AIR QUALITY EARLY WARNING SYSTEM FOR DELHI
MINISTRY OF EARTH SCIENCES, GOVT. OF INDIA
पृथ्वी विज्ञान मंत्रालय, भारत सरकार
(Project By : Indian Institute of Tropical Meteorology, Pune)
Air Quality Forecast(AQ-EWS)
The air pollution system has been developed jointly by the scientists at the Indian Institute of Tropical Meteorology (IITM), Pune, India Meteorological Department, National Centre for Medium-Range Weather Forecasting (NCMRWF) and National Center for Atmospheric Research (NCAR), Boulder, USA.
The warning system consists of
a)Real-time observations of air quality over Delhi region and details about natural aerosols like dust (from dust storms) and particulate matter using different satellite data sets.
b) Predictions of air pollutants from two different air quality prediction systems based on state-of-the-art atmospheric chemistry transport models and
Warning Messages and Alerts and Bulletins.
The modeling framework typically consists of a high-resolution weather prediction model with an atmospheric chemistry transport model. Both the models have data assimilation facility, which can assimilate data from satellites on dust aerosols, particulate matter from stubble burning and other air pollutants like SO2 and NO2. The models will take into account the background aerosols and pollutants, long-range transport of dust from dust storms and particulate matter from stubble burning. The predictions are now available up to 72 hours lead time.
The website will be accessed by the officials of Environmental Pollution Authority (EPA) and the Central Pollution Control Board (CPCB) for taking necessary steps depending upon the requirements. The new early warning system is meant to issue alerts on large scale air pollution events that may occur over the Delhi region. Station specific forecast will be continued at SAFAR web site.
MoES will be making further attempts to assimilate more data of other pollutants and also to improve the accuracy of predictions.
Winter Fog Experiment (WiFEx)
Fog is a visible mass consisting of cloud water droplets suspended in the air or near the Earth’s surface. The presence of heavy and extended period fog in the northern regions of India is one of the major weather hazards, impacting aviation, road transportation, economy and public life in the world’s most densely populated region. Maximum fog occurrence over Northwest India is about 48 days (visibility < 1000m) per year and occurs mostly during the December-February time period. All India's annual morning poor visibility days (PVD <4 km) have increased from 6.7 to 27.3 % days. Recent studies on fog in India during the past 10-15 years have prompted significant socio-economic concern due to an increase in frequency, persistence, and intensity of fog occurrence over the northern parts of the country. Land-use changes and increasing pollution in the region are responsible for growing Fog occurrence.
The objectives of the Winter Fog Experiment (WiFEx) are to develop better now-casting (next 6 hours) and forecasting of winter fog on various time and spatial scales and help reduce its adverse impact on aviation, transportation and economy, and loss of human life due to accidents. Presently, the airport fog forecast system at real-time is based upon mainly meteorological parameters alone covering synoptic- Climatological checklist and empirical methods. We need a reliable Dynamical based fog forecasting system for Fog occurrence by incorporating all fog formation parameters covering meteorological, fog Micro-physics, radiational, thermodynamical and other boundary layer processes. For attempting that, the physical and chemical characteristics of fog along with its microphysical processes responsible for its genesis, sustenance, intensity, and dissipation has to be studied. Improved understanding on the above aspects is required to develop reliable forecasting models and observational techniques for accurate prediction of Fog events.
In an effort to gain insight into these questions, the Ministry of Earth Sciences (MoES), Government of India has taken up a multi-institutional initiative to conduct an intensive ground-based measurement campaign at the Indira Gandhi International Airport (IGIA), Delhi, to understand different physical and chemical features of Fog and factors responsible for its genesis, intensity and duration. WiFEx was conducted in a pilot mode at IGIA during last winter and will be continued from December 2016 till February 2017. The main scientific objective of this project is to study the characteristics and variability of fog events and associated dynamics, thermodynamics and fog microphysics, with the aim to achieve a better understanding of fog life cycle and ultimately improve capability in fog prediction.
Extensive sets of comprehensive ground-based instrumentation, including remote sensing platforms, are deployed at the Indira Gandhi International Airport (IGIA), New Delhi. Major in-situ sensors are deployed to measure surface micrometeorological conditions, radiation balance, turbulence, the thermo-dynamical structure of the surface layer, fog droplet and aerosol microphysics, aerosol optical properties, real-time sky images, and aerosol and fog water chemistry to describe the complete environmental conditions in which fog develops. These measurements will form the basis for understanding some of the key questions on fog formation and dispersion. With these measurements, modeling efforts also will be made with the ultimate aim to improve the prediction skill. These observations from the intense campaign will be further used to validate model forecasts and to improve model capability. It is proposed to introduce this model for operational forecasts of Fog for the winter season of 2017-18.
In addition to Indian Institute of Tropical Meteorology (IITM), Pune, India Meteorology Department (IMD), National Center for Medium-Range Weather Forecast (NCMRWF), Airport Authority of India, GMR, Indira Gandhi International Airport and Indian Institute of Science Education and Research (IISER) Mohali are also participating in this observational campaign.