High resolution weather prediction model
The Ministry of Earth Sciences (MoES) has commissioned a very high resolution (12 km) global deterministic weather prediction model for generating operational weather forecasts. The model has been on trial since September 2016. It has shown significant improvements in skill of daily weather forecasts. Operationalised last month (on Jan 16), it replaces the earlier version which had a horizontal resolution of 25 km. It was very helpful in predicting the track and the intensity of the recent storm Vardah and the cold wave over the northern parts of India.
The EPS is adopted to overcome the problem of uncertainties in the forecasts. It involves the generation of multiple forecasts using slightly varying initial conditions.
The EPS also helps generate probabilistic forecasts and quantify the uncertainties.
The Ministry of Earth Sciences (MoES) provides weather, climate and hydrological services to various users round-the-clock.
Both operational and research aspects for these services are implemented through its constituent units India Meteorological Department (IMD), National Centre for Medium Range Weather Forecasting (NCMRWF), Indian Institute of Tropical Meteorology (IITM) and Indian National Centre for Ocean Information System (INCOIS).
In general, during the last five years, the skill of weather and climate forecasts in India has improved.
The improvement is noted especially in general public weather forecasts, monsoon forecasts, heavy rainfall warnings and tropical cyclone warnings and alerts.
The successes in predicting the Tropical Cyclones Phailin/Hudhud, heavy rainfall event in Chennai during December 2015, deficient rainfall during monsoon season of 2015 are the best examples for the improvement in prediction capability during the recent years.
Focused research and development activities have been carried out at IITM, NCMRWF and IMD on weather prediction model development and data assimilation methods. Data from the International and Indian satellites are being assimilated in the weather prediction models.