For most people, weather forecasting is about deciding whether to carry an umbrella, postpone a trip, or expect a hot afternoon. For India, however, the stakes are far higher.

A timely weather forecast can influence when a farmer sows or harvests a crop, when fishermen leave the coast, when cities prepare for heavy rainfall, and when disaster-response teams begin evacuations ahead of a cyclone. It can also influence food prices, insurance losses, logistics, energy demand and infrastructure planning.

That is the broader thinking behind Mission Mausam, India’s effort to build a more advanced and localised weather forecasting system. The first phase of the programme was approved with an outlay of ₹2,000 crore, with investments directed towards better observation networks, higher-resolution forecasting models, artificial intelligence and faster weather-warning systems.

The economic importance of this investment extends well beyond meteorology. Weather has a direct impact on agriculture, commodities, transport and inflation, which is why investors often monitor rainfall and crop trends while evaluating sectors exposed to weather risk. This is also relevant for participants in the commodity market, where weather can influence the supply and pricing of several agricultural commodities.

Now, as discussions emerge around a much larger ₹10,000 crore expansion, India faces a more difficult question. The country clearly needs better forecasting capabilities, but can a much bigger budget translate into equally large improvements on the ground?

Why India Needs More Precise Weather Forecasting

India is one of the more difficult countries in the world to forecast accurately.

Its geography ranges from the Himalayas to coastlines, deserts, plains and tropical regions, while its weather system is shaped by the monsoon, cyclones, heatwaves, thunderstorms, cloudbursts and highly localised rainfall patterns.

A forecast that is accurate at the state level may still be too broad for a farmer or district administration dealing with weather that changes dramatically across a relatively small area.

This is why the resolution of weather models matters.

For years, many major forecasting models used in India operated at around 12-km resolution. That means the atmosphere was divided into grid cells measuring roughly 12 km across, with the model estimating variables such as rainfall, wind and temperature for each area.

The newer Bharat Forecast System, or BharatFS, reduces that resolution to around 6 km.

On paper, halving the grid size may not sound transformational. In practice, it allows the system to represent smaller geographical features and more localised weather conditions. That can become particularly useful in regions where rainfall patterns change quickly across short distances.

BharatFS is also designed to support forecasting at the cluster-of-panchayats level, bringing India closer to the idea of genuinely local weather information rather than broad district or state-level predictions.

The challenge is that higher precision comes at a cost.

Reducing the grid size means the forecasting system must perform far more calculations. As models become more detailed, the requirement for computing power rises sharply.

Why Supercomputers Matter to Mission Mausam

This is where India’s investment in high-performance computing becomes important.

The Ministry of Earth Sciences has added major computing capacity through systems such as Arka and Arunika, which support weather and climate modelling.

Arka has a computing capacity of about 11.77 petaflops, while Arunika adds roughly 8.24 petaflops. Together with AI and machine-learning systems, these upgrades have significantly increased the computing capability available for meteorological forecasting.

This additional capacity allows weather agencies to process larger quantities of radar, satellite and surface data, run more detailed simulations and generate forecasts faster.

Speed matters because even an accurate forecast loses much of its value if it arrives too late.

A cyclone warning is most useful when authorities have enough time to evacuate vulnerable communities. A rainfall forecast helps farmers only when they can still change sowing, irrigation or harvesting decisions.

In that sense, the real value of computing power is not simply that it makes weather models more sophisticated. It makes forecasts more actionable.

AI Is Becoming Part of India’s Forecasting System

Artificial intelligence is also starting to play a larger role in weather prediction.

India is already using global AI-based systems such as GraphCast, Pangu Weather and FourCastNet as guidance tools alongside conventional forecasting models. Indian institutions are also experimenting with machine learning for nowcasting, extreme-weather detection and urban forecasting.

Traditional numerical weather prediction relies heavily on physical equations that attempt to simulate the atmosphere. AI-based systems work differently. They study enormous amounts of historical and real-time data and identify patterns that may indicate what happens next.

The future is unlikely to involve AI completely replacing traditional weather models. A more realistic direction is for both approaches to work together.

Traditional models offer a physics-based understanding of the atmosphere, while AI systems can potentially improve speed, pattern recognition and forecasting efficiency.

For India, this combination could be particularly valuable because of the complexity of the monsoon and the growing frequency of localised extreme-weather events.

Better Forecasts Are Only Useful If People Receive Them

Improving the model is only one part of the problem.

The other is getting the forecast to the person who needs it.

This is where services such as Mausamgram become important. Weather information is increasingly being delivered at the local level through platforms linked to panchayats and digital government services.

Users can access location-based forecasts, including short-term updates and forecasts stretching several days ahead.

This matters particularly in agriculture.

A farmer making a crop decision often needs more than a broad statement that rainfall is expected somewhere in the state. The useful question is whether rain is likely in that specific location, how intense it may be, and when it is likely to arrive.

That can influence sowing, irrigation, fertiliser application and harvesting.

Weather therefore has a much closer relationship with the economy than many people realise. Poor rainfall can affect agricultural output, which in turn can influence food inflation. That is why weather and inflation often intersect in India’s economic story. Investors looking to understand this relationship can also refer to Alice Blue’s guide on inflation and its impact on investments.

The Real Challenge Is Still Ground-Level Data

While better models and faster computers receive more attention, weather forecasting is only as good as the data fed into the system.

This makes ground-level infrastructure extremely important.

