Surface-Based Temperature Inversions Climatology Across China from Microwave Radiometer Network and Their Relationship to PM2.5 Pollution.

Abstract

Despite significant emission controls, severe PM2.5 pollution persists in China under unfavorable meteorological conditions. Surface-based inversions (SBIs) suppress vertical mixing, but their diurnal characteristics remain poorly understood due to the limited temporal resolution of conventional radiosondes. This study presents a comprehensive SBI climatology across mainland China using hourly microwave radiometer (MWR) temperature profiles from 33 stations during 2024-2025. MWR profiles were validated against radiosonde observations at 25 stations, showing strong correlations from surface to 5000 m. Results reveal pronounced seasonal and spatial variability, with winter SBI occurrence (39.2%) approximately three times summer values (12.1%). A distinct northwest-to-southeast gradient emerges, with the highest frequencies in the Tarim Basin and Hexi Corridor and lowest in subtropical South China. We further examined SBI-pollution relationships at monthly, daily, and hourly scales using Spearman regression and SHAP analysis across PM2.5 hotspot stations. Boundary-layer variables, including effective mixing layer height and SBI heat deficit, consistently occupy the top predictor tier for PM2.5 pollution. At hourly scale, effective mixing layer height ranks third among all predictors in every season. These findings establish that SBI climatology is one of the dominant meteorological controls on PM2.5 at the network scale, operating through consistent physical mechanisms across all time scales.