{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/9"}],"enrichment":{"capability":"Computes meteorological quantities (potential temperature, thermodynamic properties) from atmospheric data using NumPy, Torch, CuPy arrays, xarray, or fieldlist formats.","skillfed_tags":["meteorology","atmospheric-physics","array-agnostic"],"use_cases":["Compute potential temperature from model or observational data for atmospheric analysis.","Process weather datasets in multiple array formats without rewriting calculation logic.","Integrate meteorological computations into data pipelines using NumPy or Torch tensors.","Perform thermodynamic calculations as part of ECMWF earthkit-based workflows.","Accelerate atmospheric physics on GPUs by passing CuPy or Torch arrays to the library."],"what_it_does":"earthkit-meteo is a meteorological computation library from ECMWF that wraps standard atmospheric physics calculations to work with multiple array backends. It lets you compute thermodynamic properties like potential temperature from temperature and pressure data, accepting NumPy arrays, Torch tensors, CuPy arrays, xarray objects, or fieldlist formats as input. The library is part of the broader earthkit ecosystem and is classified as Graduated and Production/Stable.\n\nYou use it when you need to perform standard meteorological calculations on weather or atmospheric data without writing the physics formulas yourself. It abstracts away the array-backend differences so the same code works whether your data lives in NumPy, on a GPU via Torch, or in other formats. The package has low install friction, depends only on deprecation, earthkit-utils, and numpy at runtime, and is actively maintained.","worth_installing":"Yes, if you work with atmospheric or weather data and need standard meteorological calculations. The package is actively maintained, has low install friction, carries a permissive license, and supports modern Python versions. It is worth installing for meteorological workflows, especially if you already use NumPy or Torch and want to avoid reimplementing atmospheric physics."},"id":"earthkit-meteo","links":{"html":"https://skillfed.io/packages/earthkit-meteo","md":"https://skillfed.io/packages/earthkit-meteo.md","pypi":"https://pypi.org/project/earthkit-meteo/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-05","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"earthkit-meteo","python_support":"supports_current","summary":"A Python library for meteorological computations"},"popularity":{"monthly_downloads":76528,"position":14618,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1.0"}