Accessibility navigation

Using co-production to improve the appropriate use of sub-seasonal forecasts in Africa

Hirons, L. ORCID:, Thompson, E., Dione, C., Indasi, V. S., Kilavi, M., Nkiaka, E., Talib, J., Visman, E., Adefisan, E. A., de Andrade, F., Ashong, J., Mwesigwa, J. B., Boult, V. ORCID:, Diédhiou, T., Konte, O., Gudoshava, M., Kiptum, C., Amoah, R. K., Lamptey, B., Lawal, K. A. , Muita, R., Nzekwu, R., Nying'uro, P., Ochieng, W., Olaniyan, E., Opoku, N. K., Endris, H. S., Segele, Z., Igri, P. M., Mwangi, E. and Woolnough, S. ORCID: (2021) Using co-production to improve the appropriate use of sub-seasonal forecasts in Africa. Climate Services, 23. 100246. ISSN 2405-8807

Text (Open Access) - Published Version
· Available under License Creative Commons Attribution.
· Please see our End User Agreement before downloading.

[img] Text - Accepted Version
· Restricted to Repository staff only


It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1016/j.cliser.2021.100246


Forecasts on sub-seasonal to seasonal (S2S) timescales have huge potential to aid preparedness and disaster risk reduction planning decisions in a variety of sectors. However, realising this potential depends on the provision of reliable information that can be appropriately applied in the decision-making context of users. This study describes the African SWIFT (Science for Weather Information and Forecasting Techniques) forecasting testbed which brings together researchers, forecast producers and users from a range of African and UK institutions. The forecasting testbed is piloting the provision of real-time, bespoke S2S forecast products to decision-makers in Africa. Drawing on data from the kick-off workshop and initial case study examples, this study critically reflects on the co-production process. Specifically, having direct access to real-time data has allowed user-guided iterations to the spatial scale, timing, visualisation and communication of forecast products to make them more actionable for users. Some key lessons for effective co-production are emerging. First, it is critical to ensure there is sufficient resource to support co-production, especially in the early co-exploration of needs. Second, all the groups in the co-production process require capacity building to effectively work in new knowledge systems. Third, evaluation should be ongoing and combine meteorological verification with decision-makers feedback. Ensuring the sustainability of project-initiated services within the testbed hinges on integrating the knowledge-exchanges between individuals in the co-production process into shaping sustainable pathways for improved operational S2S forecasting within African institutions.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:99641


Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation