Winter Weather Predictions
Winter was knocking at my door! We had the first frost for this season and we know that Winter is on its way. Which kind of Winter will we get? The brutally cold one with the Arctic Vortex like last year? Or a little snow with warmer spells and icy driveways? Well, what do the experts say?
The Farmers’ Almanac (the 1800’s plus old reference for rural families) is calling in its extended forecast for another freezing, frigid, and frosty winter for two-thirds of the country: https://www.farmersalmanac.com/extended-forecast
Then you can rely on the weather people on your television stations. I found the opinions of six forecasters: https://www.nj.com/weather/2019/10/snowy-winter-on-the-way-heres-what-6-forecasters-predict-for-nj.html
This shows me that nobody really knows, and therefore everybody has come to a different conclusion. But we do have lots of weather satellites and computer programs and should be able to make a correct forecast- right? Even the weather people work with different programs. Of the many competing models they use, here in the Northeast, they frequently admit that the ‘European’ model says something else- often more accurately! We’ll get to why this is, shortly.
The Farmers’ Almanac (the 1800’s plus old reference for rural families) is calling in its extended forecast for another freezing, frigid, and frosty winter for two-thirds of the country: https://www.farmersalmanac.com/extended-forecast
Then you can rely on the weather people on your television stations. I found the opinions of six forecasters: https://www.nj.com/weather/2019/10/snowy-winter-on-the-way-heres-what-6-forecasters-predict-for-nj.html
This shows me that nobody really knows, and therefore everybody has come to a different conclusion. But we do have lots of weather satellites and computer programs and should be able to make a correct forecast- right? Even the weather people work with different programs. Of the many competing models they use, here in the Northeast, they frequently admit that the ‘European’ model says something else- often more accurately! We’ll get to why this is, shortly.
Now the woolly bear caterpillar makes its entrance! Years ago, our farmer told us to look at the brown stripes of the woolly bear caterpillars. The more brown I saw, the milder the Winter would be. But there is a problem with this method. We have a lot of those caterpillars and they all look different.
The woolly caterpillars are the larvae of the Isabella Tiger Moth. In the Fall they are looking for a safe dark place to hibernate as near frozen caterpillars. They produce a natural antifreeze to protect them from ice and the cold winter weather.
Back in the days without weather satellites, people relied on what nature was telling them. And the woolly bear caterpillar locally became the meteorologist that could predict how severe the approaching winter would be. The more brown stripe it showed, the milder Winter would be. When people can’t understand why something happens, they automatically look for clues around them to explain and predict everything. Sometimes they have more success than others.
The truth is that the woolly caterpillar can tell you a weather story, but not the one of the approaching Winter. It tells you how the weather was last Spring, during its growing season. When the woolly caterpillar thaws, it builds a cocoon around itself and after some weeks the Isabella Tiger Moths is born. They are active during the night for a couple of days. They mate, lay eggs and die. And the caterpillar cycle starts again. The warmer the Spring is, the wider the stripe will be. And of course, how wide the central brown stripe appears to you as you watch them in the Fall, is very subjective.
There are other folklore predictions. Gorgeous Fall colors predict a severe Winter. Wrong! The color of the leaves is an indication of the current and past weather and not the Winter to come. (see our post of October 2015)
Another one is the prediction of Winter by timing the bird migrations or how many nuts the butternut tree produces. The list of wrong forecasts is long.
The truth is that the woolly caterpillar can tell you a weather story, but not the one of the approaching Winter. It tells you how the weather was last Spring, during its growing season. When the woolly caterpillar thaws, it builds a cocoon around itself and after some weeks the Isabella Tiger Moths is born. They are active during the night for a couple of days. They mate, lay eggs and die. And the caterpillar cycle starts again. The warmer the Spring is, the wider the stripe will be. And of course, how wide the central brown stripe appears to you as you watch them in the Fall, is very subjective.
There are other folklore predictions. Gorgeous Fall colors predict a severe Winter. Wrong! The color of the leaves is an indication of the current and past weather and not the Winter to come. (see our post of October 2015)
Another one is the prediction of Winter by timing the bird migrations or how many nuts the butternut tree produces. The list of wrong forecasts is long.
