Improvements to the Met Office operational Visibility diagnostic using Machine Learning

Visibility forecasting is critical for aviation, transportation, and public safety, yet remains a challenging aspect of meteorology due to complex atmospheric processes and aerosol interactions. Accurate visibility prediction is essential for operational decision-making, but traditional approaches often struggle with physical realism and probabilistic reliability. 

This study addresses these challenges within the Met Office’s IMPROVER (Integrated Model post-PROcessing and VERification) framework, which provides probabilistic post-processing of Numerical Weather Prediction (NWP) output for customers including the UK Public Weather Service. Historically, visibility diagnostics in IMPROVER have been constrained by limitations in the underlying NWP model. To overcome this, two key enhancements were introduced. First, the integration of VERA (Visibility Employing Realistic Aerosols), an existing diagnostic within the Unified Model (UM), which incorporates polydisperse aerosol effects to deliver a more physically consistent representation of visibility.  
Second, building on this improved foundation, a statistical post-processing step was implemented using Quantile Regression Forests (QRF), marking the first application of machine learning within IMPROVER. QRF was chosen for its ability to capture complex, non-linear relationships and produce calibrated probabilistic forecasts. 

The primary objective was to improve forecast skill at operationally significant thresholds, particularly <7.5 km and <1 km, which are critical for aviation and road safety. Benchmarking on the EUPPBench dataset compared QRF against reliability calibration and Distribution Regression Networks (DRN). QRF demonstrated superior performance, achieving a 45% improvement in Ranked Probability Skill Score (RPSS) over the raw NWP output. Subsequent testing using Met Office data also showed significant improvement, with QRF delivering a 9% RPSS increase for thresholds <7.5 km and a 22% improvement in Continuous RPSS across all thresholds. 

This work demonstrates the value of combining physically realistic NWP diagnostics with machine learning techniques to enhance probabilistic visibility forecasts. These improvements pave the way for more reliable decision-making in sectors sensitive to visibility conditions. Putting this research into operational production as of early 2026 represents a significant step forward in the quality of our visibility forecasts.