EGU23-7802, updated on 25 Feb 2023
https://doi.org/10.5194/egusphere-egu23-7802
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Hybrid dynamical/statistical forecasts of wet season onset using CCA

Andrew Colman
Andrew Colman
  • Met Office, Climate Research, Exeter, United Kingdom of Great Britain – England, Scotland, Wales (andrew.colman@metoffice.gov.uk)

The Climate Predictability Tool (CPT) is a well established tool for creating calibrated objective predictions of seasonal rainfall anomalies, and is used for this purpose by many institutions including  the IGAD Climate Prediction and Applications Centre (ICPAC)  to create operational forecasts for the Greater Horn of Africa wet seasons. CPT can also be used to predict other variables such as wet season onset. Such predictions require a non spatially dependent definition of onset, in our case we define onset as the number of days into the season when rainfall reaches 10% of seasonal rainfall for that location. The CPT forecasts are created by detecting relationships between predictions of precipitation and SST from global dynamical forecasting systems and observed onset patterns using Canonical Correlation Analysis (CCA).  CPT has the advantage that skill statistics are automatically produced for assessing the performance of the forecasts. CPT forecasts of Short rains (October-December) onset have been found to have useful skill.

How to cite: Colman, A.: Hybrid dynamical/statistical forecasts of wet season onset using CCA, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7802, https://doi.org/10.5194/egusphere-egu23-7802, 2023.