Nowcasting precipitation events from mesoscale convective systems for Dakar, Senegal

Senegal, located in the West Sahel region, frequently experiences flooding driven by mesoscale convective systems (MCSs), which contribute 90% of the region’s rainfall. Current early warning systems for hydrological extremes struggle with timely and accurate predictions, necessitating advancements in precipitation nowcasting. Nowcasting describes short-term weather forecasts with a lead time of typically less than two hours. In this region traditional numerical weather models have limited accuracy in predicting short-term events, and nowcasting models therefore outperform numerical weather prediction in this time frame. Precipitation nowcasts can be helpful in supporting and informing decision makers on time to adapt to the risk and protect society from hydrological extremes.

A major challenge in developing warning systems for this region is the lack of radar data coverage, which is typically used in nowcasting models, compounded by a sparse ground-based observational network. Increasing the data availability and understanding the properties of MCSs could enhance the predictability of regional weather conditions, which is a primary objective of the High-resolution weather observations East of Dakar (DakE)-project. During the project, 14 automated weather stations have already been installed east of Dakar.

The objective of this study, which is part of the DakE-project, is to integrate the in-situ station data with satellite data to develop a precipitation nowcasting model that is optimally adapted to local conditions considering different spatial and temporal scales. An optical flow routine, based on statistical extrapolation of the current state of the atmosphere, is used for this purpose. To incorporate a stochastic term, which represents the unpredictable component, the STEPS (short-term ensemble prediction system) approach is applied. The skill of the forecast depends, among other things, on the geographical location, the spatial and temporal scales and the meteorological conditions, since developments that do not fulfil the steady-state assumption, such as the initiation, growth and termination of convective systems, are not resolved. The next step is to investigate whether these shortcomings can be compensated by implementing machine learning approaches.

 

References:

 

Anderson, Seonaid R., et al. "Nowcasting convective activity for the Sahel: A simple probabilistic approach using real‐time and historical satellite data on cloud‐top temperature." Quarterly Journal of the Royal Meteorological Society150.759 (2024): 597-617.

Mathon, V., Laurent, H., & Lebel, T. (2002). Mesoscale convective system rainfall in the Sahel. Journal of Applied Meteorology and Climatology, 41(11), 1081-1092.

Pulkkinen, S., Nerini, D., Pérez Hortal, A. A., Velasco-Forero, C., Seed, A., Germann, U., & Foresti, L. (2019). Pysteps: An open-source Python library for probabilistic precipitation nowcasting (v1. 0). Geoscientific Model Development, 12(10), 4185-4219.

Taylor, Christopher M., et al. "Nowcasting tracks of severe convective storms in West Africa from observations of land surface state." Environmental Research Letters 17.3 (2022): 034016.

 

 

Keywords: Nowcasting, Senegal, Mesoscale Convective System, Precipitation