Answer

India’s weather observation network plays a crucial role in forecasting and mitigating the effects of extreme weather. The country has made progress with the installation of 39 Doppler Weather Radars (DWRs) and a growing network of observation systems, yet significant gaps persist. These gaps limit early warning capabilities, leaving many vulnerable regions exposed to severe weather events.

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Gaps in India’s Weather Observation Network

• Limited Doppler Weather Radar Coverage: Currently, India has only 39 Doppler Weather Radars, which are insufficient, particularly in critical regions like the western coast and major urban centres.
  For example: Cyclone-prone regions on the western coast, including cities like Ahmedabad and Bengaluru, face inadequate radar coverage, limiting effective storm prediction.
• Inadequate Flood Coverage: Only one-third of the flood-prone population in India is covered by early warning systems, leaving many vulnerable areas without sufficient coverage.
  For example: Regions like eastern Uttar Pradesh and Assam, which are highly susceptible to monsoon flooding, often lack timely early warning signals.
• Urban Area Vulnerability: Major cities like Mumbai and Chennai are prone to frequent flooding, but the absence of localised weather forecasting tools increases their vulnerability.
• Restricted Access to Weather Data: India offers limited access to weather data, restricting researchers and innovators from developing localised forecasting models and technology.
  For example: Unlike the U.S., where open access to weather data has promoted innovation, India’s restrictions limit the development of better predictive tools.
• Limited Use of Advanced Technologies: Advanced technologies such as machine learning and AI are underutilised in India’s weather forecasting system, resulting in delayed or inaccurate predictions.
  For example: The Bihar floods of 2019 were worsened by delays in flood forecasting, which could have been improved using AI-driven models.
• Outdated Communication Channels: IMD’s weather information apps and websites are not user-friendly, especially for people in rural areas, reducing their effectiveness in delivering urgent warnings.
• Lack of Last-Mile Connectivity: Many remote or underdeveloped regions, particularly in flood-prone areas, do not receive timely early warnings, endangering vulnerable populations.

Addressing Gaps to Improve Early Warning Systems

• Expanding Radar Network: Increasing the number of Doppler Weather Radars will improve localised weather tracking, providing better early warnings for extreme weather events.
  For instance: Adding radars to cyclone-prone regions would improve the accuracy of storm predictions.
• Improving Flood Warning Coverage: Expanding early warning systems to all flood-prone areas can enhance preparedness and reduce the impact of flooding on communities.
  For instance: Implementing better flood warning systems could significantly reduce the damage caused by seasonal monsoons.
• Strengthening Urban Forecasting Systems: Urban areas require more localised weather observation stations to improve flood predictions and enable faster emergency response.
  For example: Installing localised weather stations in Mumbai could prevent a repeat of the catastrophic 2005 floods by providing earlier flood alerts.
• Open Data Sharing for Innovation: Allowing open access to weather data can encourage researchers and innovators to develop more effective tools for localised early warnings.
  For example: The open access weather data system in the U.S. has enabled the development of apps that provide hyper-localised weather forecasts.
• Incorporating Advanced Predictive Technologies: Utilising AI and machine learning for predictive modelling will enhance the accuracy and timeliness of weather forecasts.
  For instance: Integrating AI models into India’s forecasting system could prevent delays.
• Enhancing User Experience for Warnings: Improving the user interface of weather apps and websites can ensure that warnings are easily understood and acted upon, especially in rural areas.
  For instance: In Japan, intuitive early warning systems have proven highly effective in ensuring quick evacuations during tsunamis.
• Strengthening Last-Mile Connectivity: Developing better infrastructure for delivering early warnings to remote areas will ensure vulnerable populations receive timely alerts.

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Addressing the gaps in India’s weather observation network will significantly enhance the effectiveness of early warning systems for extreme weather. By expanding radar coverage, improving data accessibility, and leveraging advanced technologies, India can build a future where its citizens are better protected from the increasing frequency of extreme weather events.

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