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| Titel |
Probabilistic forecasts based on radar rainfall uncertainty |
| VerfasserIn |
S. Liguori, M. A. Rico-Ramirez |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250058734
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| Zusammenfassung |
| The potential advantages resulting from integrating weather radar rainfall estimates in
hydro-meteorological forecasting systems is limited by the inherent uncertainty affecting
radar rainfall measurements, which is due to various sources of error [1-3]. The improvement
of quality control and correction techniques is recognized to play a role for the future
improvement of radar-based flow predictions. However, the knowledge of the uncertainty
affecting radar rainfall data can also be effectively used to build a hydro-meteorological
forecasting system in a probabilistic framework.
This work discusses the results of the implementation of a novel probabilistic forecasting
system developed to improve ensemble predictions over a small urban area located
in the North of England. An ensemble of radar rainfall fields can be determined
as the sum of a deterministic component and a perturbation field, the latter being
informed by the knowledge of the spatial-temporal characteristics of the radar error
assessed with reference to rain-gauges measurements. This approach is similar to the
REAL system [4] developed for use in the Southern-Alps. The radar uncertainty
estimate can then be propagated with a nowcasting model, used to extrapolate an
ensemble of radar rainfall forecasts, which can ultimately drive hydrological ensemble
predictions.
A radar ensemble generator has been calibrated using radar rainfall data made
available from the UK Met Office after applying post-processing and corrections
algorithms [5-6]. One hour rainfall accumulations from 235 rain gauges recorded for
the year 2007 have provided the reference to determine the radar error. Statistics
describing the spatial characteristics of the error (i.e. mean and covariance) have been
computed off-line at gauges location, along with the parameters describing the error
temporal correlation. A system has then been set up to impose the space-time error
properties to stochastic perturbations, generated in real-time at gauges location, and
then interpolated back onto the radar domain, in order to obtain probabilistic radar
rainfall fields in real time. The deterministic nowcasting model integrated in the
STEPS system [7-8] has been used for the purpose of propagating the uncertainty and
assessing the benefit of implementing the radar ensemble generator for probabilistic
rainfall forecasts and ultimately sewer flow predictions. For this purpose, events
representative of different types of precipitation (i.e. stratiform/convective) and
significant at the urban catchment scale (i.e. in terms of sewer overflow within the
urban drainage system) have been selected. As high spatial/temporal resolution
is required to the forecasts for their use in urban areas [9-11], the probabilistic
nowcasts have been set up to be produced at 1 km resolution and 5 min intervals. The
forecasting chain is completed by a hydrodynamic model of the urban drainage
network.
The aim of this work is to discuss the implementation of this probabilistic system, which
takes into account the radar error to characterize the forecast uncertainty, with consequent
potential benefits in the management of urban systems. It will also allow a comparison
with previous findings related to the analysis of different approaches to uncertainty
estimation and quantification in terms of rainfall [12] and flows at the urban scale
[13].
Acknowledgements
The authors would like to acknowledge the BADC, the UK Met Office and Dr. Alan Seed
from the Australian Bureau of Meteorology for providing the radar data and the nowcasting
model.
The authors acknowledge the support from the Engineering and Physical Sciences
Research Council (EPSRC) via grant EP/I012222/1.
References
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radar nowcasts and MM5 forecasts. Hydrological Processes, accepted article.
DOI:Â 10.1002/hyp.8415
[13] Liguori S, Rico-Ramirez MA, Schellart ANA, Saul AJ, 2012. Using probabilistic
radar rainfall nowcasts and NWP forecasts for flow prediction in urban catchments.
Atmospheric Research 103: 80–95. |
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