Shillong, March 14: The National Institute of Disaster Management (NIDM), Ministry of Home Affairs, Government of India in collaboration with the State Disaster Management Authority, Meghalaya organised a three days Training Programme on Technologies Innovations in Weather Forecasting, Early Warning and Last Mile Connectivity from 14th– 16th March 2023 at the Lecture Hall, Meghalaya Administrative Training Institute (MATI), Mawdiangdiang, Shillong.
In the inaugural session held today, Smti M. War Nongbri, Executive Director, Meghalaya State Disaster Management Authority graced the programme as the Chief Guest. Speaking at the programme Smti M. War Nongbri, said that such training is very important as of late the state has witnessed the impact of disasters on the life of the people.
The economical status of the state and the country as a whole and how disasters have affected the people in the long run. She expressed hope that the training will enable the state to combat the onslaught of any natural disaster on the availability of the technological innovations in weather forecasting and early warning systems like the other states.
Dr. Rajnish Ranjan, Senior Consultant, NIDM New Delhi presented a briefing about the programme. The aims and objectives of the training programme are to sensitize the trainees about the fundamental concept of weather forecasting, early warning and last mile connectivity.
It also aims to acquaint them about the technological innovations being used by agencies, state governments and local bodies to disseminate early warning information of inclement weather and other hydro-meteorological hazards at the community level. Enabling them to understand their roles and responsibilities in dealing with the weather information and data.
To get the trainees aware of various guidelines, Acts and provisions of the Government of India and how these provisions and advisories can be effectively utilised at the ground level. To equip them with related case study and best practices of forecasting & early warning with special reference to the Automated Weather Station network and to also make them become a master trainer in their respective domains.
