But this critical in terms of planned response. Five inches of rain might cause dramatic flooding, whereas two inches is just a normal shower. In New York, three feet of snow might cause snowmageddon. Less than a foot? Weather forecasting has made enormous advances. A forecast of the quality and accuracy provided by the National Weather Service this week would have been unthinkable 20 years ago.
But as we have advanced we have realised more and more that we can only think of forecasts in terms of probabilities. The essence of extreme events is that, by definition, they are rare at a given location; conditions have to combine in just the right way to give us the worst case scenario. Advanced predictions of the most extreme winds or rain will always see significant uncertainty.
What Is Gluten? Get smart. Sign up for our email newsletter. Sign Up. Support science journalism. Knowledge awaits. See Subscription Options Already a subscriber? Create Account See Subscription Options. Continue reading with a Scientific American subscription. On one occasion he started the program in the middle rather than at its initial conditions and stored the data to three decimal places rather than its normal six.
While Lorenz expected to get a close approximation to his results, the answer turned out to be quite different. In his paper "Deterministic Nonperiodic Flow" considered the start of chaos theory , the scientist concluded that a small change in the initial conditions can drastically change the long-term behavior of a meteorological system. Based on his results, Lorenz stated it is impossible to predict the weather accurately.
In the years since his study, of course, supercomputers and other technological advances have changed that impossibility to a possibility. One of the major elements of a forecast is the chance of precipitation. After all, people want to know if they need to take an umbrella to work or if they might have to shovel their car out of the snow.
Probability P equals the forecaster's confidence C that precipitation will occur times the area A expected to get the precipitation. The first numerical, physics-based computer models were introduced into the arena of weather forecasting in the s.
As supercomputers have grown more powerful and the ways of collecting and processing weather-related data have gotten more varied, the accuracy of forecasts has improved. The rate of improvement has been calculated at about one day per decade. To put it another way: a six-day forecast in would have been as accurate as a five-day forecast was back in Supercomputers have been involved in weather forecasting since the midth century.
In the US, the National Weather Service has supercomputers that process nearly all of the observational weather data the organization collects. According to the NWS, the last major computer update came in - the combined processing power of the units is 8. In the UK, meanwhile, Microsoft and Met Office have teamed up to build the most powerful supercomputer to forecast weather and climate change.
It's expected to be operational in The British government provided 1. However, this rate has started to slow in recent years, which means that other approaches - like increasing the computational efficiency of the predictive models - may be necessary to continue making progress in terms of more accurate weather forecasting. Despite all the technological advances in these computers, they aren't always accurate.
The chaotic nature of weather means that as long as forecasters have to make assumptions about the processes taking place in the atmosphere, a chance will always exist for any computer to include errors, regardless of how powerful or fast the computer may be.
Weather stations are one way forecasters collect data to help them make predictions. But the location of the stations can affect just how accurate the data is. A great leap forward was made when supercomputers were introduced to the forecasting community in the s. The first computer model was much simpler than those of today, predicting just one variable on a grid with a spacing of over km.
This work paved the way for modern forecasting, the principles of which are still based on the same approach and the same mathematics, although models today are much more complex and predict many more variables. Nowadays, a weather forecast typically consists of multiple runs of a weather model. Operational weather centres usually run a global model with a grid spacing of around 10km, the output of which is passed to a higher-resolution model running over a local area.
To get an idea of the uncertainty in the forecast, many weather centres also run a number of parallel forecasts, each with slight changes made to the initial snapshot. These small changes grow during the forecast and give forecasters an estimate of the probability of something happening — for example, the percentage chance of it raining. The supercomputer age has been crucial in allowing the science of weather forecasting and indeed climate prediction to develop.
Modern supercomputers are capable of performing thousands of trillions of calculations per second, and can store and process petabytes of data. This means we have the processing power to run our models at high resolutions and include multiple variables in our forecasts.
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