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A5 NOVEMBER 26–DECEMBER 2, 2015 |EPOCH WEEKLY

Place de la République

on Nov 18.

ALL PHOTOS BY DAVID VIVES/EPOCH TIMES

Taverne Karlsbräu near the Place de la République on Nov 18.

Brasserie du Palais de Tokyo in Paris on Nov 18.

Idrissa Diarro, manager at the Brasserie restaurant on the Eiffel Tower, Nov 18.

The Eiffel Tower on Nov 18, illuminated in blue, white and red as a sign of support to the victims. The city’s motto: ‘Fluctuat nec mer-gitur’ or ‘Battered by the waves, it does not sink’, was projected on the monument.

Gilles Robert, manager of

Taverne Karls-bräu, on Nov

18.

the attacks:“On Sunday, we were told

that we should not gather together. Finally the place was full. Then at around 7pm there was a false alarm, a stampede on the street and we had to let 30 people in; everyone moved away from the windows, the police were on the alert, it was very strange.”

Now the fear has turned to the potential impact on busi-ness. According to the Paris Tourism Office, after the attacks at Charlie Hebdo in January, hotels saw a drop in business of 7 to 8 per cent immediately, and up to 25 per cent in the two weeks that followed. Then, eve-rything returned to normal.

But this time, with the enhanced alert and having been targeted by terrorists, professionals in the industry

expect the worst. “Clearly, in the evening, there are fewer people. But for now, it is still fresh,” said Robert.

“Eighty per cent of our cli-ents are from abroad,” said Idrissa Diarro, manager at the Brasserie restaurant in the Eiffel Tower.

“Those who are here are afraid, and those who are abroad are apprehensive to come,” he said.

On the night of the attacks, there was also a stampede of panic in his bar.

But the manager wants to remain optimistic:

“We see that everybody is more united, everyone is more considerate, and many people are encouraging us. We will not close the shops or stay home, we will not stop living because of those people.”

The Quixotic Quest for the perfect weathermanBy Jonathan ZhouEpoch Times Staff

Like so many things in the mod-ern world, the origins of scien-tific weather forecasting lay in the Renaissance. In 1450, the German mathematician Nich-olas of Cusa first wrote down a description of the hygrom-eter, a scale that measures the amount of moisture in the air. Thirty years later, Leonardo da Vinci built a rough prototype of the device.

Nearly 200 years later, the Italian Evangelista Torricelli invented the barometer, which measures atmospheric pressure. The 19th century saw a leap forward in weather prediction as telegraphs enabled meteorological observations to be relayed across continents in

real time, and computers in the mid-20th century ushered in the large-scale number-crunching forecasting techniques that have come to dominate the profession.

The centuries-old craft is still being perfected today.

NASA’s latest effortsNASA is in the middle of the Olympex project near Seat-tle, deploying a panoply of meas-urement instruments – radars, weather balloons, and even a DC-8 flying laboratory, to fly through the clouds – to gather precise data. Other gauges are collecting data from the ground and even imaging and counting individual raindrops and snow-flakes to document as minutely as possible what different kinds of precipitation look like.

All of the data will be used to verify the rain and snow-fall observations made by the Global Precipitation Measure-ment satellite system, and to test if the assumptions mete-orologists make in interpreting those observations are actually correct.

With better calibrated data, meteorologists will be able to make better forecasts.

But don’t expect complaints about the weatherman to go away. The inherent unpredict-ability of Mother Nature prob-ably won’t be conquered in the coming decades – if ever.

Numerical weather predictionWhile NASA is working to col-lect more data, ironically, when Numerical Weather Prediction (NWP), the system presently used for day-to-day forecast-

ing, was first conceived in the 1920s, the problem was not too little information, but too much.

NWP models weather systems by breaking an area into square grids, then tries to deduce how each grid would be influenced by the sum of the environmen-tal variables of the adjacent grids – temperature, humidity, wind speeds and the like . Those val-ues are run through differen-tial equations based on the laws of physics and fluid dynamics to produce forecasts of future weather conditions.

