Development of a Diagnostic Algorithm for Detecting Freezing Precipitation from ERA5 Dataset: An Adjustment to the Far East

Abstract

Freezing precipitation and the resultant ice glaze can have catastrophic impacts on urban infrastructure, the environment, forests, and various industries, including transportation, energy, and agriculture. In this study, we develop and evaluate regional algorithms for detecting freezing precipitations in the Far East, utilizing the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts, along with standard meteorological observations for 20 cold seasons (September–May) from 2004 to 2024. We propose modified diagnostic algorithms based on vertical atmospheric temperature and humidity profiles, as well as near-surface characteristics. Additionally, we apply a majority voting ensemble (MVE) technique to integrate outputs from multiple algorithms, thereby enhancing classification accuracy. Evaluation of detection skills shows significant improvements over the original method developed at the Finnish Meteorological Institute and the ERA5 precipitation-type product. The MVE-based method demonstrates optimal verification statistics. Furthermore, the modified algorithms validly reproduce the spatially averaged inter-annual variability of freezing precipitation activity in both continental (mean correlation of 0.93) and island (correlation of 0.54) regions. Overall, our findings offer a more effective and valuable tool for operational activities and climatological assessments in the Far East.