Lok Sabha Question No. 4367 (UNSTARRED), answered on 2025-03-26
Subject: Arka and Arunika Supercomputers
Ministry: EARTH SCIENCES
Asked by: Shri Jashubhai Bhilubhai Rathva

## GOVERNMENT OF INDIA LOK SABHA

## MINISTRY OF EARTH SCIENCES UNSTARRED QUESTION NO. 4367

## TO BE ANSWERED ON WEDNESDAY, 26 TH  MARCH, 2025

## ARKA AND ARUNIKA SUPERCOMPUTERS

## 4367.   SHRI JASHUBHAI BHILUBHAI RATHVA:

Will the Minister of EARTH SCIENCES be pleased to state:

- (a) the manner in which the deployment of the Arka and Arunika supercomputers improved the accuracy of weather forecasting and disaster prediction in the country;
- (b) the initiatives being undertaken to train Indian meteorologists and scientists in using AIpowered climate prediction models;
- (c) whether the Government has set any targets for reducing forecasting errors with the use of Arka in the coming years; and
- (d) whether there are any specific Government-backed  programmes  that  will utilize Arunika's  computational  power  to  optimize  fisheries,  marine  biodiversity  or  deep-sea exploration and if so, the details thereof?

## ANSWER

## THE MINISTER OF STATE (INDEPENDENT CHARGE) FOR MINISTRY OF SCIENCE AND TECHNOLOGY AND EARTH SCIENCES (DR. JITENDRA SINGH)

- (a) The  Ministry  of  Earth  Sciences  (MoES)  provides  weather  forecasting  services  to  the nation using several numerical weather prediction (NWP) models. These models are run routinely at different temporal and spatial scales on supercomputers to support operational weather forecasts. This activity requires state-of-the-art computational resources. The supercomputers (Arka and Arunika) are crucial to improving the accuracy of weather forecasts by running high-spatial resolution NWP models with a high volume of  observational  datasets  into  the  latest  data  assimilation  methods  to  improve  model initial conditions further.

For example, the current resolution of models used for short and medium range weather forecasts  is  inadequate  for  block-level  forecasts.  To  achieve  accuracy  in  weather forecasts, we need to increase model resolutions from 12.5 km to 6.5 km (short-medium range) and 35 km to 12 km (seasonal). Higher model resolution has demonstrated better predictability  skills  for  extreme  events.  Increasing  the  resolution  requires  computing resources  that  are  eight  times  greater  than  the  current  high-performance  computing (HPC) capacity. Therefore, deploying Arka and Arunika will enable operational models to run at much higher resolutions than currently possible with the existing HPC, thereby enhancing the predictability of forecasting extreme weather events such as heat and cold waves, cyclones, heavy rainfall, floods, thunderstorms, lightening, etc. across the country.

- (b) The enhanced computational capability helps to efficiently use new advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to enhance the  accuracy  of  NWP  products  further,  thereby  significantly  improving  the  forecast products rendered to various stakeholders, including disaster preparedness. The Ministry of Earth Sciences explores integrating AI and ML technologies into weather forecasting systems in addition to physics-based numerical models. This initiative is part of a broader strategy to enhance the accuracy and efficiency of meteorological predictions nationwide. To further strengthen, Indian scientists are collaborating with international programs such as "Momentum Partnership" to improve AI capability in weather and climate predictions.

The Ministry has established a dedicated virtual center on AI/ML/Deep Learning (DL) at the  Indian  Institute  of  Tropical  Meteorology  (IITM)  in  Pune.  A  dedicated  functional group has been established in IMD under the MoES to strengthen the R&D activities in AI/ML. These centers focus on leveraging AI, ML, and DL techniques for advancements in Earth Sciences. It has already developed several AI/ML-based applications tailored for localized predictions and the analysis of weather and climate patterns. Achievements and outcomes of AI and ML in the research and development (R & D) of weather prediction are provided below:

-  Improved the short-range precipitation forecast  in  1-day, 2-day,  and  3-day  lead times with a reduction in bias
-  Developed  high-resolution  (300m)  urban  gridded  meteorological  data  sets  for temperature and precipitation
-  Developed  the  time-varying  Normalized  Difference  Urbanization  Index  with  a spatial resolution of 30 meters from 1992-2023
-  Developed very high-resolution precipitation data sets for verification purposes
-  Deep Learning approach is being explored for precipitation nowcasting using data from DWRs
- (c) Yes.
- (d) The MoES has launched various missions that focus on improving different components of  earth-ocean  sciences  for  a  weather-aware  society,  which  helps  to  optimise  fisheries management, maintain marine biodiversity, and  also  in  deep-sea exploration.  The core computing  capacity  provided  by  Arunika  and  Arka  shall  be  extensively  used  for  these purposes.

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