美国国家公共电台 NPR How The Advance Weather Forecast Got Good

 

LULU GARCIA-NAVARRO, HOST:

A six-day weather forecast now is as good as a two-day forecast was in the 1970s. We are in the golden age of weather forecasting. Andrew Blum has written about how we got here. He's the author of "The Weather Machine: A Journey Inside The Forecast." He says the spark for the book came when he received a warning about Superstorm Sandy long before it came ashore¹ on the East Coast.

ANDREW BLUM: It wasn't the expert hurricane forecasters, you know, sort of putting the pieces together in their minds. It was really the outputs of these computer models that they're responding to.

GARCIA-NAVARRO: Blum went deep on how forecasts get made but also why climate change has made them essential today.

BLUM: The funny thing about the topic of the weather is there's both incredibly banal² moments - you know, getting caught in the rain, the canceling picnics. And then it swings to these incredible extremes, you know, with millions of people displaced, with billions of dollars of damage. And the weather models and the forecasts have to serve both.

And it's remarkable³ to me as well that, you know, one of the things that's happened as the sort of scale in the system has shifted to the computers is that it's no longer bound by past experience. It's no longer sort of the meteorologists say, well, this happened in the past, you know, we can expect it to happen again. We're more ready for these new extremes because we're not held down by past expectations.

GARCIA-NAVARRO: So explain to me how this system actually works.

BLUM: Well, the first thing is to know what the weather is so you can know what the weather will be. That's the crux⁴ of it. So you need as complete observations of the global atmosphere as possible, which means coming from satellites and weather buoys⁵ and from sensors⁶ and airliners⁷. And then once you know what it is, what you can do is then begin to run it forward in time.

But rather than just, you know, sort of being plugged into the supercomputers - you know, in comes the present, and out comes the future - the models are really a kind of ongoing⁸ concern. Every six hours, every 12 hours, they compare their own forecast with the latest observations. And so the models in reality are kind of - you know, they're sort of dancing together, where the model makes a forecast, and it's corrected slightly by the observations that are coming in.

GARCIA-NAVARRO: And does anyone own these models and these satellites? I mean, is it part of a global web? Or does America have a certain piece of this and every country sort of have their own proprietary⁹ information that somehow gets passed along?

BLUM: Well, it's definitely run by individual nations but individual nations with their systems tied together. And that happens both geographically¹⁰ but also temporally. You know, one of the key tools for observations that feed the weather models are the polar-orbiting satellites. It's so well-integrated that the European polar orbiters cover the earth in each local time's morning, and the American orbiters come in the afternoon.

I mean, that's a sort of pattern that has been essential to making sure that, you know, we have the latest observations from every part of the earth's atmosphere. It's about this sort of continually humming system to step forward in time and correct slightly and, you know, keep spitting out a changing forecast that hopefully gets better and better.

GARCIA-NAVARRO: And so up until now, it's been governments that have been obviously calculating the weather for the greater good. But you call privatization a major threat to weather forecasting. Explain.

BLUM: Yes, it's a 150-year-old system of governments collaborating¹¹ with each other as a global public good. You know, in the '60s, it was something that President Kennedy saw as a sort of counterpoint to the space race. You know, meteorology was a way that governments could collaborate¹². But more recently, we potentially have a kind of bifurcation. You know, we potentially have forecasts for the haves and forecasts for the have-nots.

It hasn't come yet, but you can sort of see everybody gathering¹³ around, you know, recognizing that while before this was too expensive for anyone but governments to do - you know, no private company was going to spend, you know, 20, $30 million on a supercomputer - when you look at the amount of money at stake with different weather extremes, now that equation has changed. And there is the potential for profit. And companies are working hard to capture it.

GARCIA-NAVARRO: When you say it's about the have and have-nots, how might that play out? Give me an example.

BLUM: Well, the positive example from last month was with Cyclone¹⁴ Fani in India. And this was a very similar storm one 20 years ago that tens of thousands of people had died. This time around, the forecast came far enough in advance and with enough confidence that the Indian government was able to move a million people out of the way. So there was a global forecast that had sort of an immediate¹⁵ impact in a part of the world that does not have the most sophisticated weather service.

The alternative would be, you know, if you had a hurricane coming down in Florida, for example, and you had private forecasting services that said, you know, we can predict this with an extra day ahead of time, allowing for people to evacuate¹⁶ who have access to that information. And that information becomes a commodity rather than a public good.

GARCIA-NAVARRO: Now that you know so much about the weather and the way that it works and its importance to us, I mean, what keeps you up at night?

BLUM: I mean, Sandy was a scary scenario¹⁷. But the implication is that it's not the worst possible. You know, its impacts were localized. Even in parts of New York I remember, you know, waking up the next morning. And in the neighborhood that I live, you know, things were basically normal, which was not the case three miles away.

So I think for me, you know, what keeps me up is the idea of a perfectly¹⁸ forecast storm that is catastrophic on a broader scale and that we see coming for six days ahead and need to make decisions - not just as individuals, but as a society or as a city - how to move and respond. And I think, you know, that anticipation¹⁹ is new. You know, we haven't had that capability²⁰ before. And we have it now. And the next step is to figure out how to use it properly.

GARCIA-NAVARRO: Andrew Blum is the author of "The Weather Machine: A Journey Inside The Forecast."

Thank you so much.

BLUM: Thank you.

