{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"Supports participation in the ECMWF AI Weather Quest competition by enabling forecast submission, evaluation of sub-seasonal forecasts, and training data download for AI-based weather prediction models.","skillfed_tags":["weather-forecasting","competition-framework","research-sandbox"],"use_cases":["Download and prepare sub-seasonal weather training datasets for developing machine learning forecast models.","Evaluate AI-based forecast predictions against ground truth using the competition's evaluation framework.","Submit forecast results to the ECMWF AI Weather Quest competition platform.","Visualize sub-seasonal forecast data and model outputs using integrated matplotlib and cartopy tools.","Prototype and iterate on forecast models in a research environment with built-in data pipeline support."],"what_it_does":"AI-WQ-package is a research-focused Python library for the ECMWF AI Weather Quest competition. It provides tools to download training data, develop and evaluate sub-seasonal forecast models, and submit forecasts to the competition platform. The package wraps xarray for efficient NetCDF-based data handling and integrates dask for parallel computation, numpy for numerical operations, scipy for scientific algorithms, pandas for tabular data, matplotlib and cartopy for visualization, and requests for API communication.\n\nThe package is explicitly marked as sandbox-level software under active development and not suitable for operational use. It is intended for research, testing, and competition participation only. Support is best-effort through community channels rather than official ECMWF support.","worth_installing":"Yes, if you are actively participating in the ECMWF AI Weather Quest competition or conducting research on sub-seasonal forecasting. The low install friction, active maintenance, and permissive license support research use. No, if you need production-grade weather forecasting tools or operational deployment\u2014the package explicitly disclaims such use and is sandbox-level software."},"id":"ai-wq-package","links":{"html":"https://skillfed.io/packages/ai-wq-package","md":"https://skillfed.io/packages/ai-wq-package.md","pypi":"https://pypi.org/project/ai-wq-package/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":null,"license_treatment":"permissive","name":"AI-WQ-package","python_support":"supports_current","summary":"A python package to support forecast submission, visualisation and evaluation for S2S ML/AI prediction project"},"popularity":{"monthly_downloads":122264,"position":11957,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.29"}