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Last January, 25th people in New York City were panicking.
The media spoke of a snowpocalypse that was coming, and it never happened.
While several inches of snow did occur in the city, there were erroneous forecasts that talked about 2 to 3 feet of snow in less than a day.
Based on those forecasts, the subways and much of the city infrastructure were closed.
People in general are skeptical of weather forecasts.
They see the hype in the media.
They get conflicting reports from a number of different sources.
Some forecast, maybe simply too vague.
Or they'll remember all of the missed forecasts in any new warning is simply like the boy who cried wolf.
But the reality is that better information was available for that snowstorm.
In fact, that's what my team and I are working on to develop better tools.
These better tools for forecasting can save lives and save money.
So take a look at this map of the New York City metropolitan area.
This was a weather forecast that we generated over two days before the snow even started to fall.
The dark colors over the city show only that several inches of snow were to happen, and the areas to the east over Long Island where you see it more white.
That's where the heavy snow would occur, and that is actually what happened.
So why would we need this level of precision?
It's because weather forecast really matter.
It can affect thousands of people and mean millions of dollars.
So, for example, from 1972 2012 there were over 7000 weather related disasters around the world.
Over a 1,000,000 people lost their lives from that from those events, and that was mostly in the developing world.
In the developed world, there were over $2 trillion worth of economic impact.
Better weather forecasts could help to mitigate those types of those types of impacts.
So the challenge is how do we go from this common skepticism that weather forecasts are always wrong to a level of trust?
Well, it's about relying on the science.
So on our planet, energy from the sun drives weather.
If we understand how the energy and Iraq's in our atmosphere, we can determine how the wind flows over the surface, how clouds form, where and when storms would occur.
The next thing we have to do is we introduced mathematics toe model, the physics and effectively.
That's the bridge between the science and weather forecasting.
So some of you may see these equations and think this is a sort of scary math.
But scientists like myself will solve equations like these on a super computer to create predictions in three dimensions off temperature, wind and precipitation.
The next thing we have to dio is leverage advances in computing.
So that forecasts that I showed you offer the correct snowfall in New York City a few years ago that would have taken days to compute on a large system.
But today we can compute a forecast like that in under an hour on a relatively modest computer.
So now we have two ingredients where we understand that we can produce precise forecasts, and we can produce them quickly.
But how do we know if the forecast, or even right, and how can we improve our models?
This is where we leverage additional advances in technology, such a spacecraft in orbit or advances in weather stations, and maybe even the sensors on our smartphones.
The idea is that all of these sensors give us information about whether and in fact, each and every day we are collecting were generating hundreds of terabytes of data about the weather on our planet, and that's millions of times more data compared to a few decades ago.
So now we have sort of three ingredients to improve weather forecasts.
We have improvements in the sciences, advances in computing and advances in the collection of data.
One of the problems is that thes advances are not used in an optimal way, and often we see problems with even data being shared.
And this is why the weather forecast that you commonly see are not reliable.
But we actually know how to solve that problem.
And it's really about using those ingredients in a different way in a manner that can produce forecasts that are more useful.
So what we do is that we will start by focusing on a particular geography or an application.
This is a forecast of set of thunderstorms.
Rather than saying 50% chance of thunder showers tomorrow, we're actually showing predictions of the individual storm cells that you can see a sigh in or turquoise colored surface.
Is there inside three dimensional predictions of the clouds.
This also can tell us where and when it will rain.
So what that means is that we can tell the difference in weather from one neighborhood to the next tomorrow.
So is amazing.
As this kind of capability is, it really is not enough.
What we have to be able to do is predict the impact of weather and by predicting the impact of weather.
That means that we can move from smear lee a weather forecast to a weather forecast that's actionable or, in other words, as one of my former bosses once said, You don't get points for predicting rain.
You get points for building arcs, so if we apply this principle, we can do some very valuable things.
For example, we can predict how much electricity we can really produce from wind turbines and solar panels to reduce our reliance on fossil fuels.
We can predict when storms will make the electricity go out and when the power will be restored, and we're actually doing this today we use data coming from utilities that tell us about their past performance and their infrastructure and Maur, and we ingest those into our models, which we correlate with weather, but we can apply these same ideas elsewhere.
We're already been helping farmers to produce more food and to reduce the amount of water and energy they use.
These kinds of techniques can also help reduce flight delays that airlines have due to weather.
We've had the privilege of being able to build on advances in science and technology to create a new set of forecasting methods.
Do the laws of physics.
We can never be perfect, but there's so much more that we can do.
And for me, this has really been the most rewarding because it's an actually an opportunity to really help people.
It's not that often that in one's career that they really have a chance to create new technology and new ideas that can have a positive influence on people's lives or lively hoods, or even to help the mayor of a large city to know with confidence with when that blizzard will hit and how bad it will be.
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B1 Intermediate

This weather forecasting model is actually accurate | Lloyd Treinish | TED Institute

4 Folder Collection
林宜悉 published on March 20, 2020

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