When Lewis Fry Richardson, the inventor of NWP, first tried applying the technique, it took him 6 weeks to make (terribly inaccurate) forecasts 6 hours into the future.

For NWP to be successfully implemented, Richardson imag-ined thousands of technicians, filling up a whole theatre, man-ually performing calculations in sync, the entire group coor-dinated by a single man “like

the conductor of an orchestra.” Richardson’s symphony of “slide rules and calculating machines” mercifully never became a real-ity, as the task was outsourced to IBM mainframes.

Elusive omniscienceAlthough computational power grew through the decades, weather forecasting was still far from converging on omnis-cience, and in the 1960s the meteorologist Edward Lorenz formulated a theory for why it never will.

In 1961, while running NWP simulations, Lorenz rounded one variable from  .506127 to .506, producing drastic, unex-pected changes to the forecasts made. The lessons of this epi-sode, that seemingly insignifi-cant factors could play a deci-sive influence in the outcomes of large systems, laid the ground-work for chaos theory.

In 1972, Lorenz gave a talk titled “Predictability: Does

the Flap of a Butterfly’s Wings in Brazil Set Off a Tornado in Texas?” introducing “the butter-fly effect” to the popular lexicon.

“Chaos is an inherent prop-erty of the atmosphere, of nature itself, you can’t get around it.  That has implications for limits of predictability,” said David Gold, a senior forecaster at Weather Decision Technol-ogies Inc.

At present, Gold said that the best NWP forecasts can be reli-able only one week ahead, after which the accuracy starts to break down as a result of the errors in the initial conditions inputted into the model.

“At a certain point in the future, the forecast isn’t going to be any good. It doesn’t mat-ter what you do, nonlinearity has very serious consequences,” said Gold.

It’s possible that the NASA mission could offer marginal improvements to data collec-tion efforts, Gold said, but don’t expect the moon. However, a “not unreasonable” hope is that in the coming years, day 8 pre-dictions will be as reliable as day 5 predictions are today.

Combating chaosAside from fine-tuning the pre-cision of initial observations, meteorologists have sought to combat the chaotic side of weather systems with statistics.

Ensemble forecasting runs slight variations on the initial set of conditions to create a range of results, somewhat compen-sating the large errors in fore-casting that can originate from a minuscule error in the initial conditions: a large variance in

the spread of forecasts suggests that the prediction is highly uncertain, and vice versa.

A big picture perspective, in terms of both space and time, Gold said, also has much to offer forecasting. A much better read-ing of the structures of precipi-tation, such as rainfall patterns over the Indian Ocean, where often large errors are made, has the potential to produce “big gains” in forecasting because the errors degrade the forecast more slowly.

Finally, neural networks – cut-ting edge artificial intelligence that has recently made headlines for feats from writing your email replies to copying the painting styles of masters – could theoret-ically be fed historical data sets and learn how to correct for the biases in forecasting models, but its usefulness is conditional. The climate would have to remain stable, and we need to invent a time machine.

“Certain models have biases depending on the weather cli-mate regime we’re in. … If we try to use the historical data in a data mining exercise, from the 1960s and ’70s, the climate was very different back then, so hav-ing a long data record may not help you at all,” Gold said.

“A neural network based on historical records would need hundreds if not thousands of years of [quality] data,” he added.

Unfortunately, that’s data we just don’t have. So unless NASA can figure out how to crack chaos, we’ll have to accept highly educated, carefully calcu-lated best guesses.

A hydro-meteorological radar of weather forecast service Météo-France near Vars in the French Alps on June 26.

JEAN-PIERRE CLATOT/AFP/GETTY IMAGES

Chaos is an inherent property of the atmosphere, of nature itself, you can’t get around it. That has implications for limits of predictability.David Gold, senior forecaster, Weather Decision Technologies Inc.