Since 2021, the India Meteorological Department (IMD), under the Ministry of Earth Sciences (MoES), has adopted an advanced Multi-Model Ensemble (MME) approach for long-range forecasting. This has significantly improved the accuracy of long-range forecasts, with all forecasts issued since then remaining within the error limits. For example, operational monsoon forecasts since 2021 have been within the forecast error limits, with an average absolute error of 2.2% of the Long Period Average (LPA) over the five years (2021-2025). The year-wise details of the accuracy achieved by the IMD's Long-Range Forecast system during the last ten years are provided in the table below:

 

 

During the last ten years, the forecast error exceeded 10% of the LPA only once, in 2019, when the absolute forecast error was 14% of the LPA. Since the adoption of the MME strategy in 2021, the India Meteorological Department has significantly improved the accuracy of its long-range forecasts, achieving a mean absolute forecast error of only 2.2% of the LPA.

The India Meteorological Department, under the Ministry, has progressively upgraded its LRF system by adopting an advanced MME forecasting approach based on coupled dynamical climate models. The MME system combines forecasts from multiple climate models, thereby reducing uncertainties associated with individual models and improving the reliability and skill of seasonal forecasts. The adoption of the MME forecasting system has led to a significant improvement in the reliability of seasonal monsoon forecasts. During the period 2021–2025, the average absolute error of the first-stage forecast was 3.1% of the LPA, while the average absolute error of the second-stage forecast further reduced to 2.2% of the LPA. In comparison, the average absolute error during 2016–2020 was 7.8% of the LPA. Thus, the average absolute error has reduced from 7.8% to 2.2% of the LPA, indicating a substantial enhancement in the reliability and skill of the seasonal prediction system following the adoption and operational refinement of the MME forecasting system. The MME system has also enhanced the consistency of forecasts by improving the representation of large-scale climate drivers such as the El Nino–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), leading to more reliable seasonal monsoon predictions.

The Government has taken several measures to further improve long-range monsoon forecasting and prediction of extreme weather events in the country. These include:

• Adoption of an advanced Multi-Model Ensemble and coupled dynamical climate models for operational long-range forecasting.
• Continuous upgradation of numerical weather prediction models through improved model physics, higher spatial resolution, advanced data assimilation techniques, and enhanced computational capabilities.
• Capacity enhancement under the Mission Mausam focuses on the modernization of meteorological observation systems through the expansion of the national observation network with additional Doppler Weather Radars (DWRs), Automatic Weather Stations (AWSs), Automatic Rain Gauges (ARGs), upper-air observing systems, wind profilers, and other observing platforms, along with the use of high-performance computing infrastructure and artificial intelligence/machine learning-based forecasting tools.
• Expansion of satellite-based observations through indigenous meteorological satellites and the assimilation of satellite, radar, and in-situ observations into numerical models to improve forecast accuracy.
• Dissemination of weather forecasts and warnings through multiple communication platforms, including mobile applications, APIs, web portals, SMS, television, radio, and social media, in coordination with Central and State Government agencies.

The above measures are being implemented uniformly across the country for the benefit of all States and Union Territories. Therefore, no State-wise allocation or distribution of these measures is maintained.

This information was given by the Minister of State (Independent Charge) for Earth Sciences Dr. Jitendra Singh in a written reply in Lok Sabha today.

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NKR/JKP 

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