Evaluation of WRF model rainfall forecast using citizen science in a data-scarce urban catchment
Addis Ababa, Ethiopia
25 November 2022
Citizen science in action
Authors: Hailay Zeray Tedla, Estefanos Fikadu Taye, David Walker, Alemseged Tamiru Haile
Understanding the accuracy of rainfall forecasts in the data-scarce urban catchment has a multitude of benefits given the increased urban flood risk caused by climate change and urbanization. In this study, accuracy of the weather research and forecasting (WRF) model rainfall forecast was evaluated using citizen science data. Categorical and continuous accuracy evaluation metrics were used beside gauge representativeness effect.
Highlights:
- The error of the WRF model rainfall forecast was evaluated using citizen science data as reference.
- The rainfall forecast has low error for short lead times of up to 3 days which has a potential for operational purposes in the Akaki catchment.
- The evaluation of forecast errors using data from the dense rain gauge network showed the limitation of a conventional rain gauge network.
- Future research can evaluate the rainfall forecasts to drive a rainfall-runoff model and inform flood early warnings.
