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Sajjan, Veeresh
- 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
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PDF Views:74
Authors
Sachin D. Ghude
1,
Rajesh Kumar
2,
Chinmay Jena
1,
Sreyashi Debnath
1,
Rachana G. Kulkarni
1,
Stefano Alessandrini
2,
Mrinal Biswas
2,
Santosh Kulkrani
3,
Prakash Pithani
1,
Saurab Kelkar
1,
Veeresh Sajjan
1,
D. M. Chate
1,
V. K. Soni
4,
Siddhartha Singh
4,
Ravi S. Nanjundiah
1,
M. Rajeevan
5
Affiliations
1 Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, IN
2 National Center for Atmospheric Research, Boulder, CO 80301, US
3 Centre for Development of Advanced Computing, Pune 411 008, IN
4 India Meteorological Department, Ministry of Earth Sciences, New Delhi 110 003, IN
5 Ministry of Earth Sciences, Government of India, New Delhi 110 003, IN
1 Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, IN
2 National Center for Atmospheric Research, Boulder, CO 80301, US
3 Centre for Development of Advanced Computing, Pune 411 008, IN
4 India Meteorological Department, Ministry of Earth Sciences, New Delhi 110 003, IN
5 Ministry of Earth Sciences, Government of India, New Delhi 110 003, IN
Source
Current Science, Vol 118, No 11 (2020), Pagination: 1803-1815Abstract
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.Full Text
References
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