IMD keeping pace with evolving trends

The new IMD system has been developed at the request of the Ministry of Agriculture and Farmers’ Welfare, whose existing advisory system is built to deliver forecasts in a weekly format. The blending framework, developed by the Indian Institute of Tropical Meteorology, a research institute of the Ministry of Earth Sciences, is designed to feed directly into the Ministry’s pipeline and issue probabilistic forecasts for the next four weeks.

IMD has significantly upgraded its technological capabilities in 2025 and 2026, transitioning to AI-driven, high-resolution forecasting models under its “Mission Mausam” initiative and Vision 2047. These advancements focus on hyperlocal, block-level, and impact-based forecasting to enhance disaster resilience.

For example, the induction of high-performance computers named “Arka” and “Arunika” has reduced forecast runtime. Also, a new mobile-based app designed to provide location-specific, block-level weather forecasts and early warnings, supporting the “Har Har Mausam, Har Ghar Mausam” initiative.

Improvements in data, modeling, and infrastructure have led to roughly 50 per cent improvement in forecast accuracy in the last decade. Using AI, weather info is being disseminated via WhatsApp, SMS, and digital displays in local languages. What is more, instead of only forecasting weather parameters, IMD now provides forecasts that predict the impact of the weather (e.g., flood warnings, agricultural risks). Its long-term plan aims at zero-error prediction of severe weather events three days in advance. All these developments represent a quantum jump from traditional forecasting methods to a technologically driven system, providing crucial information, well in time, to farmers, fishermen, and disaster management agencies.

I view these changes as critically important as, in the two decades that I spent working in the agriculture and allied sectors while in service, I had found such a comforting and reassuring phenomenon notably absent, especially as far as the farming community was concerned. Forecasts those days would say, for instance, that rainfall in the month of July would be normal. That could very well have meant that parts of the country would receive heavy rainfall, even to the extent of causing floods, while other parts would have such scanty rain as to experience near drought conditions. What was more, farmers in none of those areas would have been prepared for what was in store for them, on account of the comforting forecast.

Quite apart from the quantitative aspect, the geographical spread, as estimated by the department, also plays a critical role in helping the farmers plan their operations. The impact of the same amount of rainfall can vary sharply in different parts. In Anantapur district, for instance, if there is no rainfall in the first week of June, the groundnut crop cannot be sown. Again, rainfall is required around the end of July, to enable harvest of groundnut because the ground needs to be wet. The same rainfall should have occurred, say in Prakasam or Warangal district, where cotton crops are grown. The crop will suffer extremely adverse consequences.

A geographically disaggregated, temporally appropriate and timely forecast serves as an extremely important early warning to the farming community. Such forecasts go a long way in supporting precise irrigation scheduling by predicting rainfall, evapotranspiration rates, temperature, and wind speed etc., all factors that influence plant water needs.

High-quality weather forecasts depend on the ability to analyse an immense volume of data gathered from diverse sources. The accuracy of such predictions depends both on the quantity and the quality of the data that informs meteorological models. Large scale data provided by satellites on such parametres as cloud cover, precipitation, solar radiation, and soil moisture are important sources. These factors help monitor climate trends over entire continents. Radar systems track the movement and intensity of precipitation, offering detailed real-time views of rainfall events. Ground-based weather stations, which record on-the-ground conditions such as temperature, humidity, wind speed, barometric pressure, and rainfall totals also provide crucial information, which is vital for validating remote sensing data. Radiosondes, or weather balloons equipped with sensors that collect vertical profiles of atmospheric conditions are also crucial for understanding upper-air dynamics and storm development. Ocean buoys are yet another vital source, which help monitor sea surface temperatures and other marine variables that influence large-scale weather systems like El Niño and La Niña.

All such data is processed using numerical weather prediction (NWP) models sophisticated software programs that use mathematical equations to simulate atmospheric behavior. These models can provide, short-term forecasts (0-3 days), useful for tactical decisions like spraying or harvest timing, medium-range forecasts (3-10 days), which support planning of planting, irrigation, and labor scheduling and seasonal forecasts (1-3 months) which help make strategic choices about crop selection, rotation planning, and input procurement.

Predicting weather using traditional observations, of cloud color, wind direction, animal behavior, and atmospheric halo is common. A ring around the sun or moon, for instance, indicates approaching precipitation, as light refracts off ice crystals. Winds shifting to the east are often seen as a sign of impending rain. Also, specific bird calls, increased frog croaking, and ants carrying food or eggs are recognized as indicators of impending rain. In addition, traditional calendars like the Hindu Panchangam, are used for long-range, seasonal predictions.

In recent times, farmer producer organisations (FPOs) are increasingly utilizing drones integrated with Internet of Things sensors to gather hyper-local, real-time weather data, which significantly improves the accuracy of agricultural forecasts.

That is probably because weather conditions are famously known to influence people’s moods. Raising the subject of the weather, therefore, can help one judge whether the interlocutor is in an agreeable mood or not. If the response is indifferent or rude, one had better avoid any further conversation!

(The writer was formerly Chief Secretary, Government of Andhra Pradesh)