Creating a Pseudo-Deterministic Forecast for Surface Transport from an NWP Ensemble with Consistency Across Multiple Variables using KDE

In modern forecasting it is now a common technique to use an ensemble of forecasts generated by Numerical Weather Prediction (NWP) models. This necessitates a statistical approach be taken when using these weather predictions to inform decision-making and leveraging probabilities in the production of forecasts. It is often required to take the spread of predictions made by NWPs in the ensemble and reduce these to a single value, a pseudo-deterministic forecast, analogous to a forecast made be a traditional deterministic NWP, in order to allow end users to make binary decisions often defined at a definite threshold. These values may be representative of a single physical parameter modelled (e.g. road surface temperature) or may combine multiple parameters in a physically consistent manner (e.g. the road surface temperature coupled to the depth of water on the road for calculating road state), and are used by stakeholders in a number of sectors often to inform safety critical decision making. Therefore, it is important to ensure that the methodology used to reduce the ensemble of predictions to a pseudo-deterministic forecast is as accurate as possible and can retain information related to the ensemble spread , whilst ensuring consistency in parameters through the spatial and temporal domain.

The Surface Transport Forecast (STF) system produces forecasts for different transport surfaces in response to NWP outputs. The STF system is architected such that it runs simultaneously for each member of the NWP forecast ensemble, producing a corresponding ensemble of STF predictions. This enables the computation of a pseudo-deterministic forecast, which retains the maximum amount of information provided by the NWP ensemble.

To reduce the STF ensemble to a pseudo-deterministic forecast a Kernel Density Estimation (KDE) is utilised to build Probability Density Functions (PDFs), which can be readily interrogated using standard statistical techniques. It is found that pseudo-deterministic forecasts, which are consistent across a combination of physical modelled parameters, can be determined using covariant techniques, ensuring the ensemble is reduced as late as possible in the forecast production keeping the maximum benefit provided by the forecast spread. We will present the numerical and computational implementation of the described method in our STF system. Further, we will analyse the pseudo-deterministic forecasts produced and verify the validity of results at specific locations using multiple years of road observations.