EGU22-9719, updated on 09 Jan 2024
EGU General Assembly 2022
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Seasonal Forecasting of Horn of Africa’s Long Rains Using Physics-Guided Machine Learning

Victoria Deman, Akash Koppa, and Diego Miralles
Victoria Deman et al.
  • Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium

The Horn of Africa is known to be prone to climate impacts; the frequent occurrence of droughts and floods creates vulnerable conditions in the region. Gaining knowledge on (sub-)seasonal weather prediction and generating more reliable long-term forecasts is an important asset in building resilience. Most of the region is characterized by a bimodal precipitation cycle with rainfall seasons in boreal spring (March–May), termed the long rains, and boreal autumn (October–November), termed the short rains. Previous studies on seasonal forecasting focused mostly on empirical linear regression methods using information from ocean–atmosphere modes. To date, the potential of more complex methods, such as machine learning approaches, in improving seasonal precipitation predictability in the Horn of Africa still remains understudied. 


In this study, machine learning models targeting precipitation during the long rains are developed. The focus on the long rains is motivated by the fact that it is the main rain season in the region and the sources of predictability have proven to be more difficult to pin down. The long rain season has a weak internal coherence and looking at the months separately has proven to enhance prediction skill. Therefore, machine learning models are constructed for the different months (March, April, and May) separately at lead times of 1–3 months. Following an extensive survey of literature, the predictors of the long rain precipitation at seasonal timescales selected in this study include coupled oceanic-atmospheric oscillation indices (such as MJO, ENSO and PDO), regions of zonal winds over 200mb and 850mb and sea-surface temperature (SST) regions with strong correlation to long rain precipitation. Further, a selection of additional terrestrial and oceanic predictors is guided by Lagrangian transport modeling, used to identify the regions sourcing moisture during the long rains. This set of predictors include soil moisture, land surface temperature, normalized vegetation index (NDVI), leaf area index (LAI) and SST, which are averaged over the climatological source region of long rain precipitation. Finally, we provide new insights into the predictability of long rain precipitation at seasonal timescales by analyzing the relative importance of the different predictors used for developing the machine learning model.

How to cite: Deman, V., Koppa, A., and Miralles, D.: Seasonal Forecasting of Horn of Africa’s Long Rains Using Physics-Guided Machine Learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9719,, 2022.