Current Science

Open Access Open Access  Restricted Access Subscription Access

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.
User
Notifications
Font Size

  • Ghude, S. D., Kulkarni, P. S., Kulkarni, S. H., Fadnavis, S. and van der A, R. J., Temporal variation of urban NOx concentration in India during the past decade as observed from space. Int. J. Remote Sensing, 2011, 32, 849–861.

  • Liu, L. et al., A PDRMIP multimodel study on the impacts of regional aerosol forcings on global and regional precipitation. J. Clim., 2018, 31, 4429–4447.

  • Ghude, S. D. et al., Premature mortality in India due to PM2.5 and ozone exposure. Geophys. Res. Lett., 2016, 43, 4650–4658.

  • Vadrevu, K. P., Ellicott, E., Badarinath, K. V. S. and Vermote, E., MODIS derived fire characteristics and aerosol optical depth variations during the agricultural residue burning season, north India. Environ. Pollut., 2011, 159, 1560–1569.

  • Gargava, P. and Rajagopalan, V., Source apportionment studies in six Indian cities – drawing broad inferences for urban PM10 reductions. Air Qual. Atmos. Health, 2016, 9, 471–481.

  • Tiwari, S. et al., Pollution concentrations in Delhi India during winter 2015–16: A case study of an odd-even vehicle strategy. Atmos. Pollut. Res., 2018, 9, 1137–1145.

  • Krishna, R. K. et al., Surface PM2.5 estimate using satellitederived aerosol optical depth over India. Aerosol Air Qual. Res., 2019, 19, 25–37.

  • Chate, D. et al., Assessments of population exposure to environmental pollutants using air quality measurements during Commonwealth Games – 2010. Inhal. Toxicol., 2013, 25, 333– 340.

  • Beig, G. et al., Evaluating population exposure to environmental pollutants during Deepavali fireworks displays using air quality measurements of the SAFAR network. Chemosphere, 2013, 92, 116–124.

  • Parkhi, N. et al., Large inter annual variation in air quality during the annual festival ‘Diwali’ in an Indian megacity. J. Environ. Sci. (China), 2016, 43, 265–272.

  • Guttikunda, S. K. and Jawahar, P., Application of SIM – air modeling tools to assess air quality in Indian cities. Atmos. Environ., 2012, 62, 551–561.

  • Beig, G. et al., Quantifying the effect of air quality control measures during the 2010 Commonwealth Games at Delhi, India. Atmos. Environ., 2013, 80, 455–463.

  • Kumar, R., Barth, M. C., Pfister, G. G., Nair, V. S., Ghude, S. D. and Ojha, N., What controls the seasonal cycle of black carbon aerosols in India? J. Geophys. Res. Atmos., 2015, 120, 7788–7812.

  • Jena, C. et al., Inter-comparison of different NOx emission inventories and associated variation in simulated surface ozone in Indian region. Atmos. Environ., 2015, 117, 61–73.

  • Liu, Z., Liu, Q., Lin, H. C., Schwartz, C. S., Lee, Y. H. and Wang, T., Three-dimensional variational assimilation of MODIS aerosol optical depth: Implementation and application to a dust storm over East Asia. J. Geophys. Res. Atmos., 2011, 116, 1–19.

  • Li, Z. et al., A three-dimensional variational data assimilation system for multiple aerosol species with WRF/Chem and an application to PM2.5 prediction. Atmos. Chem. Phys., 2013, 13, 4265–4278.

  • Dai, T., Schutgens, N. A. J., Goto, D., Shi, G. and Nakajima, T., Improvement of aerosol optical properties modeling over Eastern Asia with MODIS AOD assimilation in a global non-hydrostatic icosahedral aerosol transport model. Environ. Pollut., 2014, 195, 319–329.

  • Peng, Z. et al., The impact of multi-species surface chemical observation assimilation on air quality forecasts in China. Atmos. Chem. Phys., 2018, 18, 17387–17404.

  • Kumar, R. et al., Toward improving short-term predictions of fine particulate matter over the United States via assimilation of satellite aerosol optical depth retrievals. J. Geophys. Res. Atmos., 2019, 124, 2753–2773.

  • Peng, Z., Liu, Z., Chen, D. and Ban, J., Improving PM2.5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter. Atmos. Chem. Phys., 2017, 17, 4837–4855.

