wass2s: A python-based tool for seasonal climate forecast in West Africa and the Sahel.
Contents:
Installation
Usage
Data Download & Management
Preprocessing Modules
Computing Predictands
Agro-Climatic Seasonality
Spell Analysis (Dry/Wet)
ETCCDI Temperature Extremes
ETCCDI Precipitation Extremes
Input Data Formats
Merging Gridded Data with Observations
Prerequisites & Data Formats
Class Initialization
Merging Methods
Visualization
Usage Example
Bias Correction Modules
Prerequisites
Precipitation Bias Correction (WAS_Qmap)
Continuous Bias Correction (WAS_bias_correction)
Data Transformation & Skewness Analysis
Prerequisites & Input Data
Skewness Detection and Handling
Distribution Fitting (Clustering Approach)
Visualization
Models and Cross-Validation
Linear Regression and Regularization Models
Linear Regression
Ridge Regression (L2 Regularization)
Lasso Regression (L1 Regularization)
ElasticNet (L1 + L2 Regularization)
Probabilistic Calculation Methods
Advanced Machine Learning Models
Support Vector Regression (SVR)
Multi-Layer Perceptron (MLP)
Stacking Ensemble (RF + XGB + MLP)
Multivariate Adaptive Regression Splines (MARS)
Optimization Methods
EOF Analysis & Principal Component Regression
EOF Analysis (WAS_EOF)
Principal Component Regression (WAS_PCR)
CCA Models
Analog Forecasting Methods
Quantifying Uncertainty via Cross-Validation
Estimating Prediction Uncertainty
Verification Module
Multi-Model Ensemble (MME) Techniques
1. Data Preparation
2. Weighted Ensembles (Linear)
3. Min et al. (2009) Probabilistic MME
4. Machine Learning Ensembles
5. Calibration & Post‑Processing
Implementation of WAS-NextGen Approaches
API Reference
wass2s: A python-based tool for seasonal climate forecast in West Africa and the Sahel.
Usage
Implementation of WAS-NextGen Approaches
View page source
Implementation of WAS-NextGen Approaches
Under construction