Browsing by Author "V. Abhijith"

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Assessment of bias correction technique to improve ozone reanalysis dataset over India
(Springer Nature, 2025) Tanu Gangwar; Anumeha Dube; V. Abhijith; Sunita Verma
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. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.
Assessment of extreme rainfall events over Kerala using EVA and NCUM-G model forecasts
(Springer, 2023) V. Abhijith; Raghavendra Ashrit; Anumeha Dube; Sunita Verma
Assessment of extreme rainfall events (ERE) is crucial for disaster management. Numerical weather prediction (NWP) model-based predictions often fail to predict the extremes. This could be due to several reasons, including insufficient model resolution to capture the sub-grid scale processes, inadequate high-quality observational data for assimilation, uncertainty in initial conditions and approximations in model physics. Estimation of rainfall for different return periods (RP) using extreme value analysis (EVA) can aid in better decision-making. RP of an event indicates its probability and rarity over the region. The current study shows how EVA can be used to supplement model predictions. This study uses the high-resolution (0.25×0.25) gridded observed rainfall data from India Meteorological Department (IMD), which has been available for 117 years (1901–2017). The generalised extreme value (GEV) distribution is applied with suitable goodness-of-fit tests. Rainfall amounts corresponding to 100-year RP are estimated using EVA over the entire data period (1901–2017) and three epochs (1901–1940, 1941–1980, and 1981–2017). The results indicate increasing rainfall amounts corresponding to 100-year RP. Similarly, rainfall amounts for 25, 50, 100, and 200-year RP over Kerala are computed to compare with the extremely heavy rainfall (≤21 cm/day) amounts reported during JJAS 2018 and 2019. Further, this approach supplements the operational forecasts of NCUM-G model forecasts. © 2023, Indian Academy of Sciences.