SWIFT

African Science for Weather Information and Forecasting Techniques

African Science for Weather Information and Forecasting

The African Science for Weather Information and Forecasting Techniques (SWIFT) project aims to develop a sustainable research capability in tropical weather forecasting to enhance the livelihoods

of African populations and improve economic development. The project brings together leading research institutions in Africa and the United Kingdom and is funded by the Global Challenges Research Fund (GCRF).

ICPAC is currently involved in two research work packages (WP-R2: Forecast evaluation and WP-R6: Sub-seasonal to Seasonal (S2S) prediction) as well as in case studies, testbeds and training.

Our Areas of Work

Sub-seasonal to Seasonal (S2S) Skills

Forecast skill assessment is an essential component of weather and climate forecasting for both the producers and the users of the forecast. ICPAC evaluates the skill of global operational prediction systems using data from the recently launched WMO S2S project over the Greater Horn of Africa. The skill assessment analysis contributes for selecting a subset of models for the construction of multi-model ensemble for producing objectively consolidated S2S forecasts for the region.

S2S Drivers

In collaboration with other institutions, ICPAC evaluates the representation of the drivers of intra-seasonal variability and their modulation to the Greater Horn of Africa rainfall in operational sub-seasonal prediction systems.

Case Studies

The SWIFT team is working on different case studies over the Greater Horn of Africa including the March-April-May 2018 extremely wet and 2019 extremely dry seasons, to better understand the various systems at different scales. These case studies are very important to develop improved understanding of climate processes and to develop concepts which can be understood by different audiences.

Testbeds

SWIFT supports different forecasting testbeds, where weather forecasters, researchers and identified users from different institutions come together for a limited period of time to perform operational forecasting. Testbeds, which are often preceded by training activities, are recognized as a key tool to improve weather predictions in different regions.

Key Outputs

  • Reports on implementation of verification metrics for S2S and probabilistic forecasts
  • Scientific papers on the analysis of operational prediction systems
  • S2S forecast testbed
  • Training material on S2S prediction

More information: https://africanswift.org/

Project Materials

Brochure

Latest Events

Climate Forum
GHACOF64: Climate Services for Anticipatory Action
Climate Forum 24 May 2023 - 25 May 2023

Climate Forum
GHACOF 61
Climate Forum 17 May 2022 - 19 May 2022

Climate Forum
GHACOF 60: Early Warning for Early Action
Climate Forum 15 Feb 2022 - 17 Feb 2022

Training
Pre-GHACOF 60 Forecast Development Workshop
Training 07 Feb 2022 - 12 Feb 2022

Latest Updates

Communiqué du 64ème Forum de Prévision Climatique de la Grande Corne de l'Afrique (GHACOF 64), 22-24 Mai 2023 ; Addis Abeba, Éthiopie
Report
Communiqué du 64ème Forum de Prévision Climatique de la Grande Corne de l'Afrique (GHACOF 64), 22-24 Mai 2023 ; Addis Abeba, Éthiopie

La période de Juin à Septembre (JJAS) est une saison des pluies importante, en particulier dans les…

Statement from the 64th Greater Horn of Africa Climate Outlook Forum (GHACOF64)
Report
Statement from the 64th Greater Horn of Africa Climate Outlook Forum (GHACOF64)

June to September (JJAS) is an important rainy season, especially in the northern regions of the Gr…

Summary for Decision Makers, March to May 2023 Season
Report
Summary for Decision Makers, March to May 2023 Season

Download our latest Summary for Decision Makers with impacts and advisories for the following secto…

Technical Statement from the 63rd Greater Horn of Africa Climate Outlook Forum (GHACOF63)
Report
Technical Statement from the 63rd Greater Horn of Africa Climate Outlook Forum (GHACOF63)

March to May (MAM) constitutes an important rainfall season, particularly in the equatorial parts o…