EGU24-13509, updated on 09 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Optimising oceanic rainfall estimates for increased lead time of stream level forecasting: A case study of GPM IMERG estimates application in the UK

Cristiane Girotto1, Farzad Piadeh2, Kourosh Behzadian1,3, Massoud Zolgharni1, Luiza Campos3, and Albert Chen4
Cristiane Girotto et al.
  • 1University of West London, School of Computing and Engineering, London, UK
  • 2Centre for Engineering research, School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, UK
  • 3Dept of Civil, Environmental and Geomatic Engineering, University College London, London, UK
  • 4College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK


Among the three main rainfall data sources (rain gauge stations, rainfall radar stations and weather satellites), satellites are often the most appropriate for longer lead times in real-time flood forecasting [1]. This is particularly relevant in the UK, where severe rainfall events often originate over the Atlantic Ocean, distant from land-based instruments although it can also limit the effectiveness of satellite data for long-term predictions [2]. The Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) estimates can be used as an alternative source for rainfall information in real-time flood forecasting models. However, the challenge lies in monitoring the vast oceanic region around the UK and integrating this extensive data into hydrological or data-driven models, which presents computational and time constraints. Identifying key monitoring area for obtaining these estimates is essential to address these challenges and to effectively use this use for water level forecasting in urban drainage systems (UDS).

This study introduced an optimised data-driven model for streamline the collection and use of GPM IMERG rainfall estimates for water level forecasting in UDS. The model’s effectiveness was demonstrated using a 20-year satellite data set from the Atlantic Ocean, west of the UK, focusing on water level forecasting for a specific UDS point in London. This data helped identify the most probable path of rainfall from the Atlantic that impacts UDS water levels. We conducted a cross-correlation analysis between the water level records and each IMERG data pixel within the selected oceanic area.

The analysis successfully pinpointed the most influential rainfall points/pixels along the Atlantic path and their respective lag times between rainfall occurrence and water level changes at any satellite-monitored point until it reaches the mainland and joins the river system. This research enhances understanding of long-distance rainfall patterns while optimising the use of GPM IMERG data. It also aids in reducing data volume and processing time for stream-level forecasting models, aiming for longer lead times.


[1] Piadeh, F., Behzadian, K., Alani, A., 2022. A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, p.127476.

[2] Speight, L., Cole, S., Moore, R., Pierce, C., Wright, B., Golding, B., Cranston, M., Tavendale, A., Dhondia, J., Ghimire S. (2016). Developing surface water flood forecasting capabilities in Scotland: an operational pilot for the 2014 Commonwealth Games in Glasgow. Journal of Flood Risk Management, 11(S2), pp. S884-S901.

How to cite: Girotto, C., Piadeh, F., Behzadian, K., Zolgharni, M., Campos, L., and Chen, A.: Optimising oceanic rainfall estimates for increased lead time of stream level forecasting: A case study of GPM IMERG estimates application in the UK, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13509,, 2024.