Air quality is a strong health driver, its accurate assessment and forecast are important in densely populated megacities to take preventive steps. We describe the first Indian operational air quality framework, SAFAR (System of Air Quality and Weather Forecasting And Research), meant for decision-makers and a research tool with a capability of three days advance forecast in four Indian megacities of distinct environment and topography. The framework includes six different components from observations and modelling to outreach. To evaluate the performance of the forecast, we focus on particulate pollutants which largely define air quality of Indian metropolis. The model prediction skill is tested for the pilot year 2019-20 which is found to be reasonable. The Normalized Gross error of PM_2.5 for Delhi is found to be highest (35%) whereas for other cities it is ∼13–20%. The Model Output Statistics (MOS) application enhanced operational forecast ability of numerical model which resulted in improving the accuracy for specific seasons (winter).

空气质量是一个强大的健康驱动因素，其准确的评估和预测对于人口稠密的特大城市采取预防措施非常重要。我们描述了第一个印度业务空气质量框架，SAFAR（空气质量和天气预报与研究系统），旨在为决策者提供一个研究工具，它具有对印度四个不同环境和地形的特大城市进行三天提前预报的能力。该框架包括六个不同的组成部分，从观察和建模到外展。为了评估预测的性能，我们关注主要定义印度大都市空气质量的颗粒污染物。模型预测技能在2019-20试点年度进行了测试，发现是合理的。PM _2.5的归一化总误差发现德里最高（35%），而其他城市则为 13-20%。模型输出统计（MOS）应用增强了数值模型的业务预测能力，从而提高了特定季节（冬季）的准确性。