Lok Sabha Question No. 1768 (UNSTARRED), answered on 2025-12-10
Subject: Advanced Computer Simulation Models to Improve Localised Weather Forecasting
Ministry: EARTH SCIENCES
Asked by: Shri Y S Avinash Reddy

## GOVERNMENT OF INDIA MINISTRY OF EARTH SCIENCES LOK SABHA UNSTARRED QUESTION NO. 1768

## TO BE ANSWERED ON WEDNESDAY, 10 TH  DECEMBER, 2025

## ADVANCED COMPUTER SIMULATION MODELS TO IMPROVE LOCALISED WEATHER FORECASTING

## 1768.   SHRI Y S AVINASH REDDY:

Will the Minister of EARTH SCIENCES be pleased to state:

- (a) the  details  of  advanced  computer  simulation  models  proposed  to  be  developed  to improve localised weather forecasting;
- (b) the manner in which these models be tailored to country's unique climate scenarios;
- (c) the details of expected improvements in forecasting accuracy;
- (d) the manner in which the mission plans to integrate artificial intelligence and machine learning (AI/ML) to enhance hyperlocal weather forecasting capabilities; and
- (e) the details of specific AI/ML techniques likely to be employed and the manner in which they will improve the accuracy and timeliness of weather predictions?

## ANSWER

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

- (a) Under  the  Mission  Mausam,  the  Bharat  Forecast  System  (BharatFS),  an  advanced computer simulation model, has already been developed, and it has been operational at a very high spatial resolution of 6 km. It also has the capability to provide predictions of rainfall events up to 10 days, covering the short and medium-range forecasts. Due to its higher resolution and improved dynamics, it generates weather forecasts at the panchayat or cluster of panchayats level. To further support the operations of highresolution model simulations in real-time, the computing facilities (Arunika and Arka) have been substantially increased to integrate voluminous data and run meso-scale, regional, and global models.

Further,  a  major  achievement  is  the  introduction  of  the  Mithuna  Forecast  System (Mithuna-FS). This new-generation global coupled model integrates the atmosphere, ocean,  land  surface,  and  sea  ice  components  with  state-of-the-art  physics  and  an upgraded data assimilation framework. Currently, this forecasting system operates at 12-km  resolution,  marking  a  significant  advancement  in  India's  medium-range localized weather forecasting capability. The Mithuna-FS suite also includes -

-  A 4-km high-resolution regional model  for  accurate  simulation of  monsoon dynamics, cyclones, and mesoscale extreme events over the Indian subcontinent;
-  A  330-m  hyper-resolution  urban  model  for  fog,  visibility,  and  air-quality forecasting over the Delhi region.

These models operate on the Arunika Supercomputer (8.24 PFLOPS) and assimilate dense observational inputs from radars, satellites, automatic weather stations (AWS), profilers,  and  ocean  systems.  Together,  they  enable  high-quality,  panchayat-level predictions  of  rainfall,  thunderstorms,  fog,  heat  stress,  and  pollutants.  This  work complements the operational Bharat Forecasting System (BharatFS).

- (b) These models under the Mission Mausam are specifically configured for India's diverse climate regimes. This includes monsoon-tuned convection schemes, improved cloud microphysics,  enhanced  representation  of  the  Western  Ghats  and  Himalayas,  and specialized  coastal  and  urban  physics.  The  4-km  regional  domain  captures  subcontinent heterogeneity, while the 330-m Delhi model resolves winter boundary-layer inversions and pollution episodes. Assimilation of dense radar, satellite, and surface observations  provides  forecasts  aligned  to  India's  agro-climatic  and  hazard-prone regions.
- (c) The  multi-scale  Mithuna-FS  suite  reduces  biases  in  rainfall,  temperature,  and  fog visibility.  Coupled  with  intelligence  and  machine  learning  (AI/ML)-based  postprocessing, these models provide sharper medium-range forecasts, better nowcasting capability, and more reliable district-scale probabilities for extreme rainfall, heatwaves, fog, air quality, and thunderstorms.
- (d) The Ministry has established a dedicated virtual centre involving the India Meteorological Department, National Centre for Medium Range Weather Forecasting (NCMRWF), and other institutes to integrate AI/ML systematically into the weather forecasting  chain  under  the  Mission  Mausam.  This  virtual  centre  coordinates  the development of AI/ML tools for bias correction, statistical post-processing, downscaling,  nowcasting,  and  multi-source  data  fusion  from  radars,  satellites,  and AWS networks.

NCMRWF also experimentally  runs  global  operational  AI  models  such  as  PanguWeather,  FourCastNet,  and  GraphCast  on  the  Arunika  Supercomputer  at  ~25  km resolution and fine-tunes them for India. These data-driven components run alongside the  dynamical  NWP  systems  on  the  Ministry's  HPC  resources,  enabling  the  rapid generation  of  tailored  hyperlocal  products  for  sectors  such  as  agriculture,  urban management, and disaster risk reduction.

AI/ML Integration enables rapid downscaling of coarse numerical weather prediction (NWP) fields to urban-ward resolutions using ML super-resolution (e.g., GANs) and CNN  nowcasting  from  real-time  observations,  producing  tailored  products  for agriculture (monsoon breaks), aviation (fog visibility), and urban management (PM2.5 alerts). Data fusion pipelines on Arunika accelerate last-mile delivery via APIs to IMD portals and stakeholder apps, supporting decision-centric forecasts during high-impact events. A dedicated functional group has been established within the IMD under the Ministry to strengthen R&D activities in AI/ML.

- (e) NCMRWF employs global pretrained AI/ML models, including Pangu-Weather (3D Earth-Specific Transformer), GraphCast (Graph Neural Networks), and FourCastNet (Adaptive Fourier Neural Operators), running experimentally at 25 km resolution on the  Arunika  Supercomputer.  Additional  techniques  encompass  deep  learning-based statistical  post-processing,  convolutional  neural  networks  (CNNs)  for  radar/satellite nowcasting, and machine learning downscaling methods like super-resolution generative  adversarial  networks  (GANs)  to  refine  Mithuna-FS  outputs  from  12  km global/4 km regional to hyperlocal scales. A novel deep learning model (meteoGAN) has  been  developed  for  the  Delhi-NCR  region  and  successfully  tested  for  rainfall downscale using ground-based and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall analysis at 300 meters spatial resolution.

These techniques enhance accuracy by learning systematic biases in dynamical NWP outputs,  yielding  reduced  root-mean-square  errors  in  key  variables  such  as  rainfall accumulation  (up  to  20-30%  improvement  in  medium-range  skill),  temperature extremes, wind speeds, fog visibility, and PM2.5 concentrations compared to raw model baselines. GraphCast and Pangu-Weather excel in hierarchical feature extraction across atmospheric  scales,  providing  sharper  spatial  localization  of  convective  events, monsoon  breaks,  and  urban  pollutants,  while  CNN-based  nowcasting  from  highfrequency  observations  extends  skillful  short-range  predictions  (0-6  hours)  beyond traditional extrapolation limits.

AI/ML models compute forecasts 100-1000 times faster than traditional NWP cycles, enabling near-real-time updates (every 15-30 minutes) between main dynamical runs on Arunika, which supports hyperlocal products for panchayat-level warnings during rapidly  evolving  events  like  thunderstorms  or  Delhi  winter  fog  episodes.  Postprocessing pipelines  fuse multi-source data  (AWS, radars, satellites) into calibrated probabilistic guidance, accelerating last-mile delivery to stakeholders in agriculture, aviation, and disaster management.

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