India's Maiden air quality forecasting framework for megacities of divergent environments: The SAFAR-project

Gufran Beig, S. K. Sahu, V. Anand, S. Bano, S. Maji, A. Rathod, N. Korhale, S. B. Sobhana, N. Parkhi, P. Mangaraj, R. Srinivas, S. K. Peshin, S. Singh, R. Shinde, H. K. Trimbake

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

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 PM2.5 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).

Original languageEnglish
Article number105204
JournalEnvironmental Modelling and Software
Volume145
Early online date15 Sep 2021
DOIs
Publication statusPublished - Nov 2021

Keywords

  • Air quality
  • Environment
  • Forecasting model
  • Megacities
  • Meteorology
  • Particulate matters
  • SAFAR
  • Topography and health

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