Abstract

The success of machine learning techniques over the years, and in particular neural networks, has opened up a new avenue of research for weather forecasting. However neural networks suffer as decision-making tools because they lack the ability to express uncertainty. Here we show how this problem can be alleviated by transforming continuous data to categorical data. Specifically, we use neural networks to easily generate probabilistic data-driven forecasts of geopotential at the 500hPa level and the temperature at the 850hPa level, using the WeatherBench dataset (a processed version of the ERA5 reanalysis dataset regridded onto a coarse resolution). Furthermore, by using a combination of variable importance analysis and ensemble modelling, we show that our data-driven neural network approach can achieve better results than both some more complex neural networks and some simple NWP models. However, our approach is not more accurate than the existing operational ECMWF IFS model. Therefore, in the second part of this talk, we present ongoing work illustrating how neural networks can be used for post-processing to improve predictions from NWP models. In particular, we show how the relatively new technique of Bayesian Neural Networks may help to improve ensemble generation and uncertainty quantification of NWP models.