APA
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Ghude, S., Kumar, R., Jena, C., Debnath, S., Kulkarni, R., Alessandrini, S., … Rajeevan, M. (2020). Evaluation of PM2.5 Forecast using Chemical Data Assimilation in the WRF-Chem Model: A Novel Initiative Under the Ministry of Earth Sciences Air Quality Early Warning System for Delhi, India. Current Science, 118(11), 1803–1815. https://doi.org/10.18520/cs/v118/i11/1803-1815
Chicago/Turabian
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Ghude, Sachin, Rajesh Kumar, Chinmay Jena, Sreyashi Debnath, Rachana Kulkarni, Stefano Alessandrini, Mrinal Biswas, et al. “Evaluation of PM2.5 Forecast Using Chemical Data Assimilation in the WRF-Chem Model: A Novel Initiative Under the Ministry of Earth Sciences Air Quality Early Warning System for Delhi, India.” Current Science 118, no. 11 (2020): 1803–1815.
MLA
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Ghude, Sachin, et al. “Evaluation of PM2.5 Forecast Using Chemical Data Assimilation in the WRF-Chem Model: A Novel Initiative Under the Ministry of Earth Sciences Air Quality Early Warning System for Delhi, India.” Current Science, vol. 118, no. 11, 2020, pp. 1803–15, doi:10.18520/cs/v118/i11/1803-1815.
BibTeX Click to copy
@article{sachin2020a,
title = {Evaluation of PM2.5 Forecast using Chemical Data Assimilation in the WRF-Chem Model: A Novel Initiative Under the Ministry of Earth Sciences Air Quality Early Warning System for Delhi, India},
year = {2020},
issue = {11},
journal = {Current Science},
pages = {1803-1815},
volume = {118},
doi = {10.18520/cs/v118/i11/1803-1815},
author = {Ghude, Sachin and Kumar, Rajesh and Jena, Chinmay and Debnath, Sreyashi and Kulkarni, Rachana and Alessandrini, Stefano and Biswas, Mrinal and Kulkarni, Santosh and Pithani, Prakash and Kelkar, Saurabh and Sajjan, Veeresh S. and Chate, D. and Soni, V. and Singh, Siddhartha and Nanjundiah, R. and Rajeevan, M.}
}
Air quality has become one of the most important environmental concerns for Delhi, India. In this perspective, we have developed a high-resolution air quality prediction system for Delhi based on chemical data assimilation in the chemical transport model â Weather Research and Forecasting with Chemistry (WRF-Chem). The data assimilation system was applied to improve the PM2.5 forecast via assimilation of MODIS aerosol optical depth retrievals using three dimensional variational data analysis scheme. Near real-time MODIS fire count data were applied simultaneously to adjust the fire-emission inputs of chemical species before the assimilation cycle. Carbon monoxide (CO) emissions from biomass burning, anthropogenic emissions, and CO inflow from the domain boundaries were tagged to understand the contribution of local and non-local emission sources. We achieved significant improvements for surface PM2.5 forecast with joint adjustment of initial conditions and fire emissions.