  • Kumar, R., Naja, M., Pfister, G. G., Barth, M. C. and Brasseur, G. P., Source attribution of carbon monoxide in India and surrounding regions during wintertime. J. Geophys. Res. Atmos., 2013, 118, 1981–1995.

  • Venkataraman, C. et al., Source influence on emission pathways and ambient PM2.5 pollution over India (2015–2050). Atmos. Chem. Phys., 2018, 18, 8017–8039.

  • Guenther, A., Karl, T., Harley, P., Weidinmyer, C., Palmer, P. I. and Geron, C., Edinburgh Research Explorer Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature) and Physics Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases). Atmos. Chem. Phys., 2006, 3181–3210.

  • Emmons, L. K. et al., Description and evaluation of the model for ozone and related chemical Tracers, version 4 (MOZART-4). Geosci. Model Dev., 2010, 3, 43–67.

  • Remer, L. A. et al., The MODIS Aerosol Algorithm, Products, and Validation. J. Atmos. Sci., 2005, 62, 947.

  • Hu, M., Advanced GSI User’s Guide, 2016.

  • Granier, C. et al., Evolution of anthropogenic and biomass burning emissions of air pollutants at global and regional scales during the 1980–2010 period. Climatic Change, 2011, 109, 163–190; https://doi.org/10.1007/s10584-011-0154-1.

  • Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J. and Soja, A. J., The fire inventory from NCAR (FINN): a high-resolution global model to estimate the emissions from open burning. Geosci. Model Dev., 2011, 4, 625–641.

  • Ghude, S. D. et al., Winter fog experiment over the Indo-Gangetic plains of India. Curr. Sci., 2017, 112, 767–784.

  • Ali, K. et al., Characterization and source identification of PM2.5 and its chemical and carbonaceous constituents during Winter Fog Experiment 2015–16 at Indira Gandhi International Airport, Delhi. Sci. Total Environ., 2019, 662, 687–696.

  • Bisht, D. S. et al., Chemical characterization of aerosols at an urban site New Delhi during winter fog campaign, 2018.

  • Hakim, Z. Q. et al., Evaluation of tropospheric ozone and ozone precursors in simulations from the HTAPII and CCMI model intercomparisons and amp;ndash; a focus on the Indian Subcontinent. Atmos. Chem. Phys. Discuss., 2018, 1–36.

  • Tang, Y. et al., 3D-Var versus optimal interpolation for aerosol assimilation: a case study over the contiguous United States. Geosci. Model Dev. Discuss., 2017, 1–27.

  • Mathur, R., Yu, S., Kang, D. and Schere, K. L., Assessment of the wintertime performance of developmental particulate matter forecasts with the eta-community multiscale air quality modeling system. J. Geophys. Res., 2008, 113, D02303; doi:10.1029/2007JD008580.

  • Ansari, T. U., Ojha, N., Chandrasekar, R., Balaji, C., Singh, N. and Gunthe, S. S., Competing impact of anthropogenic emissions and meteorology on the distribution of trace gases over Indian region. J. Atmosp. Chem., 2016, 1–18; 10.1007/s10874-016-9331-y.

  • Thompson, G., Field, P. R., Rasmussen, R. M. and Hall, W. D., Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: implementation of a new snow parameterization. Mon. Weather Rev., 2008, 136, 5095– 5115.

  • Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A. and Collins, W. D., Radiative forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models. J. Geophys. Res. Atmos., 2008, 113, 2–9.

  • Janjic, Z., Nonsingular Implementation of the Mellor-Yamada Level 2.5 Scheme in the NCEP Meso model. 2002, pp. 1–61.

  • Tewari, M. et al., Implementation and verification of the unified Noah land surface model in the WRF model. In 20th Conference on Weather Analysis and Forecasting, 2004.

  • Bougeault, P. and Lacarrere, P., Parameterization of orographyinduced turbulence in a Mesobeta – Scale Model. Mon. Weather Rev., 1989.

  • Grell, G. A. and Freitas, S. R., A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmos. Chem. Phys., 2014, 14, 5233–5250.

  • Chin, M., Rood, R. B., Lin, S. J., Müller, J. F. and Thompson, A. M., Atmospheric sulfur cycle simulated in the global model GOCART: model description and global properties. J. Geophys. Res. Atmos., 2000, 105, 24671–24687.


Abstract Views: 159

PDF Views: 74




  • 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: 159  |  PDF Views: 74

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.

References





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