Monsoon forecasting in India traces back to the 1880s under British colonial administration. Pioneers like Sir Gilbert Walker embarked on understanding the relationship between Himalayan snow cover and Indian rainfall. Initially, forecasting relied on statistical correlations, leveraging mathematical relationships between physical indicators such as sea surface temperatures and atmospheric pressure systems. However, these early models heavily depended on historical data and struggled to accommodate sudden atmospheric changes.
As knowledge expanded, parameters like El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) were introduced to refine forecasts. These improvements allowed forecasts to become more detailed, incorporating up to 16 climate parameters. Despite these advancements, the models were still reliant on fixed historical relationships, faltering in years with unusual parameter combinations.
In 1988, Dr. Vasant Gowariker introduced India's first dynamic model, marking a shift towards simulating the atmosphere’s physical behavior rather than relying solely on historical patterns. This model divided India into four broad regions, making monsoon prediction more scientific. Yet, despite its progress, challenges remained in capturing micro-level variations and deviations from average atmospheric behavior.
The failure to predict the 2002 drought prompted the Indian Meteorological Department (IMD) to reassess its forecasting methods. Realizing a single model was inadequate, multiple physical predictors were combined, and data assimilation began integrating real-time observations into the models. The Statistical Ensemble Forecast System (SEFS), introduced in 2007, improved accuracy by considering results from multiple model runs. Nevertheless, predicting extreme rainfall events like sudden floods remained challenging.
Recent advancements have seen the IMD combining outputs from coupled global models that simulate oceans, atmosphere, and land simultaneously. This approach offers a broader and more realistic prediction, enhancing accuracy, especially in forecasting below-normal and above-normal rainfall years. However, the MME's effectiveness hinges on the quality of international models, and accurately predicting the exact onset or break spells of the monsoon remains a challenge.
Looking ahead, the IMD plans to adopt unified modeling frameworks for seamless predictions across different time scales. More powerful computing will enable higher resolution models, improving regional rainfall predictions. Additionally, the exploration of Artificial Intelligence (AI) and Machine Learning (ML) is underway to identify hidden patterns in atmospheric behavior, missed by traditional models. To commemorate India’s journey in monsoon science, a Monsoon Museum is being established.
Statistical models depend on historical data patterns, whereas dynamic models simulate the physical processes of the atmosphere and oceans for rainfall prediction.
Why was there a need to move from a single model to multiple models?A single model cannot capture the complexity of global climate. Using multiple models increases the likelihood of accurate forecasts, especially when atmospheric behavior is unpredictable.
What are the main challenges still facing monsoon prediction today?Accurately predicting the onset date, sudden dry or wet spells during the season, and regional rainfall variations remains difficult, even with advanced models.
How is technology improving future monsoon forecasting?Advancements in supercomputing, the integration of ocean-atmosphere data, higher resolution models, and AI-based pattern recognition are enhancing forecast accuracy. Unified modeling systems represent a significant step forward.
How accurate are India’s current monsoon forecasts?India's seasonal monsoon forecast accuracy has improved by about 5-10% compared to previous decades, although some extreme weather events still surprise forecasters.
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