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<p>This study provides the first systematic evaluation of three global reanalysis ozone products MERRA-2, CAMS and ERA5 against quality-controlled CPCB ground observations over five climatological regions of India. This study has two primary objectives: (1) to document the performance of these datasets across India’s geographically, climatologically and demographically diverse regions; and (2) to evaluate the effectiveness of various bias-correction methods in improving their agreement with observations. Observed daily mean ozone concentrations ranged from 6.7 to 57.6&#xa0;µg/m<sup>3</sup>; all three-reanalysis exhibited regionally coherent biases, with CAMS most prone to overestimation (mean bias: 42.3 to 108.1&#xa0;µg/m<sup>3</sup>). Spatial patterns of bias varied by region, with the largest positive departures over the Indo Gangetic Plains (IGP), Western India (WI), Himalayan Region (HR), Central India (CI) and Southern India (SI). Verification metrics like RMSE, MAE, correlation coefficient (r), index of agreement (d) is used to analyse the strengths and weaknesses of each dataset in capturing ozone variability over these regions. To enhance the dataset accuracy bias correction techniques, including Quantile–Quantile (QQ) mapping, Ratio Adjustment Transformation (RAT-add and RAT-multi), and Variance Scaling (Vari), were applied. The RAT-multi method emerged as the most effective, substantially reducing F-Bias, RMSE, and MAE while improving correlation (r) and Index of agreement (d). Notable improvements were observed in CI and IGP, where Corrected MERRA-2 achieved an RMSE of 17.164&#xa0;µg/m<sup>3</sup> and F-Bias ~ 1. In the IGP region, the CAMS ozone dataset was corrected using the RAT-multi method showed statistically significant performance, by achieving improvement of 75.647%. This was followed by WI (72.080%), SI (69.358%), HR (67.313%), and CI showed the least improvement with 65.257%. Challenges persisted in the Himalayan Region due to its complex topography. This study establishes a benchmark for bias correction of reanalysis datasets over India, with corrected CAMS using RAT-multi outperforming others. This study underscores the importance of post-processing reanalysis data to address biases arising from limitations in model physics and parametrization, thereby improving its applicability for regional air quality assessments.</p>

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Assessment of bias correction technique to improve ozone reanalysis dataset over India

  • 期刊论文

  • 发表日期：

    2025年7月4日

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  • Tanu Gangwar,
  • Anumeha Dube,
  • Abhijith V,
  • Sunita Verma

摘要

This study provides the first systematic evaluation of three global reanalysis ozone products MERRA-2, CAMS and ERA5 against quality-controlled CPCB ground observations over five climatological regions of India. This study has two primary objectives: (1) to document the performance of these datasets across India’s geographically, climatologically and demographically diverse regions; and (2) to evaluate the effectiveness of various bias-correction methods in improving their agreement with observations. Observed daily mean ozone concentrations ranged from 6.7 to 57.6 µg/m3; all three-reanalysis exhibited regionally coherent biases, with CAMS most prone to overestimation (mean bias: 42.3 to 108.1 µg/m3). Spatial patterns of bias varied by region, with the largest positive departures over the Indo Gangetic Plains (IGP), Western India (WI), Himalayan Region (HR), Central India (CI) and Southern India (SI). Verification metrics like RMSE, MAE, correlation coefficient (r), index of agreement (d) is used to analyse the strengths and weaknesses of each dataset in capturing ozone variability over these regions. To enhance the dataset accuracy bias correction techniques, including Quantile–Quantile (QQ) mapping, Ratio Adjustment Transformation (RAT-add and RAT-multi), and Variance Scaling (Vari), were applied. The RAT-multi method emerged as the most effective, substantially reducing F-Bias, RMSE, and MAE while improving correlation (r) and Index of agreement (d). Notable improvements were observed in CI and IGP, where Corrected MERRA-2 achieved an RMSE of 17.164 µg/m3 and F-Bias ~ 1. In the IGP region, the CAMS ozone dataset was corrected using the RAT-multi method showed statistically significant performance, by achieving improvement of 75.647%. This was followed by WI (72.080%), SI (69.358%), HR (67.313%), and CI showed the least improvement with 65.257%. Challenges persisted in the Himalayan Region due to its complex topography. This study establishes a benchmark for bias correction of reanalysis datasets over India, with corrected CAMS using RAT-multi outperforming others. This study underscores the importance of post-processing reanalysis data to address biases arising from limitations in model physics and parametrization, thereby improving its applicability for regional air quality assessments.