Application of ALADIN model outputs for solid precipitation forecasting and detection

Abstract

Meteorological services face a major challenge in providing reliable snowfall forecasts and detecting solid precipitation, as precise data is needed for weather warnings, damage assessments, hydrological modeling, and many other tasks. This study evaluates the skill of the ALADIN numerical weather prediction model in solid precipitation forecasting at 126 stations of the Czech Hydrometeorological Institute (CHMI) between August 2019 and July 2025. Model performance was assessed against quality-controlled human present weather observations using common verification metrics, including the Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), and Heidke Skill Score (HSS). Further, the ALADIN_SN weighted index of solid precipitation occurrence was assimilated from ALADIN snow forecasts in hourly intervals using two sets of empirical lead-time-dependent weights. During the snow season, ALADIN_SN showed a mean POD of 0.68, FAR of 0.48, and HSS of 0.53, with an overall accuracy exceeding 83% at all stations. Air temperature is the most important variable affecting the model performance, with better results at temperatures below approximately +2°C. The skill of ALADIN_SN also improved with increasing solid precipitation intensity and duration. Case studies demonstrated that ALADIN_SN successfully captured large-scale snowfall events associated with synoptic systems but struggled with localized or short-lived precipitation and phase transitions at temperatures above freezing, and it often overestimated the spatial extent of solid precipitation zones. Overall, the ALADIN_SN parameter provides a spatially consistent, computationally efficient, and operationally feasible method for identifying solid precipitation occurrence, suitable for supplementing networks of automated measurement systems and human weather observers.