WAS-NextGen: An Objective Multi-Method Framework for Seasonal Climate Forecasting in West Africa
Invited talk in the WMO Artificial Intelligence Webinar Series — “Good practices of KNUST and AGRHYMET”.
About this talk
Presented in the WMO Artificial Intelligence Webinar Series (session on the good practices of KNUST and AGRHYMET), this talk introduces WAS-NextGen, an objective, reproducible framework for seasonal climate forecasting in West Africa and the Sahel, implemented through the open-source Python package wass2s.
Watch the recording
The WAS-NextGen segment begins at 29:38 (the KNUST presentation opens the session).
Slides
Abstract
Seasonal climate forecasting in West Africa has traditionally relied on consensus-based methods that lack reproducibility and high-resolution detail. This presentation introduces WAS-NextGen, an automated, multi-method framework that integrates machine-learning-calibrated multi-model ensembles (SV–ML–CMME), statistical–dynamical CCA calibration, lagged-predictor components, and analogue-year methods. Implemented via the open-source Python package wass2s, the system ensures a fully reproducible workflow from data acquisition to probabilistic mapping.
Highlights
- From subjective to objective. Replaces consensus-based forecasting with an automated, traceable, skill-assessed pipeline, aligned with WMO guidance for objective and operational seasonal forecasting.
- One reproducible workflow. End-to-end and modular: data acquisition → preprocessing and bias correction → model training with leakage-free cross-validation → verification → multi-model ensemble → probabilistic tercile maps.
- Multiple forecasting engines, combined objectively. ML-calibrated multi-model ensembles (SV–ML–CMME), statistical–dynamical CCA calibration, lagged-predictor models, and analogue-year methods — behind a common interface.
- Open source and operational. Built on the
wass2sPython package, supporting national meteorological and hydrological services and capacity building across the region.
Resources
- 📦 Package:
wass2son GitHub · documentation · PyPI (pip install wass2s) - 📄 Paper: wass2s: An Open-Source Python Tool for Objective Seasonal Climate Forecasting in West Africa and the Sahel (SSRN)
- 🎙️ Webinar series: WMO AI Webinars
How to cite
@misc{houngnibo_wass2s,
author = {Houngnibo, Coovi Mahuw{\`e}tin Mandela and Segnon, Alcade
and Tonle, Franck and Kiema, Ars{\`e}ne W. and Ali, Abdou
and Sounouke, Valerie H. and Zougmor{\'e}, Robert},
title = {{wass2s}: An Open-Source Python Tool for Objective Seasonal
Climate Forecasting in West Africa and the Sahel},
year = {2026},
note = {SSRN preprint},
url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6569116}
}Acknowledgements
Developed at the AGRHYMET Regional Climate Centre for West Africa and the Sahel (RCC-WAS), with support from the Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) project.