Introduction#

Welcome to PyCPT Version 2.5 for Seasonal Climate Forecasts! The goal of this tutorial is to learn how to configure and run PyCPT, Version 2.5+, in order to make, calibrate and verify multi-model seasonal forecasts of precipitation based on the NOAA North American Multi-Model Ensemble (NMME) and European Copernicus Climate Change Service (C3S) databases, obtained from IRI Data Library. PyCPT helps to implement the NextGen approach using the Climate Predictability Tool (CPT).

The PyCPT set-up and results are illustrated for a West African precipitation forecast example for Jun-Sep 2023, initialized in May 2023. The example uses a two-model multimodel ensemble of the NCEP CFSv2 and ECMWF SEAS5 models.

PyCPT 2.5 is:

  • a rewrite of PyCPT based on Xarray and a new set of Python libraries that provide a Python interface to the CPT fortran-based package, and facilitate access to GCM forecasts/hindcasts and observational gridded datasets via IRI Data Library

  • designed to be run natively using Python on linux, Windows and Mac

  • easily installed by downloading a python conda environment file for your platform

  • easily customizable to individual forecaster needs using Jupyter Notebooks

  • enables the use of observational predictand gridded datasets, such as ENACTS, from the user’s laptop in CPTv10.tsv format.

Who is this guide for?

Basic familiarity with Python, seasonal climate forecasting, and IRI’s Climate Predictability Tool (CPT) is assumed.