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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


Affiliations
1 Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India
2 National Center for Atmospheric Research, Boulder, CO 80301, United States
3 Centre for Development of Advanced Computing, Pune 411 008, India
4 India Meteorological Department, Ministry of Earth Sciences, New Delhi 110 003, India
5 Ministry of Earth Sciences, Government of India, New Delhi 110 003, India
 

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 – 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 threedimensional 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.

Keywords

Air Quality, Particulate Matter, Chemical Data Assimilation, Aerosol Optical Depth, Fire Emissions.
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  • 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

Abstract Views: 282  |  PDF Views: 134

Authors

Sachin D. Ghude
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India
Rajesh Kumar
National Center for Atmospheric Research, Boulder, CO 80301, United States
Chinmay Jena
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India
Sreyashi Debnath
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India
Rachana G. Kulkarni
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India
Stefano Alessandrini
National Center for Atmospheric Research, Boulder, CO 80301, United States
Mrinal Biswas
National Center for Atmospheric Research, Boulder, CO 80301, United States
Santosh Kulkrani
Centre for Development of Advanced Computing, Pune 411 008, India
Prakash Pithani
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India
Saurab Kelkar
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India
Veeresh Sajjan
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India
D. M. Chate
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India
V. K. Soni
India Meteorological Department, Ministry of Earth Sciences, New Delhi 110 003, India
Siddhartha Singh
India Meteorological Department, Ministry of Earth Sciences, New Delhi 110 003, India
Ravi S. Nanjundiah
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India
M. Rajeevan
Ministry of Earth Sciences, Government of India, New Delhi 110 003, India

Abstract


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 – 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 threedimensional 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.

Keywords


Air Quality, Particulate Matter, Chemical Data Assimilation, Aerosol Optical Depth, Fire Emissions.

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DOI: https://doi.org/10.18520/cs%2Fv118%2Fi11%2F1803-1815