Key lessons from the DACCIWA project for operational meteorological services

• DOI: 10.5281/zenodo.3957318
• Publication Date: 2020-07-23
• Authors: Peter Knippertz; John H. Marsham; Angela Benedetti; M. J. Evans; Andréas Fink; Anke Kniffka; Roderick van der ...; C. Dearden; Konrad Deetz; Sophie L. Haslett; Sékou Keita; Fabienne Lohou; Marlon Maranan; James Mollard; Gregor Pante; Irene Reinares Ma...; Matthew Young; Aristide Akpo; B.N. Adler; Leonard K. Amekudzi; Karmen Babić; Jean‐Pierre Chabo...; J. Christine Chiu; Hugh Coe; Cheikh Dione; Catherine Leal-Li...; Peter Hill; Norbert Kalthoff; Bernhard Vogel; Véronique Yoboué
• Keywords: info:eu-repo/classification/ddc/000; 550; 000; ddc:000; air pollution; information & general works; prediction; life_on_land; 15. Life on land; sustainable_cities_and_communities; Computer science; numerical weather prediction models; West Africa, weather, climate, air pollution, prediction, numerical weather prediction models, satellite data, field campaign; 13. Climate action; weather; West Africa; field campaign; climate; satellite data; good_health_and_well_being; climate_action; affordable_and_clean_energy

This document describes the conclusions of the EU-funded project Dynamics- Aerosol-Chemistry-Cloud Interactions in West Africa (DACCIWA) directly relevant to operational meteorological services. DACCIWA produced the most comprehensive observational dataset of the atmosphere over densely populated southern West Africa to date and used this dataset to foster our understanding of atmospheric processes, and to evaluate dynamical models and satellite data. With this document DACCIWA aims to help improve atmospheric predictions across time-scales, which are important for the development of greater resilience of the West African population to hazardous weather and climate change.