Ashwin Vijay Jadhav

Aerosols modulate the radiation budget of the Earth by absorbing and scattering the incoming as well as outgoing radiative fluxes, which result in perturbation of the atmospheric state variables like temperature, pressure, and humidity levels. Inaccurate prescription of the aerosol’s refractive indices in numerical models can lead to unrealistic aerosol-radiation interactions and thus biases in the computed radiative fluxes and consequent meteorological parameters in the model simulations. In this study, the prescribed refractive indices of different aerosol species like black carbon, organic carbon, and mineral dust, in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model have been revised to the latest and more accurate observationally constrained estimates over the Indian region. The resultant simulated clear-sky downwelling shortwave radiation (SWDOWN) shows the largest improvement in mean bias error over the Indo-Gangetic Plain and Eastern regions (~60%), particularly at Bhubaneswar (~94%), followed by Southern India (~47%), and Central and Desert regions (~26%) on an annual scale. Seasonally, SWDOWN mean bias in clear-sky is reduced by ~10–20% across most of the stations. The improved radiative fluxes produce systematic meteorological adjustments, including surface cooling (~−0.2 to −1.5 °C) and increased relative humidity (~3–15%), with the strongest response during the monsoon over Central India. These changes suppress boundary layer development, leading to reduced ventilation and enhanced near-surface PM2.5 in polluted regions, especially during post-monsoon and winter. The revised refractive indices of aerosols also increase the aerosol optical depth by 0.05–0.1 across the domain. The study highlights the significance of realistic aerosol optical properties in chemistry transport models over the Indian region to improve aerosol–radiation–meteorology coupling for more accurate prediction of extreme events.

Surface solar radiation (SSR) is a critical component of the Earth’s energy balance and plays a pivotal role in climate modelling, hydrological processes, and solar energy planning. In data-scarce regions like India, where dense ground-based radiation networks are limited, reanalysis and satellite-derived SSR datasets are often utilized to fill observational gaps. However, these datasets are subject to systematic biases, particularly under diverse sky and seasonal conditions. This study presents a comprehensive evaluation of four widely used SSR datasets: ERA5, IMDAA, MERRA2, and CERES, against high-quality in situ observations from 27 India Meteorological Department (IMD) stations, for the period 1985–2020. The assessment incorporates multi-scale temporal analysis (daily/monthly), spatial validation, and sky-condition stratification via the clearness index (K_t). The results indicate that CERES exhibits the best overall performance with the lowest RMSE (16.30 W/m²), minimal bias (–2.5%), and strong correlation (r = 0.97; p = 0.01), particularly under partly cloudy conditions. ERA5, with a finer spatial resolution, also performs robustly (RMSE = 20.80 W/m²; MBE = –0.8%; r = 0.94; p = 0.01), showing consistent agreement with observed seasonal cycles, though slightly underestimating SSR during monsoonal cloud cover. MERRA2 shows moderate overestimation (+4.4%) with region-specific bias variability, while IMDAA demonstrates persistent overestimation (+10.2%) across all conditions, highlighting limited sensitivity to atmospheric transparency. Importantly, this study reconciles apparent contradictions between monthly and sky condition-based bias analyses, attributing them to aggregation differences. While reanalysis datasets overestimate SSR during the monsoon on average, they tend to underestimate it under heavily overcast conditions. These insights are critical for guiding the selection and application of SSR datasets in solar energy modelling, SPV system design, and climate diagnostics across India’s heterogeneous atmospheric regimes.

Surface solar radiation (SSR) is a fundamental energy source for an equitable and sustainable future. Meteorology-induced variability increases uncertainty in SSR, thereby limiting its reliability due to its intermittent nature. This variability depends on several meteorological factors, including clouds, atmospheric gases, and aerosol concentrations. This research investigates the detailed impact of different levels of clouds and aerosols on SSR across India. Utilizing satellite data with reanalysis retrievals, the research covers a span of three decades (30 years), from 1993 to 2022. Aerosols contributed to an average attenuation of ~13.33% on SSR, while high, mid, and low cloud conditions showed much stronger impacts, with an attenuation of ~30.80%, ~40.10%, and ~44.30%, respectively. This study reveals an alarming pattern of increasing cloud impact (C_impact) on SSR in the recent decade, with a significant increasing rate of ~0.22% year⁻¹ for high cloud (HC_impact) and ~0.13% year⁻¹ for mid cloud (MC_impact) impact, while low cloud impact (LC_impact) showed minimal change. The trend of aerosol impact (A_impact) also showed an average increase of ~0.14% year⁻¹ across all regions. The findings underscore the imperative of considering climatic variables while studying the growing solar dimming. Our findings also will assist policymakers and planners in better evaluating the solar energy resources across India.