Improving machine learning-based weather forecast 1 post-processing with
clustering and transfer learning
Abstract
Machine learning has been widely applied in numerical weather
prediction, but the incorporation of new observational sites into models
trained on stations with long historical records remains a challenge.
Here we propose a post-processing framework consisting of three machine
learning methods: station clustering with K-means, temperature
prediction based on decision trees, and transfer learning for
newly-built stations. We apply this framework to post-processing
forecasts of surface air temperature at 301 weather stations in China.
The results show significant reductions (as much as
39.4%~20.0%) in the root-mean-square error of
operational forecasts at lead times as long as 7 days. Moreover, the use
of transfer learning to incorporate new stations improves forecasts at
the new site by 36.4% after only one year of data collection. These
results demonstrate the potential for clustering and transfer learning
to boost existing applications of machine learning techniques in weather
forecasting.