Artificial intelligence (AI) and machine learning are revolutionizing weather predictions in India, according to Mrutyunjay Mohapatra, the chief of the India Meteorological Department (IMD). In an interview with PTI, Mohapatra emphasized that these emerging technologies will significantly enhance current numerical weather forecasting models in the next five years.
To lead this development, a dedicated expert team consisting of IMD and Ministry of Earth Sciences (MoES) professionals has been formed. Collaborations with prestigious institutes such as the Indian Institutes of Technology (IITs) and Indian Institutes of Information Technology (IIITs) have also been established to leverage their expertise in AI and machine learning.
Mohapatra highlighted that the IMD is expanding its observational systems to provide mesoscale weather forecasts at the panchayat level, covering areas of over 10 square kilometers more rapidly. The IMD has deployed a network of 39 doppler weather radars, covering 85% of the country’s landmass, enabling hourly forecasts for major cities.
Additionally, the IMD has digitized weather records dating back to 1901, presenting an opportunity to utilize AI for better understanding weather patterns. Mohapatra explained that AI models can analyze this extensive historical data and improve forecasts without relying solely on the physics of weather phenomena.
It is worth noting that AI is being increasingly adopted by weather agencies worldwide as a means to enhance forecasting capabilities, reduce costs, and boost efficiency. AI tools offer the advantage of not only aiding in understanding weather patterns but also playing a crucial role in fighting climate change.
For instance, projects like watsonx.ai, a collaboration between NASA and IBM, employ AI to monitor environmental shifts and provide future forecasts based on collected data. Similarly, Google’s DeepMind has developed GraphCast, an AI-driven weather forecasting model capable of delivering 10-day predictions within a minute. According to scientists at Google DeepMind, GraphCast has outperformed traditional weather prediction methods with a 90% verification rate.
The advantage of AI over conventional methods lies in its ability to leverage historical weather data to predict patterns more economically and accurately. By identifying intricate patterns within datasets, AI enhances forecast precision in ways that traditional equations cannot match. Furthermore, AI-powered forecasting techniques like GraphCast exhibit energy efficiency, being approximately 1,000 times more cost-effective than conventional methods, as reported by the Financial Times.
In conclusion, the integration of AI and machine learning into weather forecasting holds tremendous potential for improving prediction accuracy. The IMD, along with collaborations and advancements in technology, is at the forefront of harnessing these technologies to enhance the understanding and forecasting of weather patterns in India.
FAQ
1. How will artificial intelligence enhance weather forecasts?
Artificial intelligence will leverage historical data to improve the accuracy of weather forecasts. By identifying intricate patterns within datasets, AI models can predict weather patterns more economically and accurately compared to traditional methods that rely on costly computing power.
2. What are the benefits of using AI in weather forecasting?
AI-powered forecasting tools not only aid in understanding weather patterns but also play a crucial role in combating climate change. These tools reduce costs and boost efficiency in weather agencies globally.
3. What AI projects are being developed for weather predictions?
Projects like watsonx.ai, a collaboration between NASA and IBM, monitor environmental shifts and provide future forecasts based on gathered data. Google’s DeepMind has also developed GraphCast, an AI-driven weather forecasting model that has shown significant advancements in accuracy and cost-effectiveness.
4. How does AI outperform conventional weather prediction methods?
AI models leverage historical weather data to identify intricate patterns that conventional equations cannot always capture. This offers increased precision and cost-effectiveness in making accurate weather predictions.
Artificial intelligence (AI) and machine learning have the potential to revolutionize weather predictions in India and around the world. The integration of these technologies into weather forecasting models is expected to significantly enhance prediction accuracy in the next five years. This development is led by a dedicated expert team consisting of professionals from the India Meteorological Department (IMD) and the Ministry of Earth Sciences (MoES). Collaboration with prestigious institutes such as the Indian Institutes of Technology (IITs) and Indian Institutes of Information Technology (IIITs) further leverages expertise in AI and machine learning (link).
The IMD is expanding its observational systems to provide mesoscale weather forecasts at the panchayat level, covering areas of over 10 square kilometers more rapidly. This is made possible by a network of 39 doppler weather radars deployed across the country, covering 85% of the landmass. These radars enable hourly forecasts for major cities.
One of the key advantages of AI in weather forecasting is its ability to leverage historical weather data. The IMD has digitized weather records dating back to 1901, creating an opportunity to utilize AI for better understanding weather patterns. AI models can analyze this extensive historical data and improve forecasts without solely relying on the physics of weather phenomena.
AI is increasingly being adopted by weather agencies worldwide to enhance forecasting capabilities, reduce costs, and increase efficiency. Projects like watsonx.ai, a collaboration between NASA and IBM, employ AI to monitor environmental shifts and provide future forecasts based on collected data. Google’s DeepMind has developed GraphCast, an AI-driven weather forecasting model capable of delivering 10-day predictions within a minute. This model has shown a verification rate of 90% and outperformed traditional weather prediction methods (link).
The benefits of using AI in weather forecasting are significant. AI’s ability to identify intricate patterns within datasets enables more accurate predictions compared to traditional methods. By leveraging historical weather data, AI models offer increased precision and cost-effectiveness in making accurate weather forecasts. AI-powered forecasting techniques like GraphCast also exhibit energy efficiency, being approximately 1,000 times more cost-effective than conventional methods (link).
In conclusion, the integration of AI and machine learning into weather forecasting holds tremendous potential for improving prediction accuracy and efficiency. The IMD, in collaboration with prestigious institutes and advancements in technology, is at the forefront of harnessing these technologies to enhance the understanding and forecasting of weather patterns in India.
The source of the article is from the blog lanoticiadigital.com.ar