Introduction#

Welcome to PyCPT Version 2 for Seasonal Climate Forecasts! The goal of this tutorial is to learn how to configure and run PyCPT, Version 2, 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. 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 2022, initialized in May 2022. The example uses a two-model multimodel ensemble of the NCEP CFSv2 and ECMWF SEAS5 models.

PyCPT 2 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

  • easily customizable to individual forecaster needs using Jupyter Notebooks, by importing the four new Python modules, available via the iri-nextgen conda channel

  • designed to be run natively using Python on linux, Windows and Mac (feature still in progress for Windows)

  • designed to facilitiate the use of observational gridded datasets, such as ENACTS, from the user’s laptop in NetCDF or CPTv10.tsv format. (feature still in progress)

Who is this guide for?

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