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

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.

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Journal
Scientific Reports
Published
2025-11-12
DOI
https://doi.org/10.1038/s41598-025-23307-1
Primary Topic
Atmospheric aerosols and clouds
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Shivani Shah, Manoj K. Mishra, P. K. Thapliyal, Rashmi Sharma et al.
Scientific Reports
Atmospheric aerosols and clouds
article

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

Shivani Shah, Manoj K. Mishra, P. K. Thapliyal, Rashmi Sharma, Rajmal Jat, Bipasha Paul Shukla, Deepak Putrevu, Gaurav Rajabhau Govardhan, Prafull P. Yadav, Sachin Dinkar Ghude, Rajesh Kumar
article en

Abstract

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.

Scientific ReportsVol. 15(1)
NSF National Center for Atmospheric Research (US), Indian Space Research Organisation (IN), U.S. National Science Foundation (US), National Centre for Medium Range Weather Forecasting (IN), Indian Institute of Tropical Meteorology (IN), Ministry of Earth Sciences (IN), Savitribai Phule Pune University (IN)
National Science Foundation, National Aeronautics and Space Administration, Indian Space Research Organisation, Indian Institute of Technology Madras, Space Applications Centre
Openalex Percentile: Top 69%
Atmospheric aerosols and clouds
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