新概念美音版第四冊 Lesson 14 The Butterfly Effect 蝴蝶效應

 

 

Lesson 14   The Butterfly Effect 蝴蝶效應

First listen and then answer the following question.

聽錄音，然後回答以下問題。

Why do small errors make it impossible to predict the weather system with a high degree of accuracy?

    Beyond two or three days, the world's best weather forecasts are speculative, and beyond six or seven they are worthless.

    The Butterfly Effect is the reason. For small pieces of weather -- and to a global forecaster, small can mean thunderstorms and blizzards -- any prediction deteriorates rapidly. Errors and uncertainties multiply, cascading upward through a chain of turbulent features, from dust devils and squalls up to continent-size eddies that only satellites can see.

    The modern weather models work with a grid of points of the order of sixty miles apart, and even so, some starting data has to guessed, since ground stations and satellites cannot see everywhere. But suppose the earth could be covered with sensors spaced one foot apart, rising at one-foot intervals all the way to the top of the atmosphere. Suppose every sensor gives perfectly accurate readings of temperature, pressure, humidity, and any other quantity a meteorologist would want. Precisely at noon an infinitely powerful computer takes all the data and calculates what will happen at each point at 12.01, then 1202, then 12.03…

    The computer will still be unable to predict whether Princeton, New Jersey, will have sun or rain on a day one month away. At noon the spaces between the sensors will hide fluctuations that the computer will not know about, tiny deviations from the average. By 12.01, those fluctuations will already have created small errors one foot away. Soon the errors will have multiplied to the ten-foot scale, and so on up to the size of the globe.

                        JAMES GLEICK, Chaos

New words and expressions 生詞和短語

    forecast
n.  預報
    speculative
adj. 推測的
    blizzard
n.  暴風雪
    deteriorate
v.  變壞
    multiply
v.  增加
    cascade
v.  瀑布似地落下
    turbulent
adj. 狂暴的
    dust devil
    小塵暴，塵旋風
    squall
n.  暴風
    eddy
n.  旋渦
    grid
n.  坐標方格
    sensor
n.  傳感器
    humidity
n.  溫度
    meteorologist
n.  氣象學家
    Princeton
n.  普林斯頓（美國城市名）
    New Jersey
n.  新澤西（美國州名）
    fluctuation
n.  起伏，波動
    deviation
n.  偏差

參考譯文

    世界上最好的兩三天以上的天氣預報具有很強的猜測性，如果超過六七天，天氣預報就沒有了任何價值。

    原因是蝴蝶效應。對於小片的惡劣天氣 -- 對一個全球性的氣象預報員來說，“小”可以意味著雷暴雨和暴風雪 -- 任何預測的質量會很快下降。錯誤和不可靠性上升，接踵而來的是一係列湍流的徵狀，從小塵暴和暴風發展到隻有衛星上可以看到的席卷整塊大陸的旋渦。

    現代氣象模型以一個坐標圖來顯示，圖中每個點大約是間隔60英裏。既使是這樣，有些開始時的資料也不得不依靠推測，因為地麵工作站和衛星不可能看到地球上的每一個地方。假設地球上可以布滿傳感器，每個相隔1英尺，並按1英尺的間隔從地麵一直排列到大氣層的頂端。再假定每個傳感器都極極端準確地讀出了溫度、氣壓、溫度和氣象學家需要的任何其他數據。在正午時分，一個功能巨大的計算機搜集了所有的資料，並算出在每一個點上12：01、12：02、12：03時可能出現的情況。

    計算機無法推斷出1個月以後的某一天，新澤西州的普林斯頓究竟是晴天還是雨天。正午時分，傳感器之間的距離會掩蓋計算機無法知道的波動、任何偏平均值的變化。到12：01時，那些波動就已經會在1英尺遠的地方造成偏差。很快這種偏差會增加到尺10英的範圍，如此等等，一直到全球的範圍。