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<p>This study investigates the impact of assimilating high resolution (770&#xa0;m) Aerosol Optical Depth (AOD) retrieval derived from the Oceansat-3 Ocean Colour Monitor (OCM) sensor into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) for the first time, aiming to improve fine particulate matter (PM₂.₅) forecasts over India. AOD assimilation, evaluated over the period 01–15 November 2023, leads to substantial improvements in model accuracy, reducing PM₂.₅ biases by 30–70% and lowering root mean square error (RMSE) across critical regions such as Delhi, Punjab, Bihar, and West Bengal. The assimilation substantially improves initial conditions of surface PM₂.₅ estimates by approximately 60&#xa0;µg/m³. Forecast accuracy is the highest on the first day, with an RMSE of 21.35&#xa0;µg/m³ and a correlation coefficient (R) of 0.75, followed by increasing RMSE values of 30.40&#xa0;µg/m³ on Day 2 and 32&#xa0;µg/m³ on Day 3, with correlations of 0.73 and 0.70, respectively, reflecting degradation of assimilation benefits by model uncertainties over time. With MODIS nearing phase-out, high-resolutionOCM AOD retrievals offer a reliable alternate choice for future AOD assimilation in the AIRWISE forecasting system over India.</p>

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Assimilation of high-resolution Ocean Color Monitor (OCM) aerosol optical depth in WRF-Chem improves PM₂.₅ forecasts over the Indian region

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月12日

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  • Prafull P. Yadav,
  • Sachin D. Ghude,
  • Rajesh Kumar,
  • Gaurav Govardhan,
  • Rajmal Jat,
  • Shivani Shah,
  • B. P. Shukla,
  • Manoj K. Mishra,
  • Deepak Putrevu,
  • P. K. Thapliyal,
  • Rashmi Sharma

摘要

This study investigates the impact of assimilating high resolution (770 m) Aerosol Optical Depth (AOD) retrieval derived from the Oceansat-3 Ocean Colour Monitor (OCM) sensor into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) for the first time, aiming to improve fine particulate matter (PM₂.₅) forecasts over India. AOD assimilation, evaluated over the period 01–15 November 2023, leads to substantial improvements in model accuracy, reducing PM₂.₅ biases by 30–70% and lowering root mean square error (RMSE) across critical regions such as Delhi, Punjab, Bihar, and West Bengal. The assimilation substantially improves initial conditions of surface PM₂.₅ estimates by approximately 60 µg/m³. Forecast accuracy is the highest on the first day, with an RMSE of 21.35 µg/m³ and a correlation coefficient (R) of 0.75, followed by increasing RMSE values of 30.40 µg/m³ on Day 2 and 32 µg/m³ on Day 3, with correlations of 0.73 and 0.70, respectively, reflecting degradation of assimilation benefits by model uncertainties over time. With MODIS nearing phase-out, high-resolutionOCM AOD retrievals offer a reliable alternate choice for future AOD assimilation in the AIRWISE forecasting system over India.