惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

C
Check Point Blog
美团技术团队
Jina AI
Jina AI
人人都是产品经理
人人都是产品经理
The Cloudflare Blog
V
Visual Studio Blog
Google DeepMind News
Google DeepMind News
Hugging Face - Blog
Hugging Face - Blog
云风的 BLOG
云风的 BLOG
有赞技术团队
有赞技术团队
T
The Blog of Author Tim Ferriss
WordPress大学
WordPress大学
月光博客
月光博客
宝玉的分享
宝玉的分享
小众软件
小众软件
MongoDB | Blog
MongoDB | Blog
Apple Machine Learning Research
Apple Machine Learning Research
A
About on SuperTechFans
J
Java Code Geeks
博客园_首页
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
N
Netflix TechBlog - Medium
Vercel News
Vercel News
博客园 - 聂微东

Business Tech News: Latest Updates on Innovations, Startups, and Market Trends | The HinduBusinessLine

Additive steps up lubrication Tackling iron deficiency from a steel mill Geo-engineering against climate change ZincGel vs Li-ion battery Why the energy sector isn’t AI-ready yet IT services giant TCS takes an AI-led avatar IIT-M revives forgotten route to industrial wastewater treatment IIT-Kanpur-incubated start-up develops unique battery technology Two faces of water Why the made-in-India ePlane is unique Moving satellite data at laser speed Longer-lasting zinc battery How simulation tech can ready robots for the real world DAE commissions world’s first nuclear heat-based copper-chlorine hydrogen plant DAE commissions world’s first nuclear heat-based copper-chlorine hydrogen plant Subterranean forest of fungi Using sound waves to bypass charge-based circuits AI aides to decode Indian law How the US funding cut impacts cancer research The time to deploy thorium is now The protein-peptide bonds that heal IIT-Kanpur hosts India’s first DORIS beacon How plants summon help Fishing out fake news using a deep-learning neural network IIT-Madras sets up testing tank for ships, submarines Dentistry’s prehistoric drill With AI, science is borderless How ‘spent’ graphite breathes new life into fuel cell Coal gas can yield clean hydrogen at $1.25 a kg Light, compact antennas
IMD launches pilot weather forecast within 1 km radius in...
By BL New Delhi Bureau · 2026-05-12 · via Business Tech News: Latest Updates on Innovations, Startups, and Market Trends | The HinduBusinessLine
New Delhi, May 12 (ANI): Union Minister of State (Independent Charge) for Science & Technology, Earth Sciences Jitendra Singh addresses during the launch of two advanced weather forecast products developed under the Ministry of Earth Sciences (MoES), in New Delhi on Tuesday. (@DrJitendraSingh X/ANI Photo)

New Delhi, May 12 (ANI): Union Minister of State (Independent Charge) for Science & Technology, Earth Sciences Jitendra Singh addresses during the launch of two advanced weather forecast products developed under the Ministry of Earth Sciences (MoES), in New Delhi on Tuesday. (@DrJitendraSingh X/ANI Photo) | Photo Credit: ANI

India Meteorological Department (IMD) on Tuesday unveiled two forecast products – a pan-India district-level monsoon forecast 10 days in advance and for a geographical area of 1 km in Uttar Pradesh as pilot model. The weather bureau said it has great potential for the agriculture sector.

Launching the two weather forecast products, developed jointly by the IMD, Pune-based Indian Institute of Tropical Meteorology (IITM) and National Centre for Medium Range Weather Forecasting (NCMRWF), Earth Sciences Minister Jitendra Singh said that it marks a major shift from conventional weather (rainfall, temperature, fog, cold, heatwave, cloudburst, etc.) forecasting towards impact-based and decision-support forecasting. It is capable of providing precise, location-specific and actionable information to farmers, administrators, disaster managers and citizens.

Earth Science Secretary M Ravichandran said that the numerical model which IMD was using was not sufficient because various physics approximations involved. “It is not suitable to go for such a high resolution (from 12.5 km to 1 km), and also the computational requirements are very high. So, it was difficult,” he said, referring to the numerical model.

UP facilities

“With the help of IITM and NCMRWF and IMD, fusion of both numerical model and also the data-driven training model can have a better forecast at different timescales. These particular two products, using first ever Artificial Intelligence (AI) driven system, are user-driven and the agriculture ministry needed this information very badly,” said Ravichandran.

On the pilot project, he said data are most crucial and Uttar Pradesh has over 500 weather stations and 2,400 automatic rain gauges (ARGs). If other states too set up such infrastructure it is possible to cover entire country over the next 2-3 years with such highly localised forecast, Ravichandran said.

On the forecast of monsoon advance over different parts of the country, he said people can feel they are ready to receive the rainfall. “Earlier, we used to give only the onset of the monsoon in the southern tip of India (Kerala coast) and it slowly progresses in different states. Now, we are going to give a granular scale, even at the district level, when the monsoon will be on,” he said, adding for the first time IMD would be able to do this which was so far provided by some of the international agencies.

“With all the resources available, we have put in together with the pilot experiments in the monsoon weather scale. I am sure that it is an evolutionary process. Maybe next year we have more and more observations and more states to come up,” he said.

50 more Doppler radars

The minister said that India had 16-17 Doppler Weather Radars a decade ago, which has now increased to around 50, and another 50 planned under Mission Mausam. He said this expansion of observational networks, automatic weather stations, high-performance computing systems and digital dissemination platforms has substantially improved forecasting capability and early warning systems across the country.

He said the High Spatial Resolution Rainfall Forecast for Uttar Pradesh, has been developed as a pilot service to generate rainfall forecasts at 1-km spatial resolution up to 10 days in advance. The system uses advanced AI-driven downscaling techniques and integrates data from ARGs, Automatic Weather Stations (AWSs), Doppler Weather Radars and satellite-based rainfall datasets.

Published on May 12, 2026