Clustering numerical weather forecasts to obtain statistical prediction intervals

The numerical weather prediction (NWP) model outputs are point deterministic values arranged on a three-dimensional grid. However, there is always some level of uncertainty in the prediction. Many applications would benefit from provision of relevant uncertainty information along with the forecast. A common means of formulating and communicating forecast uncertainty are prediction intervals (PI). In this study, various methods for modelling the uncertainty of NWP forecasts are investigated and PIs provided for predictions accordingly. In particular, the interest is in analysing the historical performance of the system as a valuable source of information for uncertainty analysis. Various clustering algorithms are employed to group the performance records as the first step of the PI determination process. In the second step, a range of methods are used to fit appropriate probability distributions to errors of each cluster. As a result, PIs can be computed dynamically depending on the forecast context. The clustering algorithms are applied over different feature sets and derived and generated features. All presented PI computation methods are empirically evaluated using a comprehensive verification framework in a set of experiments involving two real-world data sets of NWP forecasts and observations. The proposed evaluation provides a considerably fairer and more reliable judgement compared to existing methods. Results show that incorporating trained uncertainty model outputs into the NWP point predictions provides PI forecasts with higher reliability and skill. This can lead to improvement of decision processes for many applications that rely on these forecasts.