Amy Green

Researcher, Research Assistant

Current work

I currently work on the PYRAMID (Platform for dYnamic, hyper-resolution, near-real time flood Risk AssessMent Integrating repurposed and novel Data sources) project, focused on developing a digitally-enabled environment for near real-time flood forecasting. My work on this project is primarily on data and visualization for flood risk management, sourcing novel data as inputs for hydrological model inputs, and working on creating interactive visualistations for a suitable flood risk management platform.


I have recently submitted a doctoral thesis entitled improving radar rainfall estimation for flood risk using Monte Carlo ensemble simulation was part of the DREAM CDT, funded through NERC. This project aims to explore and exploit the space-time properties of rainfall and reflectivity, to gain an improved understanding of the error structure between the two, by identifying the extent of uncertainties within the radar rainfall estimation process. This is achieved by generating an ensemble of realistic rainfall event fields, with a prescribed correlation structure, marginal distribution and advection. Simulation methods are enriched by clustering event properties, for improved parametrisation. For each rainfall field, an ensemble of realistic radar images can then be generated, with imposed errors representing different uncertainties in the radar rainfall estimation process. This is done by inverting standard radar processing methods, allowing the identification of the frequency and extent of lost important information. The wider implications in hydrology are considered, including the impact on discharge uncertainty. This simulation tool has been demonstrated to identify the impact of uncertainty on quantitative precipitation estimation. This provides an increased understanding of the spatial behaviour of radar rainfall errors, allowing for the improved design of correction strategies.

I have a masters degree in mathematics and statistics from Newcastle University, and have a keen interest in predicting environmental extremes, as well as applied statistics, and coding.