TITLE:
On the Downscaling of Meteorological Fields Using Recurrent Networks for Modelling the Water Balance in a Meso-Scale Catchment Area of Saxony, Germany
AUTHORS:
Rico Kronenberg, Klemens Barfus, Johannes Franke, Christian Bernhofer
KEYWORDS:
Downscaling; Recurrent Neural Networks; NARX; WaSim-ETH; Water Balance; ERA-40 Re-Analysis
JOURNAL NAME:
Atmospheric and Climate Sciences,
Vol.3 No.4,
October
15,
2013
ABSTRACT:
In this study, recurrent networks to downscale
meteorological fields of the ERA-40 re-analysis dataset with focus on the
meso-scale water balance were investigated. Therefore two types of recurrent
neural networks were used. The first approach is a coupling between a recurrent
neural network and a distributed watershed model and the second a nonlinear
autoregressive with exogenous inputs (NARX) network, which directly predicted
the component of the water balance. The approaches were deployed for a
meso-scale catchment area in the Free State of Saxony, Germany. The results
show that the coupled approach did not perform as well as the NARX network. But
the meteorological output of the coupled approach already reaches an adequate
quality. However the coupled model generates as input for the watershed model insufficient
daily precipitation sums and not enough wet days were predicted. Hence the
long-term annual cycle of the water balance could not be preserved with
acceptable quality in contrary to the NARX approach. The residual storage
change term indicates physical restrictions of the plausibility of the neural
networks, whereas the physically based correlations amongthe components of
the water balance were preserved more accurately by the coupled approach.