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The Multi-Hazard Early Warning Decision Support System (MHEW-DSS) represents a paradigm shift in India’s meteorological capabilities. Developed in-house by the India Meteorological Department (IMD) under the Ministry of Earth Sciences (MoES), it is a flagship digital transformation initiative under Mission Mausam. Launched officially in January 2024, the system transitions India from fragmented, manual forecasting to an integrated, automated, and impact-based warning regime.

Core Objectives and Vision

The primary goal of MHEW-DSS is to build an indigenous, real-time forecasting ecosystem that translates complex scientific data into actionable insights. It aligns with the government’s vision of a "Weather Ready and Climate Smart Nation," encapsulated in the philosophy “Har Har Mausam, Har Ghar Mausam.”

Key Features and Technological Innovations

The MHEW-DSS leverages open-source technology and Geographic Information System (GIS) maps to streamline the forecasting pipeline:

• Automation: Over 90% of weather data collection and quality checks are automated, enabling faster detection of weather systems.
• Enhanced Modeling: The system utilizes more than 95% of Numerical Weather Prediction (NWP) model inputs, a massive leap in data integration.
• Extended Lead Time: Forecast lead time has increased from 5 days to 7 days, providing authorities with a critical window for preparation.
• WAFES Core: The Weather Analysis and Forecast Enabling System (WAFES) serves as the central engine, allowing meteorologists to visualize weather conditions through GIS-based maps and generate real-time alerts.

Economic and Operational Impact

• The implementation of MHEW-DSS has yielded significant tangible benefits across various metrics. It has achieved a 30% improvement in forecast accuracy while reducing the time required to prepare forecasts by 50% (from 6 hours down to 3).
• From a fiscal perspective, the system has saved approximately ?250 crore by eliminating dependence on foreign vendors. Furthermore, the accuracy in predicting cyclone landfall points has reduced evacuation costs to one-third of what they were in 1999. Environmental sustainability is also a key byproduct; the digital workflow saves 23.4 tonnes of paper and approximately 210,240 kWh of electricity annually.

Case Study: Zero Casualty Success

• During Cyclone Biparjoy and Cyclone Dana, the precision of MHEW-DSS enabled timely evacuations, resulting in zero casualties in the affected regions of Gujarat and Odisha.

Sectoral Benefits: Impact-Based Forecasting

The system employs Impact-Based Forecasting, which assesses how weather affects specific socio-economic sectors rather than just predicting rain or wind levels.

• Agriculture: It provides twice-weekly Agromet advisories. Farmers adopting these reports have seen a 52.5% increase in annual income, with potential economic benefits in rain-fed districts estimated at ?13,331 crore.
• Public Health: The system supports Heat Action Plans and aids in predicting vector-borne diseases like Dengue and Malaria by analyzing weather patterns.
• Energy: It optimizes renewable energy planning (Solar/Wind) and protects grid infrastructure from extreme events through early warnings.
• Last-Mile Connectivity (Mausamgram): This hyper-local portal provides location-specific forecasts for over 6.2 lakh villages and 1.5 lakh pin codes, ensuring the most remote citizens are reached.

Institutional Framework

The MHEW-DSS ecosystem is supported by three major pillars:

• Ministry of Earth Sciences (MoES): The nodal ministry providing administrative and scientific oversight.
• India Meteorological Department (IMD): The operational lead responsible for data generation and dissemination.
• Mission Mausam: The strategic umbrella (approved Sept 2024) that funds and guides the enhancement of observation networks, data assimilation, and modeling.