In the grey hush before dawn, when the sea’s salt air still trembles with unspoken warnings, the sky over Bangladesh (and indeed much of the world) holds secrets we’ve long struggled to read. But now, an artificial intelligence developed by DeepMind is promising to peer deeper, farther, and faster into those secrets — offering a new vantage, and new hope, in an age of intensifying storms.
Turning Data Into Atmospheric Insight
On 14 November 2023, DeepMind unveiled GraphCast, a neural-network model capable of generating a 10-day global weather forecast in under a minute—delivered with accuracy that, in many measures, edges beyond the world’s best conventional system. Trained on decades of atmospheric data, GraphCast learns to “imagine” how Earth’s weather evolves — stepping forward in time by repeatedly applying six-hour predictions to reach up to ten days.
By comparison, traditional weather-forecast systems rely on supercomputers that crunch equations representing atmospheric physics. These systems, though powerful, can take hours to render a forecast at global scale. GraphCast, once trained, consumes a fraction of that energy and computing effort.
What does “more accurate” mean in practice? In evaluations, GraphCast outperformed the European Centre for Medium-Range Weather Forecasts’ High Resolution Forecast (ECMWF HRES) in 90 % of 1,380 measures — metrics like temperature, humidity, wind speed, and pressure at multiple atmospheric levels.
Yet the story doesn’t end there. In December 2024, DeepMind extended its ambition with GenCast, an ensemble AI model that forecasts probabilities across 50+ possible weather trajectories — reaching out 15 days ahead. GenCast’s promise: not just to guess one “best” future, but to illuminate risks, uncertainties, and extremes.
And in mid-2025, DeepMind made a bold leap: Weather Lab, a public interface that visualizes AI cyclone-path predictions, has been launched. The system is already interacting with the U.S. National Hurricane Center, offering AI-predicted tracks, intensities, and multiple possible futures.
When Storms Speak, AI Listens Better
In July 2024, Hurricane Beryl barreled across the Atlantic. Conventional models projected one path; GraphCast anticipated a sharp deflection, redirecting the storm toward southern Texas rather than Mexico, nearly a week in advance — and it was correct. That wasn’t an isolated success. Across that hurricane season, AI models showed tantalizing promise in tracking storms with finer foresight.
Yet, as Reuters cautions, even the most precise forecast cannot eliminate disaster risk. Floods in Europe in 2024 were foreshadowed by numerical models — but communication gaps, uncertainties, and systemic vulnerabilities meant lives were still lost.
This tension underscores both the power and responsibility of AI in meteorology. Forecasts won’t save lives by themselves; the trust, infrastructure, and decisions that follow them must do so.
Skeptics, Synergies, And Scope
No one in this field imagines AI will suddenly render traditional weather science obsolete. In fact, ECMWF has acknowledged the promise of AI-based forecasting and is developing an Artificial Intelligence/Integrated Forecasting System (AIFS) rooted in GraphCast’s architecture. Many scientists argue the real power lies in hybrid systems — marrying data-driven AI models with physics-based predictions to harness the strengths of both.
Others remain cautious. AI models can amplify biases in data; abrupt, chaotic weather shifts (like the sudden intensification of storms) may still confound predictions. Some experts have also warned that while results are strong, claims that AI will replace conventional systems “overnight” are overhyped.
And then there’s scale and access: training such models demands immense data and compute resources. For many nations — especially in the Global South — that barrier remains formidable.
Still, in the corridors of meteorological agencies, AI weather forecasting has shifted from speculative to imperative. In interviews, Met Office scientists have called DeepMind’s advances “exciting,” while ECMWF has described them as “significant” additions.
A Dawn For Climate Resilience
For Bangladesh — a land of deltaic lowlands and narrowing coastlines — more timely, accurate forecasts could be transformative. Cyclone shelters, early evacuations, crop planning, energy dispatch — all stand to benefit when lead times lengthen, uncertainty shrinks, and forecasts land closer to reality.
Imagine a farmer in Patuakhali at 4 a.m. receiving a forecast that not only predicts rain five days ahead, but shows a 30 % chance of a cyclone veering eastwards, with wind speeds crossing a known risk threshold. Or a district disaster manager in Chittagong checking multiple AI storm tracks to decide whether to evacuate a coastal village now — or wait until noon. These are the shifts that change outcomes on the ground.
It’s easy, in headlines, to portray AI as a deus ex machina in the climate-risk narrative. But the real story is quieter, deeper: AI isn’t replacing meteorologists — it’s helping them see farther. It doesn’t dictate decisions — it offers options. It doesn’t banish uncertainty — it frames it.
Looking Beyond The Horizon
As GraphCast and GenCast mature, there are frontiers still to explore: Can AI nudge forecasting ever closer to 30 days? Early tests suggest it might. One study recalibrated GraphCast’s starting data and achieved an 86 % improvement in 10-day accuracy — and even “reasonably accurate” forecasts up to 33 days in some scenarios.
Elsewhere, new models like FengWu push to surpass AI weather models — attempting to stretch predictive skill beyond a 10-day horizon. The arms race in forecasting is real — and accelerating.
But no matter how far models extend, their promise is only as strong as the systems that use them — the networks of meteorological services, the community of forecasters, the bridges to decision-makers, and the people on the ground who must act.
In the language of weather, uncertainty will always whisper. But now, AI is helping us listen more closely — mapping subtle signals, narrowing futures, offering clarity in chaos.
In this new chapter of forecasting, our task is not merely to predict the next storm, but to kindle trust, bridge science and society, and turn insight into action. For in a changing climate, the difference between forecast and fate may lie in what we do with the knowledge we gain.