(SOUNDBITE OF FOUR TET'S "LUSH")

1 ashore   

adv.在(向)岸上，上岸

参考例句：

• The children got ashore before the tide came in.涨潮前，孩子们就上岸了。
• He laid hold of the rope and pulled the boat ashore.他抓住绳子拉船靠岸。

2 banal   

adj.陈腐的，平庸的

参考例句：

• Making banal remarks was one of his bad habits.他的坏习惯之一就是喜欢说些陈词滥调。
• The allegations ranged from the banal to the bizarre.从平淡无奇到离奇百怪的各种说法都有。

3 remarkable   

adj.显著的，异常的，非凡的，值得注意的

参考例句：

• She has made remarkable headway in her writing skills.她在写作技巧方面有了长足进步。
• These cars are remarkable for the quietness of their engines.这些汽车因发动机没有噪音而不同凡响。

4 crux   

adj.十字形；难事，关键，最重要点

参考例句：

• The crux of the matter is how to comprehensively treat this trend.问题的关键是如何全面地看待这种趋势。
• The crux of the matter is that attitudes have changed.问题的要害是人们的态度转变了。

5 buoys   

n.浮标( buoy的名词复数 )；航标；救生圈；救生衣v.使浮起( buoy的第三人称单数 )；支持；为…设浮标；振奋…的精神

参考例句：

• The channel is marked by buoys. 航道有浮标表示。 来自《现代汉英综合大词典》
• Often they mark the path with buoys. 他们常常用浮标作为航道的标志。 来自辞典例句

6 sensors   

n.传感器，灵敏元件( sensor的名词复数 )

参考例句：

• There were more than 2000 sensors here. 这里装有两千多个灵敏元件。 来自《简明英汉词典》
• Significant changes have been noted where sensors were exposed to trichloride. 当传感器暴露在三氯化物中时，有很大变化。 来自辞典例句

7 airliners   

n.客机，班机( airliner的名词复数 )

参考例句：

• The fog grounded the airliners. 大雾迫使班机停飞。 来自《简明英汉词典》
• They placed very stable and accurate atomic clocks on regularly scheduled jet airliners. 他们将非常稳定、准确的原子钟装在定期飞行的喷气式班机上。 来自辞典例句

8 ongoing   

adj.进行中的，前进的

参考例句：

• The problem is ongoing.这个问题尚未解决。
• The issues raised in the report relate directly to Age Concern's ongoing work in this area.报告中提出的问题与“关心老人”组织在这方面正在做的工作有直接的关系。

9 proprietary   

n.所有权，所有的；独占的；业主

参考例句：

• We had to take action to protect the proprietary technology.我们必须采取措施保护专利技术。
• Proprietary right is the foundation of jus rerem.所有权是物权法之根基。

10 geographically   

adv.地理学上，在地理上，地理方面

参考例句：

• Geographically, the UK is on the periphery of Europe. 从地理位置上讲，英国处于欧洲边缘。 来自辞典例句
• All these events, however geographically remote, urgently affected Western financial centers. 所有这些事件，无论发生在地理上如何遥远的地方，都对西方金融中心产生紧迫的影响。 来自名作英译部分

11 collaborating   

合作( collaborate的现在分词 )； 勾结叛国

参考例句：

• Joe is collaborating on the work with a friend. 乔正与一位朋友合作做那件工作。
• He was not only learning from but also collaborating with Joseph Thomson. 他不仅是在跟约瑟福？汤姆逊学习，而且也是在和他合作。

12 collaborate   

vi.协作，合作；协调

参考例句：

• The work gets done more quickly when we collaborate.我们一旦合作，工作做起来就更快了。
• I would ask you to collaborate with us in this work.我们愿意请你们在这项工作中和我们合作。

13 gathering   

n.集会，聚会，聚集

参考例句：

• He called on Mr. White to speak at the gathering.他请怀特先生在集会上讲话。
• He is on the wing gathering material for his novels.他正忙于为他的小说收集资料。

14 cyclone   

n.旋风，龙卷风

参考例句：

• An exceptionally violent cyclone hit the town last night.昨晚异常猛烈的旋风吹袭了那个小镇。
• The cyclone brought misery to thousands of people.旋风给成千上万的人带来苦难。

15 immediate   

adj.立即的；直接的，最接近的；紧靠的

参考例句：

• His immediate neighbours felt it their duty to call.他的近邻认为他们有责任去拜访。
• We declared ourselves for the immediate convocation of the meeting.我们主张立即召开这个会议。

16 evacuate   

v.遣送；搬空；抽出；排泄；大(小)便

参考例句：

• We must evacuate those soldiers at once!我们必须立即撤出这些士兵！
• They were planning to evacuate the seventy American officials still in the country.他们正计划转移仍滞留在该国的70名美国官员。

17 scenario   

n.剧本，脚本；概要

参考例句：

• But the birth scenario is not completely accurate.然而分娩脚本并非完全准确的。
• This is a totally different scenario.这是完全不同的剧本。

18 perfectly   

adv.完美地，无可非议地，彻底地

参考例句：

• The witnesses were each perfectly certain of what they said.证人们个个对自己所说的话十分肯定。
• Everything that we're doing is all perfectly above board.我们做的每件事情都是光明正大的。

19 anticipation   

n.预期，预料，期望

参考例句：

• We waited at the station in anticipation of her arrival.我们在车站等着，期待她的到来。
• The animals grew restless as if in anticipation of an earthquake.各种动物都变得焦躁不安，像是感到了地震即将发生。

20 capability   

n.能力；才能；(pl)可发展的能力或特性等

参考例句：

• She has the capability to become a very fine actress.她有潜力成为杰出演员。
• Organizing a whole department is beyond his capability.组织整个部门是他能力以外的事。