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Statistical Downscaling of Gusts During Extreme European Winter Storms Using Radial-Basis-Function Networks
Abstract
Winterstorms and related gusts can cause extensive socio-economic
damages. Knowledge about the occurrence and the small scale structure of
such events may help to make regional estimations of storm losses. For a
high spatial and temporal representation, the use of dynamical
downscaling methods (RCM) is a cost-intensive and time-consuming option
and therefore only applicable for a limited number of events. The
current study explores a methodology to provide a statistical
downscaling, which offers small scale structured gust fields from an
extended large scale structured eventset. Radial-basis-function (RBF)
networks in combination with bidirectional Kohonen (BDK) maps are used
to generate the gustfields on a spatial resolution of 7 km from the
6-hourly mean sea level pressure field from ECMWF reanalysis data. BDK
maps are a kind of neural network which handles supervised
classification problems. In this study they are used to provide
prototypes for the RBF network and give a first order approximation for
the output data. A further interpolation is done by the RBF network.
For the training process the 50 most extreme storm events over the North
Atlantic area from 1957 to 2011 are used, which have been selected from
ECMWF reanalysis datasets ERA40 and ERA-Interim by an objective wind
based tracking algorithm. These events were downscaled dynamically by
application of the DWD model chain GME → COSMO-EU. Different model
parameters and their influence on the quality of the generated
high-resolution gustfields are studied. It is shown that the statistical
RBF network approach delivers reasonable results in modeling the
regional gust fields for untrained events.
One of the disadvantages of using Artificial Neural Networks (ANNs) is their significant training time need, which scales with the complexity of the network and with the complexity of the problem that is needed to be solved. Radial Basis Function Neural Networks (RBFNNs) are neural networks that use the linear combination of radial basis functions, utilizing hybrid learning procedures which can solve the time requirement problem efficiently. However, it is not trivial to determine their structural parameters, such as the number of neurons as well as the parameters of each neuron. To solve that problem we have developed a new training method: we apply a clustering step to the training data, which results in information both about the quasi-optimum number of necessary neurons in the model and the approximate parameters of the neurons.
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