Answer:

India’s weather forecasting, primarily managed by the India Meteorological Department (IMD), is crucial for predicting monsoons, cyclones, and extreme weather events. With over 23 Doppler Weather Radars and a network of 700+ Automatic Weather Stations, IMD has enhanced its capabilities. However, climate change-induced unpredictability necessitates further upgrades in precision forecasting and real-time data analytics.

Need for Upgrading India’s Weather Forecasting Capabilities:

• Increasing Climate Variability: The rise in climate change-induced weather anomalies requires more precise forecasting tools to predict extreme events such as cyclones, floods, and heatwaves.
  For example: During the 2019 Odisha cyclone (Fani), early warnings by IMD helped in evacuating 1.2 million people, but prediction of its intensity required advanced models.
• Hyper-Local Weather Events: There is a need for hyper-local weather forecasts to predict events like urban flooding and heat islands, which are currently difficult with existing broad-based models.
  For example: In Mumbai’s 2017 floods, lack of hyper-local data resulted in delayed response and significant economic loss, highlighting the need for more granular weather predictions.
• Increased Agricultural Dependence: Agriculture, employing 58% of India’s population, heavily relies on accurate weather forecasts for timely sowing, irrigation, and harvesting.
  For example: Precise rainfall prediction is critical for the Kharif crop season, especially in states like Punjab and Haryana, where water resources are limited.
• Disaster Preparedness: Improved forecasting is vital for better disaster management, minimising the impact on human lives and infrastructure.
  For example: The Kerala floods in 2018 exposed the need for more accurate rainfall forecasts and flood warnings to prevent widespread devastation.
• Evolving Urban Landscapes: Rapid urbanisation increases the complexity of weather patterns in cities, necessitating advanced technology for real-time updates and accurate forecasts.

Recent Improvements in India’s Meteorological Services:

• Introduction of Advanced Weather Models: IMD has adopted the Global Forecast System (GFS) and Unified Model (UM) for better long-range forecasts, improving accuracy by 20-40%.
  For example: These models provided more accurate predictions for Cyclone Amphan in 2020, allowing better preparedness and response.
• Expansion of Doppler Radar Network: The installation of new Doppler Weather Radars in critical locations has enhanced IMD’s capacity to monitor real-time weather conditions.
  For example: The addition of radars in the Northeast region has significantly improved storm and flood forecasts, reducing risks to vulnerable communities.
• Development of Nowcasting Systems: IMD’s Nowcast services offer real-time weather updates with a lead time of 3-6 hours, crucial for aviation, agriculture, and disaster management.
• Integration of Satellite Data: Using data from Indian satellites like INSAT-3DR and SCATSAT-1, IMD has improved its ability to monitor weather systems over the ocean and inland.
  For example: These satellites played a critical role in monitoring the 2021 Tauktae cyclone in the Arabian Sea, enhancing early warning systems.
• Use of High-Performance Computing: Upgrading computational infrastructure with Mihir and Pratyush supercomputers has enabled more sophisticated weather simulations and faster data processing.
  For example: These systems have reduced prediction errors for rainfall by about 10% over the past five years, aiding in better water resource management.

Challenges Persisting in India’s Weather Forecasting:

• Inadequate Coverage in Remote Areas: Many regions, especially in the Northeast and Himalayan belt, lack adequate weather monitoring infrastructure.
  For instance: The Arunachal Pradesh region still faces challenges in receiving timely and accurate weather updates due to sparse meteorological stations.
• Insufficient Real-Time Data Sharing: There is a need for better integration and real-time sharing of data across various agencies and platforms.
  For instance: The lack of data integration between IMD and local bodies during the 2015 Chennai floods resulted in greater damage.
• Limited Public Awareness and Utilisation: Despite advancements, public awareness about accessing and using weather forecasts remains low, particularly in rural areas.
  For example: Farmers in Bihar often rely on traditional knowledge rather than updated weather forecasts, affecting agricultural productivity and disaster readiness.
• Technical and Technological Gaps: Current systems face technological limitations in predicting rapidly changing weather phenomena like tornadoes and cloudbursts.
  For example: The Uttarakhand cloudburst in 2013 underscored the need for enhanced predictive models capable of forecasting sudden and extreme weather events.
• Resource Constraints: Continuous updates to forecasting models and infrastructure require substantial investment and skilled manpower, which are currently limited.

Potential Impact of Enhanced Weather Forecasting:

• Improved Disaster Management: Enhanced forecasting capabilities can lead to timely evacuations and resource mobilisation, reducing fatalities and damage.
  For example: Accurate cyclone forecasts in Odisha have historically led to fewer casualties due to timely evacuations and well-planned responses.
• Optimised Agricultural Planning: Reliable weather predictions can help farmers plan better, reducing crop losses and enhancing yield.
• Economic Stability and Growth: Minimising disruptions caused by weather events can stabilise markets, reduce insurance claims, and sustain economic activities.
• Enhanced Water Resource Management: Improved forecasting can aid in efficient water storage and distribution, crucial for irrigation and urban water supply.
  For example: Forecasting tools enabled better management of water resources in the Krishna Basin, aiding in irrigation planning and drought mitigation.
• Strengthened Infrastructure Resilience: Proactive weather forecasts can guide infrastructure development to withstand extreme weather, reducing repair costs and enhancing durability.
  For example: Mumbai’s Coastal Road Project incorporated climate-resilient features based on weather data predictions, aiming to withstand future storm surges.

Enhancing India’s weather forecasting capabilities is vital to address the growing challenges posed by climate change. Future efforts must focus on upgrading technology, expanding infrastructure, and integrating data systems. By doing so, India can improve disaster preparedness, economic resilience, and sustainable development, ensuring a safer and more prosperous future for its population amidst a changing climate.

DOWNLOAD PDF