Nonlinear Wave Ensemble Averaging using Neural Networks
Ricardo Campos
UMD/AOSC
20 Feb, Noon, in 2155
Abstract:
This lecture presents results of a GWES assessment using NDBC buoys,
studying the errors of 10-m wind speed (U10m), significant wave height
(Hs), and peak period (Tp), in function of forecast range and severity
(percentiles). Then it focuses on a large experiment using neural
networks (NN) applied to nonlinear ensemble averages. First, using a
single location approach, considering two buoys in the Pacific and the
Atlantic Ocean. Then moving to a spatial approach at the Gulf of
Mexico. The NN simulates the residue of the ensemble mean, i.e., the
difference from the arithmetic mean of the ensemble members to the buoy
observations. The sensitivity NN test considered a total of 12
different numbers of neurons, 8 different filtering windows (residue),
and 100 seeds for the random initialization. Independent NN models have
been constructed for specific forecast days, from Day 0 to Day 10.
Results show that a small number of neurons are sufficient to reduce
the bias, while 35 to 50 neurons are optimum to reduce both the scatter
and average errors. More complex NN models with a higher number of
neurons presented worse results. Finally, a comparison showed
significant improvements of the best neural network models (NNs)
compared to the traditional arithmetic ensemble mean (EM). The
correlation coefficient for forecast Day 10, for example, was increased
from 0.39 to 0.61 for U10m, from 0.50 to 0.76 for Hs, and from 0.38 to
0.63 for Tp.