Finding more skill in UK temperature forecasts

IMPROVER (Integrated Model Post-Processing and Verification) has been developed by the Met Office as an open-source probability-based post-processing system to fully exploit our convection permitting, hourly cycling ensemble forecasts. This toolbox of post-processing steps is used to produce the Met Office’s operational forecasts for the public.

Screen temperature is a key diagnostic for public weather and one that high resolution numerical weather prediction models are good at forecasting. However, forecasts on a km-scale grid are still unable to represent the variation of temperature across specific sites, for example those in valleys unresolved by the model orography. The production of site-specific forecasts therefore requires the addition of sub-grid detail which may be added through physical corrections and / or using statistical methods.

In operational Met Office site-specific temperature forecasts IMPROVER is used to apply a temperature lapse rate adjustment to account for site displacement from the grid cell average altitude. In addition, the forecasts are calibrated using Ensemble Model Output Statistics (EMOS) to remove model biases and adjust the spread of our probabilistic forecasts. EMOS coefficients are calculated by pooling all sites together, allowing the application of calibration to both observed and unobserved sites. The resulting forecasts are more accurate than the unadjusted gridded forecasts at these locations, but there remains scope for improvement, particularly at more challenging sites.

In this talk I will present the results of applying a whole host of calibration techniques available within IMPROVER to screen temperatures across the UK area. These include EMOS with differing configurations, Standardised Anomaly Model Output Statistics (SAMOS), Quantile regression Random Forests (QRF), and Reliability Calibration. Further machine learning approaches will also be discussed. A constraint on all techniques is that they must be applicable to all forecast sites, not just those returning observations. The relative performance, as well as the pros and cons of the different methods will be discussed, as will the future of our approach to site-specific screen temperature forecasts.