In spite of many weather satellites and complicated software programs running on supercomputers, it is extremely hard to make an accurate weather prediction even for one day. So why is it so difficult? Well, in essence, it is the ‘Butterfly Effect’! As early as the late 1700’s, french mathematician and philosopher Pierre-Simon Laplace, who championed Newtonian Laws of motion, espoused the notion that Newton’s Laws- given a known starting state, could predict the future location of everything from stars to atoms. With a ‘sufficient’ intelligence (meaning the ability to make all the necessary calculations), one could predict the weather in the future. Unfortunately, the amount of data and the millions of calculations that would be needed to do this, was out of reach of the abilities of even the smartest humans.
John von Nuemann Indeed, when John Von Nuemann designed his first computers in the 1950’s at the Institute for Advanced Study in Princeton, NJ, he believed that the time had finally come when we would have a machine that would be ideally suited for the job. Given enough memory and speed, computers could perform the millions of calculations needed to make long range predictions. Many scientists were hired by governments, militaries and institutions to make this work. The economic impacts were huge and the financial incentives immense!
Many believed that we would even be able to control weather, making rain by seeding clouds, or steering air masses, blocking or dissipating gathering storms. Yet, many decades later, we are only marginally better than when we decided to apply science, rather than listening to the woolly caterpillar.
Many believed that we would even be able to control weather, making rain by seeding clouds, or steering air masses, blocking or dissipating gathering storms. Yet, many decades later, we are only marginally better than when we decided to apply science, rather than listening to the woolly caterpillar.
The reason is that we were making a fundamental assumption about the laws of nature that was incorrect. No set of measurements can be completely accurate. They are only as accurate as your ruler can measure. So all models will have approximate information to use for making approximate predictions. It was believed that very small differences in measurements were insignificant on the grander scales of a model, and there would be a convergence of outcomes in the end, as these small variations (below the measuring threshold) balanced each other out or faded away and disappeared. Afterall, our predictions of the recurrences of celestial events over grand time scales and guiding spacecraft to distant planets, appeared to be very accurate.
Our calculations worked! We sent spacecrafts to the ends of our solar system and beyond, over multi-year trips, accurately enough to take stunning photographs of worlds we may never reach! But yet, we could not make very accurate models of local weather on Earth five days out, even with supercomputers?
Our calculations worked! We sent spacecrafts to the ends of our solar system and beyond, over multi-year trips, accurately enough to take stunning photographs of worlds we may never reach! But yet, we could not make very accurate models of local weather on Earth five days out, even with supercomputers?
Finally, in 1961, mathematician Edward Lorenz was working on this problem when he serendipitously discovered the underlying mistake everyone was making. He was modeling long term forecasts using data points accurate to six decimal places (-.506127). That is down to one part per million! Because of long runtimes for these models, he decided to make a shortcut on one model by reentering the data only to three decimal places (-.506). He reasonably felt that a one part in a thousand rounding was inconsequential. Afterall, weather satellite data rarely can read ocean surface temperatures to that degree of accuracy. But when he reran the model, the forecasts now diverged wildly. It turns out that even tiny fluctuations in any point in a system can have great effects on an outcome. Hence, a butterfly flapping its wings in the Amazon, can cause a multiplying cascade of disturbances leading to a hurricane in Florida. Some credit this insight as being the foundation of the discipline of Chaos Theory.
Today’s models are certainly improving, but we still lack the raw horsepower to get to long term forecasting. Perhaps when we perfect quantum computing - using the laws of quantum mechanics (I’ve read that it may be very soon) and increase our calculation capacity by many orders of magnitude over binary code, it will be possible. Every world power (the U.S, China, Russia, Great Britain, France, Germany, India, etc.) is racing to be first in quantum computing, and the winner may control the world. So the stakes are very high.
But today, with our supercomputers and sensing technologies, we still can’t account for enough variables to make long term weather predictions. Of course, if I see a woolly bear caterpillar, I have a choice to take this guy as a forecaster or the other one nearby with a more favorable stripe.
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