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Content archived on 2024-04-30

River basin modelling, management and flood mitigation.

Objective



The research programme aims to develop and enhance the accuracy with which precipitation can be measured by weather radars at a range of temporal and spatial resolutions. To facilitate this, a number of fundamental, inter-related issues including radar reflectivitylrainfall intensity relationship, drop-size distributions, vertical reflectivity profiles and raingauge adjustment techniques will be investigated. In parallel with this, work will be conducted on the development of improved techniques for quantitative precipitation forecasts as a means of increasing the leadtimes of urban and rural flow forecasts. The work will focus on the development of a dynamic time series approach to precipitation modelling, a detailed analysis convective rainfall systems and the application of adaptive neural network models to complex, dynamic precipitation systems.
The work described above will be integrated in order to provide input data for global climate change models. In addition, the future frequency of severe weather systems in Europe will be addressed. Finally, the research programme aims to bridge the divide between the meteorological and hydrological domains byinterfacing quantitative precipitation forecasts with hydrological models - both rural and urban - and to report on the spatial and temporal resolutions required. By combining the expertise of two National Meteorological Institutes with two European Centres of Excellence in radar hydrology, that considerable progress can be made in the field of high resolution precipitation estimation and forecasting using remotely sensed data for meteorological and hydrological applications.
KEYWORDS (max 10):
weather radar, neural networks, climate change, convective rainfall, quantitative precipitation forecasts

Call for proposal

Data not available

Coordinator

H R Wallingford Group Ltd
EU contribution
No data
Address
Howberry Park
OX10 8BA Wallingford
United Kingdom

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Total cost
No data

Participants (5)