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Delhi weather: Why IMD keeps predicting rain but the heat won't quit

On paper, Delhi was supposed to have a 'pleasant day' on Tuesday (May 20). The India Meteorological Department had issued a yellow alert — thunderstorms, dust storms, some strong surface winds, maybe a touch of lightning. But as any Delhiite could tell you, the city felt more like a 'tandoor.' A blazing, suffocating heat wrapped the capital, and even the winds did little more than shuffle around the hot air. Pleasant? Not by a long stretch. It's not just a one-off mistake either. Time and again, the IMD has struggled to forecast Delhi's weather with any meaningful accuracy. From missing sudden storms that blow roofs off metro stations to underestimating rainfall that paralyses entire cities, India's official weather agency finds itself repeatedly caught off guard. And that begs the question: after more than 150 years of operations, why is the IMD still struggling to get it right? How often do IMD forecasts go wrong? On May 2 this year, Delhi was battered by a torrential downpour — 77 mm of rainfall in a single day, the second-highest May total since 1901. It was the wettest day in over eight months. And yet, not even a whisper of this made it to the IMD's forecasts the night before. On June 28, 2023, an unpredicted storm dumped 91 mm of rain on Delhi in a single hour. The agency had warned of 'light to moderate' rainfall. What came instead looked like the start of a monsoon apocalypse — flooding roads, disrupting traffic, collapsing infrastructure. The story repeats across the country. In December 2023, Tamil Nadu was devastated by heavy rainfall that killed at least 10 people. Once again, the IMD failed to forecast the intensity of the storm. Its Director General, Mrutyunjay Mohapatra, later admitted that while rain had been predicted, the storm's ferocity caught the department off guard. A warning was only issued in the early morning hours—too late for most to prepare. Ironically, the IMD is globally respected for its monsoon modelling. It uses the Dynamical Model, a complex climate system-based tool involving ocean-atmosphere coupling, and has recently integrated data from the high-performance computing system Mihir, one of India's most powerful supercomputers. Why did India start forecasting weather in the first place? The story of India's weather forecasting woes begins not today, but nearly a century and a half ago. Founded in 1875, the IMD's first mission was to crack the code of the monsoon. Back then, it wasn't just about rain. Famine stalked the land, and agriculture was wholly dependent on monsoon rainfall. The British colonial government saw meteorology as a matter of life, death and revenue. The IMD's first meteorologist, Henry Francis Blanford, tried to connect Himalayan snow cover with monsoon rainfall. His successor, John Eliot, added data from Australia and the Indian Ocean. But no matter how many charts they drew or patterns they spotted, the forecasts still failed to prevent famines. In 1899–1900, more than a million Indians died in a famine that Eliot had confidently predicted would not come. In 1904, Sir Gilbert Walker took the reins. A statistician by training, Walker introduced the idea of global pressure patterns—including what we now call the Southern Oscillation, part of the El Niño system—into monsoon prediction. He identified 28 variables that seemed to influence rainfall. But even then, forecasting was more art than science. How accurate are IMD forecasts today? India's seasonal monsoon predictions, vital for both agriculture and the broader economy, have historically had limited precision. Over the past 20 years, the IMD has achieved an average accuracy of just 42 per cent for its initial monsoon forecasts. That means in 14 out of 24 years since 2001, the actual rainfall deviated by more than five percentage points from the early forecast—making it statistically less reliable than a coin flip, according to a Hindustan Times analysis. That said, IMD's forecasting reliability has improved over the last five years, with fewer large deviations than in the past. The IMD's official monsoon outlook carries a standard error margin of ±5 per cent. For instance, the 2025 forecast anticipates rainfall at 105 per cent of the Long Period Average (LPA), allowing for this range. In recent years, the IMD has enhanced its precision for short-range and extreme weather forecasts by 40–50 per cent, largely due to high-resolution numerical models and AI tools. What models has IMD used and replaced? For nearly a century, India's monsoon forecasting relied on tweaking statistical models. In 1988, a new power regression model introduced by Vasant Gowariker promised more accurate forecasts using 16 parameters. But over time, many of those predictors lost relevance. The model failed to predict droughts in 2002 and 2004, and confidence in it eroded. From 2007, IMD began using ensemble statistical forecasting—blending multiple models to generate a more accurate estimate. Between 2007 and 2018, the average forecast error dropped from 7.94 per cent to 5.95 per cent of the LPA. That's progress — but still not precision. What are the latest forecasting systems IMD is using? The real shift came with the launch of the Monsoon Mission Coupled Forecasting System (MMCFS) in 2012. Unlike statistical models, MMCFS integrates data from oceans, land and atmosphere to simulate monsoon behaviour. In 2021, a multi-model ensemble (MME) system was added, incorporating forecasts from climate centres in the US, Japan and other countries. The rationale: if one model fails, maybe five together won't. Since these upgrades, IMD forecasts have become more accurate—at least on paper. The government claims that seasonal rainfall forecasts now have 21 per cent less error than those issued in the 1990s and early 2000s. But public confidence remains low. Ask someone in Delhi when they last trusted an IMD forecast, and you're likely to get a sarcastic shrug. Why are extreme events still so hard to predict? Part of the issue is scientific. Thunderstorms and cloudbursts are inherently difficult to predict. They form quickly, behave erratically, and require ultra-high-resolution data. For such events, IMD relies on nowcasts—forecasts issued just 2–3 hours in advance. But systemic constraints persist. Countries like the US and UK have long benefited from dense weather station networks, rapid data transmission and vast computing power. India is still catching up. Under Mission Mausam, the IMD has increased its radar capacity from 26 to 39, with plans to scale up to 126. Doppler radars are being installed nationwide, and a new high-density mesonet—automated local weather stations—is being rolled out in major cities. New tools like microwave radiometers and wind profilers are also being introduced. How does IMD compare to international weather agencies? Unlike the US National Weather Service or the European Centre for Medium-Range Weather Forecasts, the IMD often struggles with clarity and urgency in communication. Alerts may go out late at night. Warnings often lack actionable detail. And public trust—once lost—is hard to win back. In July 2023, Bengaluru received 132 mm of rain in under four hours. The IMD had issued only a 'moderate rain' alert. Lakes overflowed, tech parks shut down, and people were seen kayaking through flooded streets. Compare that with the Netherlands—one of the most flood-prone countries—where smart drainage systems are linked to forecasts, adjusting pumping capacity in real time. At its core, weather forecasting is not just a scientific challenge. It's a public service. Getting it wrong isn't just embarrassing—it's dangerous.