A Sunny Outlook

Some years ago, I wrote a chapter in a book called Farming Futures. The book is about social entrepreneurship in India, and my chapter was about a firm called Skymet. Skymet is a private weather forecasting firm based partially out of Pune and partially out of Noida (along with other office in other locations). But researching for the chapter got me interested in both how the art and science of weather forecasting had developed over time, and where it is headed next.

Only trivia enthusiasts are likely to remember the name of the captain on whose ship Charles Darwin made his historic voyage that was to result in the publication of “On the Origin of Species”. Fewer still will remember that Admiral Robert FitzRoy committed suicide. The true tragedy, however, is that it is almost certainly his lifelong dedication to predicting the weather that caused him to take his own life.
We have, in the decades and centuries since, come a long way. Weather forecasting today is far more advanced than it was in Admiral FitzRoy’s day. Britain, for example, Admiral FitzRoy’s own nation, today has an annual budget of more than 80 million GBP to run its meteorological department. It has an accuracy of around 95% when it comes to forecasting temperatures, and an accuracy of around 75% when it comes to forecasting rain – anybody who is even remotely familiar with Britain’s notoriously fickle weather would know that this is no small achievement.

Farming Futures: Emerging Social Enterprises in India

Those numbers that I cited, and the tragic story of Admiral FitzRoy, come from a lovely book called The Weather Experiment.

But I first read about weather, and the difficulties associated with forecasting it in a book called Chaos, by James Gleick:

Lorenz enjoyed weather—by no means a prerequisite for a research meteorologist. He savored its changeability. He appreciated the patterns that come and go in the atmosphere, families of eddies and cyclones, always obeying mathematical rules, yet never repeating themselves. When he looked at clouds, he thought he saw a kind of structure in them. Once he had feared that studying the science of weather would be like prying a jack-in–the-box apart with a screwdriver. Now he wondered whether science would
be able to penetrate the magic at all. Weather had a flavor that could not be expressed by talking about averages. The daily high temperature in Cambridge, Massachusetts, averages 75 degrees in June. The number of rainy days in Riyadh, Saudi Arabia, averages ten a year. Those were statistics. The essence was the way patterns in the atmosphere changed over time…

Ch. 1, The Butterfly Effect, Chaos, by James Gleick

What is the Butterfly Effect, you ask? It gets its own Wikipedia article, have fun reading it.

All of which is a very long way to get around to the write-up we’re going to be talking about today, called After The Storm.

On 29 October 1999, a “Super Cyclone” called Paradip devastated parts of Odisha and the east coast of India. At wind speeds of almost 250 kms per hour, it ravaged through the land, clearing out everything in its path. Fields were left barren, trees uprooted like mere matchsticks, entire towns devastated. More than 10,000 people lost their lives.
Fast forward to two decades later. In 2020, bang in the middle of the Covid-19 pandemic, another cyclone—known as Amphan—speeds through the Bay of Bengal. It crashes into the land like Paradip did in 1999. Like before, many homes are destroyed and structures uprooted. But one thing is different: this time’s death toll is 98. That’s a 100 times lower than 1999’s casualties.
What made this difference possible? Simply put: better, timely and more accurate weather prediction.


We’ve made remarkable progress since the days of Admiral FitzRoy. Predicting the weather is still, admittedly, a very difficult and very expensive thing, as this lovely little write-up makes clear, but it is also something we’re much better at these days. We have better instruments, better computing power, better mathematical and statistical tools to deploy, and the ability to synthesize all of these to come up with much better forecasts – but it’s not perfect, and it’s not, well, good enough.

Those last two words aren’t meant as a criticism or a slight – far from it. The meteorologists themselves feel that is is not good enough:

“It almost becomes like flipping a coin,” Professor Islam says. “The IMD is not to be blamed. They will be very good at predicting the weather three or four days in advance. Beyond that, it cannot be done because there is a fundamental mathematical limitation to these questions.”
“IMD can do another sensor, another satellite, they can maybe improve predictions from two days, to three days. But can they do ten days? There is no evidence. Right now there is no weather forecasting model on the globe. India to Europe to Australia, it doesn’t matter, it’s not there.”


As Professor Islam says, he wants to move from up from being able to forecast the next four to five days, to being able to predict weather over the next ten days. Why? So that communities in the path of a storm have adequate time to move. What could be more important than that when it comes to meteorology.

So what’s the constraint? This is a lovely analogy:

“I give this example to my students,” the professor says, “Look, usually all of science and AI is based on this idea of driving with the rearview mirror. I don’t have an option, so I’m looking into my rearview mirror and driving. I will be fine as long as the road in the front exactly mirrors the rearview. If it doesn’t and I go into a turn? Disastrous accident.”


It’s weird what the human brain will choose to remind you of, but this reminds me, of all things, of a gorilla. That too, a gorilla from a science fiction book:

Amy distinguished past, present, and future—she remembered previous events, and anticipated future promises—but the Project Amy staff had never succeeded in teaching her exact differentiations. She did not, for example, distinguish yesterday from the day before. Whether this reflected a failing in teaching methods or an innate feature of Amy’s conceptual world was an open question. (There was evidence for a conceptual difference.) Amy was particularly perplexed by spatial metaphors for time, such as “that’s behind us” or “that’s coming up.” Her trainers conceived of the past as behind them and the future ahead. But Amy’s behavior seemed to indicate that she conceived of the past as in front of her—because she could see it—and the future behind her— because it was still invisible.

Michael Crichton, Congo

That makes a lot of sense, doesn’t it? And that’s the fundamental problem with any forecasting tool: it necessarily has to be based on what happened in the past, because what else have we got to work with?

And if, as Professor Islam says, the road in the future isn’t exactly like the past, disaster lies ahead.

But Artificial Intelligence and Machine Learning need not be about predicting what forms the storms of the future might take. They can be of help in other ways too!

“It hit us that the damage that happened to the buildings in the poorer communities could have been anticipated very precisely at each building’s level,” Sharma explains. “We could have told in advance which roofs would fly away, and which walls would collapse, which not so. So that’s something we’ve tried to bring into the AI model, so that it can be a predictive model.”

“What we do is, essentially, this: we use satellite imagery or drone imagery and through that, we identify buildings. We identify the material and technology of the building through their roofs as a proxy, and then we simulate a sort of a risk assessment of that particular building, right? We also take the neighbouring context into account. Water bodies, how high or low the land is, what kind of trees are around it, what other buildings are around it.”

The team at SEEDS and many others like it are more concerned about the micro-impact that weather events will have. Sharma is interested in the specifics of how long a building made from a certain material will be able to withstand the force of a cyclone. This is an advanced level of interpretation we’re talking about. It’s creative, important and life-saving as well.


In other words, we may not know the intensity of a particular storm, and exactly when and where it will hit. But given assumptions of the intensity of a storm, can we predict which buildings will be able to withstand a given storm and which ones won’t?

This is, as a friend of mine to whom I forwarded this little snippet said, is very cool.

I agree. Very cool indeed.

And sure, accuracy about weather forecasting may still be a ways away, and may perhaps lie forever beyond our abilities. But science, mathematics and statistics might still be able to help us in other ways, and that (to me) still counts as progress.

And that is why, all things considered, I’d say that when it comes to the future of weather forecasting, sunny days are ahead.

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Excellent, excellent stories, and the one I have covered today is also available in podcast form, narrated by Harsha Bhogle, no less. All their other stories are wroth reading too, and I hope you have as much fun going through them as I have.