PyCPT2-Seasonal-Forecast-User-Guide

Seasonal climate forecasting with GCM hindcasts involves evaluating model selection, applying MOS techniques like CCA and PCR for calibration, and generating probabilistic forecasts. This Skill guides you through the complete NextGen approach using PyCPT 2.5.

Applying MOS techniques for bias correction in climate models requires using CCA or PCR to calibrate GCM outputs. This Skill automates the Model Output Statistics process within the PyCPT 2.5 platform, validating results with skill assessment metrics.

To validate and assess the skill of seasonal climate forecasts, you calculate various skill metrics against observed datasets. This Skill enables comprehensive skill assessment by comparing MOS model outputs with historical data using Python libraries like xarray and numpy.

Yes, you need access to the IRI Data Library to retrieve GCM hindcasts and observed datasets. This Skill requires these specific datasets as inputs to perform model fitting, bias correction, and probabilistic forecasting for your target region.

Yes, you can use xarray and cartopy for climate forecast visualization. This Skill utilizes xarray for data handling and cartopy alongside matplotlib for plotting skill scores and generating visual outputs of deterministic and probabilistic forecasts.

To generate probabilistic rainfall forecasts for West Africa, you load a case directory in PyCPT 2.5, configure predictand and predictor datasets, run MOS analysis, and output probabilistic predictions. This Skill provides a complete workflow for regional climate forecasting.