Accurate representation of CO over India remains challenging because of large uncertainties in emissions, atmospheric transport, and sparse in‑situ measurements. This study examines the impact of assimilating MOPITT total column CO retrievals into the Weather Research and Forecasting model coupled with Chemistry (WRF‑Chem) using the Gridpoint Statistical Interpolation (GSI) system for October-December 2019, a period strongly influenced by post‑monsoon crop‑residue burning and stagnant meteorology over northern India. Model performance is evaluated against independent TROPOMI CO columns and MOPITT vertical profiles. The control simulation (WRF‑CNTL) shows a persistent positive bias, overestimating TROPOMI CO by about 0.4-0.6 × 10¹⁸ molecules cm⁻² across the Indo‑Gangetic Plain. Assimilation (WRF‑DA) markedly reduces these biases and better captures observed spatial and temporal variability. Normalized mean bias decreases from 33.5% to 14.4%, while the index of agreement increases from 0.45 in the control to 0.72 in the data assimilation run. Comparisons with MOPITT profiles indicate that assimilation lowers near‑surface CO by 40-100 ppbv and reduces profile errors by 40-60% in the lower troposphere. These results show that MOPITT CO assimilation effectively constrains regional CO distributions and substantially enhances WRF‑Chem performance over India.
Yadav et al. (Wed,) studied this question.