Improving aerosol–radiation interactions in the operational forecasting system – AIRWISE

Abstract

Accurate representation of aerosol optical properties remains a key uncertainty in aerosol–radiation interactions (ARIs) in numerical weather prediction models. Operational air pollution forecasting systems use globally prescribed complex refractive indices (RIs) that inadequately represent regional aerosol composition, inducing biases in ARIs and boundary layer evolution. In this study, updated and more realistic RIs of aerosols over Delhi are implemented within the Air Quality Warning and Integrated Decision Support System for Emissions (AIRWISE) to quantify their radiative and meteorological impacts during the October 2023–January 2024 season. Sensitivity experiments with RIs of different aerosol species indicate reduction in downwelling shortwave radiation at the surface (SWDOWN) by up to ∼80 W m −2 (diurnally ∼43 W m −2 ) during a severe post-monsoon episode. This radiative perturbation decreases surface temperature (∼0.2 °C), near-surface wind speed (∼0.4 m s −1 ), and boundary layer height (∼200 m), while increasing daytime humidity (3 %–4 %). Comparable sensitivity is observed during an extreme winter episode under stagnant, humid conditions favorable for haze persistence. Seasonally, monthly mean SWDOWN decreases by 25–37 W m −2 relative to the control simulation, accounting for ∼1/4 to 1/3 of the total ARIs in clear sky conditions. Evaluation against surface radiation measurements from Winter Fog Experiment (WiFEX 2023–2024) at Indira Gandhi International Airport, Delhi shows substantial bias reduction in December 2023 (62 %) and January 2024 (35 %). The revised radiative forcing systematically modifies near-surface thermodynamics and increases PM 2.5 concentrations, thereby altering pollution–meteorology feedback in highly polluted urban environments.