Extreme rainfall forecasting: new statistical simulation and Big Data methods for making sense of rainfall radar and rain gauges

Lead Research Organisation: Newcastle University
Department Name: Sch of Engineering


The huge potential for rainfall radar for flood forecasts and risk assessments in real time for cities and small catchments has not yet been realised due to uncertainties in observing high rainfall intensities and data volumes when used at full resolution. Conventional radar calibration procedures only partly account for do not account for beam attenuation by intense rainfall, so invalidating the product in its use for the very application where it has most potential.

A new high resolution rainfall radar has been installed in Newcastle for use in flood risk studies in combination with a dense telemetered rain gauge network across the city and surrounding area. This project will develop big data and statistical simulation approaches to calibrating and using the radar and rain gauge data effectively for real time and long term assessments of flood risk.

Further, conventional calibration approaches attempt to provide a best estimate in the minimum error sense, thus leading to a variance reduction. The radar images are usually interpreted as integrals of precipitation over the time between two scans, however in reality they are snapshots. We will to interpret radar measurements according to their correct characteristics. A new data-intensive approach will be developed using new advances in space-time conditional stochastic simulation and applied to the new rainfall radar.

Risk - the radar data provide information on the occurrence in real time of flood hazard and the archive allows estimation of extremes. By combination with other information on impacts and damages from rainfall and flooding on e.g. transport and buildings from the Urban Observatory, flood risk to people and infrastructure can be analysed both predictively and retrospectively: in a radical departure from conventional weather and flood forecasting, risk will be used to guide and better target calibration and forecasting procedures.

Mitigation - risk assessment and forecasting will allow better warning and adaptation of operations by stakeholders in the region (e.g. Northumbrian Water Ltd, NEXUS transport and University Estates Services) who have significant exposure to convective storm flooding.
Big Data - high volume and rate data will be generated and analysed from the radar and telemetered rain gauges. 5 years of data will be available for analysis by the end of the project (started June 2016). However, orders of magnitude higher data volumes (TB) will be generated by the Monte Carlo ensemble statistical simulation methods to be used for calibration and uncertainty estimation. The Monte Carlo ensembles simulation in high space time resolution generates a huge amount of data to be analysed.


10 25 50
Description DREAM Challenge week 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Postgraduate students
Results and Impact Group work to solve real world challenges with other PhD students in my Centre for Doctoral Training (DREAM), with presentations to and input from industrial partners.
Year(s) Of Engagement Activity 2018,2019
Description Participation in DREAM Symposium 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Postgraduate students
Results and Impact Conference to discuss current research and present projects from my Centre for Doctoral Training (DREAM).
Year(s) Of Engagement Activity 2018,2019,2020