Comparative assessment of stability indices from NWP models and satellite nowcasting

Accurate forecasting of atmospheric stability is critical for predicting convection and associated severe weather hazards, including thunderstorms, hail, and damaging wind gusts. Atmospheric stability indices (SIs) are routinely used to assess convective potential, yet their reliability across different data sources remains incompletely understood. This study evaluates the forecast skill of stability indices derived from two operational numerical weather prediction (NWP) models: the Integrated Forecast System (IFS, ECMWF) and the Global Forecast System (GFS, NCEP) alongside satellite-derived estimates from the NOAA-LAP product, using high-resolution Global Climate Observing System Reference Upper Air Network (GRUAN) radiosonde observations at Lamont (USA) as reference truth. The analysis focuses on five commonly employed indices: K-Index, Total Totals, Lifted Index, Convective Available Potential Energy (CAPE), and Precipitable Water. Results demonstrate that forecast accuracy varies significantly across indices. Precipitable Water exhibits the highest correlation and lowest normalized RMSE, whereas CAPE proves most challenging to predict, with substantially higher errors across all data sources. Among the three products, IFS consistently delivers superior performance, achieving the highest correlation coefficients and lowest errors for nearly all indices. Notably, the satellite-based NOAA-LAP nowcasting product, despite its higher temporal refresh rate and spatial resolution, does not surpass the hourly forecasts from the global NWP models. This underscores the intrinsic uncertainty in estimating atmospheric stability from either approach. The IFS advantage is likely attributable to its finer vertical resolution (137 hybrid levels versus 20 standard pressure levels in GFS), which better captures the thermodynamic structure essential for parcel-based indices. These findings have important implications for severe weather forecasting and early warning systems. While stability indices remain valuable operational tools, their limitations in predicting convection suggest that reliance on conventional scalar metrics alone is insufficient. Future work should explore richer representations of the atmospheric column or probabilistic frameworks that preserve more thermodynamic information for convective onset prediction.