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Real-time PM 2.5 forecast over Delhi: Performance of high resolution (400 m) WRF-Chem model integrated with data assimilation and dynamical downscaling
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  • Chinmay Kumar Jena,
  • Sachin D Ghude,
  • Rajesh Kumar,
  • Sreyashi Debnath,
  • Vijay K Soni,
  • Santosh H Kulkarni,
  • G Beig,
  • Ravi S Nanjundiah,
  • M Rajeevan
Chinmay Kumar Jena
Indian Institue of Tropical Meteorology, Indian Institue of Tropical Meteorology

Corresponding Author:[email protected]

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Sachin D Ghude
Indian Institute of Tropical Meteorology, Indian Institute of Tropical Meteorology
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Rajesh Kumar
National Center for Atmospheric Research, National Center for Atmospheric Research
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Sreyashi Debnath
Indian Institute of Tropical Meteorology, Indian Institute of Tropical Meteorology
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Vijay K Soni
Indian Meteorological Department, Indian Meteorological Department
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Santosh H Kulkarni
CDAC, CDAC
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G Beig
Indian Institute of Tropical Meteorology, Indian Institute of Tropical Meteorology
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Ravi S Nanjundiah
Indian Institute of Tropical Meteorology, Indian Institute of Tropical Meteorology
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M Rajeevan
MoES, MoES
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Abstract

We present a very high-resolution (400 m) operational air quality forecasting system developed to alert citizens of Delhi and the National Capital Region (NCR) about acute air pollution episodes. Such a high-resolution system has been developed for the first time and is evaluated during October 2019-February 2020. The system assimilates near real time aerosol observations from in situ and space-borne observations in the WRF-Chem model to produce a 72-h forecast every day in a dynamical downscaling framework. The assimilation of aerosol optical depth and surface PM 2.5 observations improves the initial condition for surface PM 2.5 by about 45 µg/m 3 (about 50%). The accuracy of the forecast degrades slightly with time as mean bias increases from +2.5 µg/m 3 on the first day to-17 µg/m 3 on the third day of forecast. Our forecasts are found to be very capable both for PM 2.5 concentration and unhealthy/ very unhealthy air quality indices categories. 2