Doppler Weather Radars are among the most critical tools used to track rainfall, thunderstorms and cyclones. India’s radar network has expanded over the years, and by 2026 the country had around 50 Doppler Weather Radars in operation, alongside a wider network of Automatic Weather Stations and rain gauges.

Mission Mausam aims to strengthen this observation network further with additional radars, wind profilers, radiometers and upper-air monitoring systems.

The logic is simple: a better model cannot compensate indefinitely for weak observation coverage.

If large parts of the country do not have enough high-quality real-time data, forecasting accuracy will eventually hit a limit.

This is particularly important for localised events such as cloudbursts and thunderstorms, which can develop rapidly and may not be captured effectively by distant observation systems.

This Is Also Where Mission Mausam Has Slowed Down

The biggest challenge so far has not necessarily been the technology itself.

It has been execution.

Large weather systems are complex to procure, install and integrate. They require suitable locations, technical testing and connectivity with existing meteorological networks.

This has contributed to slower-than-planned spending under Mission Mausam.

For 2025-26, the original budget estimate was around ₹1,329 crore, but the revised estimate was reduced to about ₹840 crore, largely due to delays in procuring systems such as Doppler Weather Radars, wind profilers and radiometers.

In the previous financial year, spending was around ₹516 crore.

This does not mean the mission has failed.

Large scientific infrastructure programmes often take time to implement. But it does highlight a key problem: allocating more money is easier than deploying it efficiently.

That becomes particularly important when discussions turn to a possible ₹10,000 crore expansion.

Why Consider ₹10,000 Crore Before Phase I Is Fully Complete?

At first glance, the obvious question is why India should move towards a much larger second phase when parts of the first phase are still being implemented.

There is a reasonable case for caution.

If procurement and installation are already causing delays, increasing the budget alone will not automatically solve the problem. A larger allocation could simply create more projects waiting to be executed.

However, there is also a strong argument against waiting too long.

Weather infrastructure has long development cycles. Radars, observation systems and computing infrastructure cannot be built overnight. If the government waits until every element of Phase I is fully complete before planning the next phase, India could lose valuable time.

The better approach may therefore be to continue investing, but with clearer implementation benchmarks.

Instead of measuring progress mainly through budget allocation, policymakers could track how many new radars become operational, which regions gain new coverage, how much warning lead time improves and whether local forecast accuracy is actually getting better.

That would make the success of Mission Mausam easier to evaluate.

Why Better Weather Forecasting Matters for the Economy

The economic case for better forecasting is stronger than it may initially appear.

Agriculture is the most obvious example. More accurate rainfall and temperature forecasts can help farmers reduce losses and make better crop decisions.

Insurance is another area. More granular weather data can improve agricultural and property-risk assessment and may support the development of more sophisticated weather-linked insurance products.

Transport and logistics also benefit. Heavy rainfall, fog, storms and cyclones can disrupt airports, ports, roads and railway networks. Earlier warnings allow operators to modify routes and schedules before disruptions become more expensive.

Energy is increasingly important too. Solar and wind generation depend directly on weather conditions, while heatwaves can sharply increase electricity demand. As India expands renewable-energy capacity, better forecasting will become increasingly relevant to grid management.

For investors, these factors matter because weather risk can affect revenues, input costs, supply chains and margins across multiple sectors. Understanding these operational risks is part of broader fundamental analysis, particularly for businesses exposed to agriculture, infrastructure, energy and logistics.

Climate Risk Raises the Stakes Further

India’s need for better forecasting is also becoming more urgent as extreme-weather events become more disruptive.

Heatwaves can reduce labour productivity and increase power demand. Heavy rainfall can damage roads, homes and crops. Cyclones can disrupt ports and industrial activity, while irregular monsoons can affect food production and inflation.

Weather forecasting cannot prevent these events.

What it can do is create more time to react.

Even a modest improvement in warning time can allow authorities to move people to safety, help farmers protect crops and give businesses time to secure assets or reroute operations.

That makes forecasting not just a scientific investment, but also a form of economic risk management.

Can Mission Mausam Justify a Much Bigger Expansion?

The case for continued investment is strong.

India has already expanded its computing capacity, introduced the higher-resolution Bharat Forecast System, increased local weather services and improved its observation network.

But the question is not whether the technology is improving.

The more difficult question is whether the entire system — from observation and computing to communication and execution — is improving at the same pace.

That is where the next phase of Mission Mausam will be judged.

A ₹10,000 crore expansion would represent a major increase in ambition. If implemented well, it could help India build a much denser and more precise forecasting network.

But if procurement delays continue, the size of the budget may matter less than the pace at which the infrastructure is actually deployed.

The Bottom Line

India’s weather forecasting investment is ultimately about giving people more time to make better decisions.

The country is moving towards more detailed forecasting models, greater computing power, wider use of artificial intelligence and more localised weather services.

Those are meaningful improvements.

The remaining challenge is to build enough ground infrastructure and execute quickly enough for those capabilities to reach their full potential.

If India does move ahead with a ₹10,000 crore expansion of Mission Mausam, the success of that investment should not be judged by how much money is allocated.

It should be judged by whether forecasts become more accurate, warnings arrive earlier, coverage improves and the economic losses caused by extreme weather begin to fall.

Because in a country where weather can influence everything from crop yields to transport networks and inflation, a better forecast is not simply useful information.

It is economic infrastructure.