1. Urban pluvial flood forecasting - rainfall data processing - experimental site in Redbridge
Prof. Čedo Maksimović, Dr. Christian Onof, Li-Pen Wang
Dept. Civil and Environmental Engineering, Imperial College London
Visitors from University of Iowa, ICL

2. Surface Flooding Forecast / Warning
A more reliable flooding forecast could be implemented if a high-resolution rainfall prediction with longer lead time is carried out
Rainfall Depth
Observed
Predicted
Resolution
Now
Time
Lead time
Runoff Modelling
Sewer Simulation
Surface Flooding Forecast / Warning

3. Rainfall data processing
Aim to carry out high-resolution rainfall forecasting with longer lead time
Rainfall data processing

4. NOWCASTING An integrated notion
Focus on statistically-based downscaling techniques
Why deterministic?  because the aim of this work is to “forecast.”

5. Possible improvements in Nowcasting, Calibration & Downscaling
Information based upon NWP outputs
Advection of rainfields
Larger-scale weather variations
Enhanced radar observations
X-Band radar and ground raingauge data
Super-resolution techniques to improve the spatial resolution
Statistically-based space-time downscaling techniques
Cascade-based spatial disaggregation method
Temporal correlation between successive rainfields

6. Generalised Linear Models (GLMs)

7. Poisson cluster based methods

8. Cascade-based Methods: scale-invariance

9. Cascade-based methods: mapping to rainfall sequences

10. Cran Brook catchment in the London borough of Redbridge
Experimental site

11. Redbridge catchment and Roding river

12. Radars surrounding the Redbridge area

13. Three real-time accessible tipping bucket raingauges over the Redbridge catchment (frequency: 1 – 5 min)

14. Real-time access to 30+ weather monitoring systems in each London Borough (frequency: 30 min)

15. One pressure sensor for Roding river (frequency: 5-10 min)

16. Two sensors for water depth measurement in sewers (frequency: 5/10 min)

17. One sensors for water depth measurement in open channels (frequency: 5-10 min)

18. Thank you for your attention
Li-Pen Wang