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Abstract

There has been a massive up-gradation of weather forecasting capabilities in India under the modernization programme of the Government of India, which covers various components such as atmospheric observation network; strengthening of computing facilities; data integration and product generation; and dissemination of information to an optimum level. It has improved forecasting capabilities for high impact weather events like cyclones, severe thunderstorm, heavy rainfall and floods in a significant manner. IMD now has a network of automatic weather stations, Doppler Weather Radars (DWR), state-of-the-art upper air systems etc. These observations are now being used to run numerical prediction models on High Performance Computing Systems (HPCS). Global Forecast System (GFS T574/L64) was made operational at IMD New Delhi, incorporating Global Statistical Interpolation (GSI) scheme as the global data assimilation for the forecast up to seven days. Mesoscale forecast system WRF (ARW) with 3DVAR data assimilation is being operated daily twice, at 27 km, 9 km and 3 km horizontal resolutions for the forecast up to three days using initial and boundary conditions from the IMD GFS T574. At ten other regional centres, very high resolution mesoscale models (WRF at 3 km resolution) are made operational with the installation of High End Server. Doppler weather and mesoscale dynamical model-based Nowcast system was made operational for the national Capital of Delhi. Polar WRF is implemented to provide day-to-day short range (48 hours) weather forecast for the Maitri region over Antarctica. District level quantitative five days weather forecasts based on Multi-Model Ensemble (MME) system are being generated to support Agro-Meteorological Advisory Service of India. All these NWP products are routinely made available on the IMD web site www.imd.gov.in.

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Bhowmik, S.K.R. (2016). Operational Tropical Cyclone Forecasts Models at IMD and Their Performance. In: Mohanty, U.C., Gopalakrishnan, S.G. (eds) Advanced Numerical Modeling and Data Assimilation Techniques for Tropical Cyclone Prediction. Springer, Dordrecht. https://doi.org/10.5822/978-94-024-0896-6_17

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