Recent updates to the Met Office IMPROVER post-processing system

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. Post-processed MOGREPS-UK model forecasts are blended with deterministic UKV model forecasts and data from the coarser resolution global ensemble, MOGREPS-G as well as ECMWF, to produce seamless probabilistic forecasts from now out to 14 days. For precipitation, an extrapolation nowcast is also blended in at the start. Forecasts are converted to probabilities at the start, and all initial stages of post-processing are performed on gridded data, with site-specific forecasts extracted as a final step, helping to ensure consistency. Data are processed on a 10km global grid and on a 2km UK-centred grid. Physical and statistical corrections are applied to the data to ensure the probability distribution functions for each source model are sufficiently similar for blending into a seamless probabilistic forecast.

 

In this talk we present an overview of the enhancements added to the Met Office IMPROVER capability over the past year, including applications of statistical and machine learning methods and comparison with truth data to improve parameters including replacing Ensemble Model Output Statistics (EMOS) with Statistical Anomaly Model Output Statistics (SAMOS) for temperature, wind speed and wind gust. We also consider Quantile Regression Random Forests visibility, extending forecasts to 14 days using ECMWF data and working towards being able to create physically consistent realisations of precipitation parameters for hydrological models, including regridding surface snow amount to reflect a higher-resolution grid and orography.