PyCPT2-Seasonal-Forecast-User-Guide

What problem does it solve?

This Skill Unit provides comprehensive guidance and tools for users to perform subseasonal to seasonal climate forecasting using the PyCPT 2.5 platform.

Core Features & Use Cases

• Model Selection: Evaluate and select the most appropriate GCM models for a specific region and season.
• MOS Techniques: Apply Model Output Statistics (MOS) techniques like CCA and PCR for bias correction and forecast calibration.
• Skill Assessment: Validate and verify the performance of the MOS models using various skill metrics.
• Forecasting: Generate both deterministic and probabilistic forecasts for the desired region and season.
• Use Case: A researcher wants to predict the rainfall over West Africa for the upcoming season. This Skill Unit guides them through the entire process, from data selection and model fitting to forecast generation and skill assessment.

Quick Start

Open the PyCPT 2.5 environment and load the 'pycpt_WAfricaJJAS_startMay2023' case directory. Configure the model selection and predictand/predictor datasets. Run the analysis and plot the skill scores for each GCM.