Earth angular momentum forecasts are naturally accompanied by forecast
errors that typically grow with increasing forecast length. In contrast
to this behavior, we have detected large quasi-periodic deviations
between atmospheric angular momentum wind term forecasts and their
subsequently available analysis. The respective errors are not random
and have some hard to define yet clearly visible characteristics which
may help to separate them from the true forecast information. These
kinds of problems, which should be automated but involve some adaptation
and decision-making in the process, are most suitable for machine
learning methods. Consequently, we propose and apply a neural network to
the task of removing the detected artificial forecast errors. We found,
that a cascading forward neural network model performed best in this
problem. A total error reduction with respect to the unaltered forecasts
amounts to about 30% integrated over a 6 day forecast period.
Integrated over the initial 3 day forecast period, in which the largest
artificial errors are present, the improvements amount to about 50%.
After the application of the neural network, the remaining error
distribution shows the expected growth with forecast length. However, a
24 hourly modulation and an initial baseline error of
2*10-8 became evident that were hidden before under
the larger forecast error.