This paper presents the general regression neural networks (GRNN)
as a nonlinear regression method for the interpolation of monthly
wind speeds in complex Alpine orography. GRNN is trained using data
coming from Swiss meteorological networks to learn the statistical
relationship between topographic features and wind speed. The terrain
convexity, slope and exposure are considered by extracting features
from the digital elevation model at different spatial scales using
specialised convolution filters. A database of gridded monthly wind
speeds is then constructed by applying GRNN in prediction mode during
the period 1968-2008. This study demonstrates that using topographic
features as inputs in GRNN significantly reduces cross-validation
errors with respect to low-dimensional models integrating only geographical
coordinates and terrain height for the interpolation of wind speed.
The spatial predictability of wind speed is found to be lower in
summer than in winter due to more complex and weaker wind-topography
relationships. The relevance of these relationships is studied using
an adaptive version of the GRNN algorithm which allows to select
the useful terrain features by eliminating the noisy ones. This research
provides a framework for extending the low-dimensional interpolation
models to high-dimensional spaces by integrating additional features
accounting for the topographic conditions at multiple spatial scales.
Copyright (c) 2012 Royal Meteorological Society.