmonization, quality control, and post-processing. Particular attention is given to the handling of heterogeneous data formats (e.g., GRIB, NetCDF, BUFR) and the efficient processing of large datasets in near-real-time environments.

We further discuss how Python libraries support the implementation of user-oriented services, including tailored forecast products, data visualization, and verification workflows. The modularity of the Python ecosystem allows rapid prototyping and iterative development, which is essential for responding to evolving user requirements and incorporating new data sources, such as AI-based forecasts.

At the same time, the use of Python in operational settings introduces challenges related to performance, dependency management, and long-term maintainability. We share lessons learned regarding the balance between flexibility and robustness, including strategies for testing, deployment, and integration with existing infrastructure.

By providing concrete examples from our operational environment, this contribution aims to illustrate both the benefits and limitations of Python as a core technology for meteorological service providers, and to inform best practices for its effective use in production systems.