Long Range Forecast System Explained
Source: PIB
GS I: Important Geophysical Phenomena (Monsoon, Climate), GS III: Disaster Management, Science and Technology
Overview
- The India Meteorological Department (IMD) has enhanced the performance of its Long Range Forecast (LRF) system through the adoption of the Multi-Model Ensemble (MME) approach since 2021.
- Improved forecasting is supported by Mission Mausam, expanded observation networks, advanced numerical weather prediction models, and high-performance computing.
- The strengthened forecasting framework enables more reliable seasonal outlooks, aiding informed decision-making in weather-sensitive sectors and improving climate resilience.
Why in the News?
The Ministry of Earth Sciences informed Parliament that IMD’s Long Range Forecast (LRF) system has achieved significantly higher accuracy since adopting the Multi-Model Ensemble (MME) approach in 2021.
News in Brief
- Since 2021, the India Meteorological Department (IMD) has adopted the Multi-Model Ensemble (MME) approach, significantly improving the accuracy of long-range monsoon forecasts.
- The average absolute forecast error declined from 7.8% of the Long Period Average (LPA) during 2016–2020 to 2.2% during 2021–2025.
- The improved forecasting system has enhanced the reliability and seasonal monsoon predictions, supporting better planning and disaster preparedness.
Key Highlights
Improved Forecast Accuracy
- All operational forecasts stayed within prescribed error limits, with an average absolute error of just 2.2% of the Long Period Average (LPA) between 2021 and 2025, driven by advanced modeling, infrastructure growth, and AI tools.
Multi-Model Ensemble (MME)
- Merges outputs from multiple climate models to lower individual uncertainties and boost reliability.
Better Representation of Climate Drivers
- Enhances prediction by incorporating large-scale climate phenomena such as:
- El Niño–Southern Oscillation (ENSO)
- Indian Ocean Dipole (IOD)
Measures to Strengthen Forecasting
- Adoption of an advanced Multi-Model Ensemble and coupled dynamical climate models for operational long-range forecasting.
- Continuous upgradation of numerical weather prediction models.
- Expanding observation networks under Mission Mausam with,
- Doppler Weather Radars (DWRs)
- Automatic Weather Stations (AWSs)
- Automatic Rain Gauges (ARGs)
- Wind profilers and upper-air observing systems
- Greater use of High-Performance Computing (HPC) and AI/ML tools.
- Expanded satellite observations through indigenous meteorological satellites and the assimilation of satellite, radar, and in-situ observations into numerical models to improve forecast accuracy.
- Fast dissemination of forecasts through mobile apps, APIs, SMS, TV, radio and social media in coordination with Central and State Government agencies.
Significance and Challenges
- Agriculture – Helps farmers choose the best planting and harvesting times.
- Water Management- Guides the storage and supply of drinking water and irrigation.
- Disaster Preparation- Warns communities early about severe floods and long droughts.
- Public Sectors– Assists energy grids, transport networks, and health agencies in planning.
- Early Warnings- Builds stronger safety shields against extreme weather shifts.
Challenges
- Climate Change– Makes future weather patterns harder to guess accurately.
- Data Gaps– Lacks enough weather stations in vulnerable or remote areas.
- Local Storms– Struggles to pinpoint exact spots for sudden, severe weather.
- High Costs- Demands huge computer power and constant model upgrades.
Long Range Forecast (LRF)
- A seasonal weather forecast issued for periods beyond two weeks, primarily for predicting the southwest monsoon.
Long Period Average (LPA)
- The 50-year average of all-India monsoon rainfall, used as the benchmark to classify monsoon performance.
Multi-Model Ensemble (MME)
- A forecasting technique that combines outputs from multiple climate models to improve forecast accuracy and reduce uncertainties.
Mission Mausam
- A Ministry of Earth Sciences (MoES) initiative to modernize India’s weather forecasting through advanced observation systems, high-performance computing, AI-based forecasting, and improved dissemination of weather information.
Conclusion
India’s improved Long Range Forecast System, powered by the Multi-Model Ensemble (MME) approach and supported by initiatives such as Mission Mausam, is strengthening the accuracy of seasonal weather predictions, enabling better disaster preparedness, agricultural planning, and climate resilience.
Key Takeaways
UPSC Prelims and Mains Practice Question
Consider the following statements regarding the Long Range Forecast (LRF) System in India:
- Since 2021, the India Meteorological Department (IMD) has adopted the Multi-Model Ensemble (MME) approach for long-range forecasting.
- The MME approach combines forecasts from multiple climate models to improve the reliability of seasonal forecasts.
- The MME system has improved the representation of large-scale climate drivers such as the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) in seasonal forecasting.
Which of the statements given above is/are correct?
A. 1 and 2 only
B. 2 and 3 only
C. 1 and 3 only
D. 1, 2 and 3
Answer: D
Mains Practice Question
Q. Discuss the significance of advancements in long-range weather forecasting for India’s agriculture, disaster management, and climate resilience. What challenges remain in improving forecast accuracy? (10 Marks)
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