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Movie Review: Cloudy with a Chance of Meatballs
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Analytics and performance cookies: these cookies help us collect statistical and analytical usage to help up analyze website usage. Depending on your social media setting, the social media network will have record of this and may display your name or identifier in relation to this action. Advertising and targeted advertising cookies: these cookies track your browsing habits and location to provide you with advertising in line with your interests. The following year, however, the Great War arrived. At age 32, with his important research ongoing, Richardson could have kept to his agreeable job.
Yet even as his principles would not permit him to serve in the military, he still felt he should take part in the war. A few weeks later, he and his slide rule, notes, and instruments were at the front. Over six weeks in , with a bale of hay for his desk, Richardson patiently solved equation after equation for hundreds of variables. Even in , one had to go pretty far to find places in the United States that were not disturbed by people. After a good deal of searching, Robert Paine, a newly appointed assistant professor of zoology at the University He chose the weather over Central Europe on May 20, —a date for which Bjerknes had already published a trove of data about temperature, humidity, barometric pressure, and wind speed.
Cloudy with a Chance of Meatballs (film)
Richardson created a map of the atmosphere over the region, split into 25 equal-sized cells with sides of about miles. Every block was further divided into five layers with about the same mass of air in each layer. Because atmospheric density decreases with altitude, these layers were divided at heights of 2, 4, 7, and 12 kilometers above the ground. Richardson divided the 25 big blocks into two types: P cells, for which he recorded the atmospheric pressure, moisture and temperature; and M cells, for which he calculated wind speed and direction.
He alternated P and M cells on his grid, creating a sort of checkerboard. For example, wind speed in an M cell could be deduced by pressure changes in the P cells that surrounded it. Plugging all the available data from 7 a. He had also predicted a freakish rise in atmospheric pressure over Munich.
In fact, the barometer had held steady that day. Many scientists, then and now, would not publish such a resounding dud of an experiment. And he believed the merit of the method would be obvious even if the first practical application needed work. When he published Weather Prediction by Numerical Processes in , he described his disappointing results, in great detail.
Or perhaps the gaps between measuring stations were too great the finite-difference method needed sufficiently fine-grained data to approximate the continuous changes in weather, just as an adequate number of snapshots would be needed to portray the continuous motion of a ball. Another suspect was the need to interpolate data in cells where it was lacking. In fact, a few years ago, Peter Lynch of the Irish Meteorological Service showed that the trouble was simply that data-collection methods failed to correct for minor noise in the data.
B ut there was another source of potential error, which Richardson realized required further research: the turbulence that knocked air out of predictable paths, sending eddies of air up or down or sideways, where they banged into other eddies, passing energy from whirl to whirl. Air that gets knocked around like this further disturbs the flow of things by creating smaller local currents that move in the opposite direction from the main stream.
Finally, at the smallest scale, too little energy is left to overcome viscosity—the resistance to motion created by friction between individual molecules of air. Turbulent eddies in this layer are crucial to weather prediction, because they carry heat and moisture up into the higher atmosphere and down toward the surface of the earth, shaping the weather. For instance, Richardson had observed how fluctuations in wind speed seemed to depend on the difference in wind speeds at different heights and the difference in temperature at those heights. When ground temperature fell, resulting in a greater difference between ground temperature and temperatures higher up, wind fluctuations became less frequent.
He concluded this was due to buoyancy forces caused by eddies moving through different temperature zones interacting with eddies moving through regions of different wind speeds. He devised an equation to predict the occurrence of turbulence based on a ratio of these two effects. Where others looked at war and saw only unpredictable turbulence that math could not master, Richardson saw measurable quantities and inexorable laws.
That ratio of heat energy to wind energy, now called the Richardson number, is used today to predict where turbulence will occur in both the atmosphere and the ocean. When the ratio is high, warmer air is adding energy, making more and larger eddies.
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In other words, for turbulence, scale does not matter, as the same patterns are seen in the smallest and largest instances. Of course, we experience turbulence differently when it is blowing a dust mote around than when it is creating a massive storm. Turbulence is, in fact, in the eye of the beholder. A pilot flying through eddies too small to bump his plane around will not notice them—the effect of all the tiny eddies is averaged out into a general sense that the ride is smooth. On the other hand, neither will he notice an enormous eddy that enfolds the entire plane, any more than a fish would notice the water in which it swims.
It was too difficult to do in real time, many thought, and it had not produced an accurate prediction. His proposal languished for decades before technology caught up with it. Only after the advent of computers capable of doing quick calculations did his numerical-process approach become the standard method for forecasting. Today, his technique remains the basis for weather forecasts and climate modeling. Mandelbrot knew this firsthand.
In his quest to understand the role national boundaries play in causing wars, Richardson had written a paper on the difficulties of measuring national coastlines. He did not realize he was wrestling with the fact that coasts are fractals—twisty irregular shapes that are the same at any scale, from an inch to a mile. Mandlebrot, however, saw that Richardson had supplied him with a practical case for the importance of such fractals, and wrote his first major paper on the subject as a result. T hough Richardson had returned to work for the Meteorological Office in , he only stayed for one year.
In , a government reorganization placed the agency under the Air Ministry, which was in charge of the Royal Air Force. As his conscience would not allow him to work for any military organization, Richardson felt duty bound to resign. By the end of the s, he had enrolled in university to study psychology. In the next few years, war replaced the weather as his main focus.
For two decades before his death in , Richardson painstakingly collected data about arms races, economic upheavals, insurgencies, revolutions, riots, and combat. He had these deep ideas, but he was all the time looking at data. Where others looked at war and saw only unpredictable turbulence that math could not master, Richardson was once again looking for measurable quantities and inexorable laws that could be modeled with equations. As with his weather work, he was producing papers, but also working toward another magnum opus.
After all, then as now, most of what passes for analysis of wars and conflict consists of talk about one individual eddy or another in the vast political atmosphere. His theory, by contrast, would offer a way to step out of our local turbulence, and see the larger patterns.
As he had in meteorology, Richardson sought hard data—measurements that would not vary with the politics or passion of the observer. Any interpretation would be clouded by bias.
Instead, he would simply count the dead.