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Ocean surface temperatures on April 6 2015. Credit: Kevin Gill/Flickr
16 January 2015 11:40

Roz Pidcock

Roz Pidcock

Global temperature Explainer: How do scientists measure global temperature?

Every year around this time, there’s a flurry of activity in the world’s major meteorological agencies as they prepare to release official global temperature figures for the previous year.

This year, there’s particular interest as it looks likely 2014 will be the hottest year on record.

First out the blocks with the official data was the Japan Meteorological Agency (JMA). Earlier this month, it confirmed 2014 had taken the top spot with global temperatures 0.27 degrees Celsius above the long-term average. Today, it’s the turn of NASA and the US National Oceanic and Atmospheric Administration, with the UK Met Office following suit next week.

Why so many records? While global temperature is a simple enough idea, measuring it is harder than you might think. We take a look at how scientists measure global temperature.

To get a complete picture of Earth’s temperature, scientists combine measurements from the air above land and the ocean surface collected by ships, buoys and sometimes satellites, too.

The temperature at each land and ocean station is compared daily to what is ‘normal’ for that location and time, typically the long-term average over a 30-year period. The differences are called an ‘anomalies’ and they help scientists evaluate how temperature is changing over time.

A ‘positive’ anomaly means the temperature is warmer than the long-term average, a ‘negative’ anomaly means it’s cooler.

Daily anomalies are averaged together over a whole month. These are, in turn, used to work out temperature anomalies from season-to-season and year-to-year.

Scientists use four major datasets to study global temperature. The UK Met Office Hadley Centre and the University of East Anglia’s Climatic Research Unit jointly produce HadCRUT4 .

In the US, the GISTEMP series comes via the NASA Goddard Institute for Space Sciences (), while the National Oceanic and Atmospheric Administration () creates the MLOST record. The Japan Meteorological Agency ( ) produces a fourth dataset.

Here’s how global temperatures in the four datasets compare over the past 130 years. You can see they all show a warming trend, but there are some year-to-year differences too.

Since Articles for You mostly uses AMP (about 72% according to Chartbeat), that’s where you’re going to see the traffic if you’re using a separate Analytics property to track AMP traffic (as 2018 New Genuine leather Flats Designer sneakers men women Classic Casual Shoes python tiger bee Flower Embroidered Cock Love sneakers outlet store cheap price best sale online official cheap price 7WmWzv
) — just something to keep in mind. If you’re not using AMP, just make sure the property or report suite you’re using includes mobile traffic.

For this example, I used Google Analytics and navigated to Acquisition > Referrals. In the Source column looked for “” (you can find the equivalent report in Adobe Analytics by going to Traffic > Referrers). It should look similar to this:

Once you click on, it’ll show you the rest of the referral path:

That’ll tell you how much traffic you’re getting from Articles for You , but you’ll want to create a segment for this so you can use it in conjunction with other reports. Here’s how I made a new segment called “Chrome Content Suggestions”:

Before I dug into this, my initial thought was that article suggestions would be based on Google News settings. It makes sense, after all, since many Google News users have already taken the time to specify their interests and preferred sources. But from what I can tell, Google News doesn’t appear to play a role in these content suggestions. I digress to make this point because I think it’s strange that Google doesn’t leverage existing technology from Google News, but instead appears to be developing separate (and frankly superior) ways of surfacing personalized article content in parallel with Google News. I’ll save my speculation for another post, but I will say this: there’s starting to be an awful lot of redundancy between Google News and the AI-powered Google content discovery features we’re talking about here, and redundancy in business usually results in consolidation. Just sayin’.

So how does Google determine what to suggest? From my research and experimentation, there are two main types of article recommendations:

Personalized Trending

Since personalized recommendations are based on your activity in Google, they’ll populate automatically, with no need to provide explicit input. However, you can add interests manually by using the “Customize feed” options inside the Google App. If you do that, you’ll quickly see that any settings changes you make in the Google App will be reflected in Articles for You and the Cards in Feed and the Google App itself. Here’s what it looks like when you go to add a topic:

Now we get to the million dollar question: how exactly do you optimize for these article suggestion features? A few suggestions:

That last suggestion is important, so I want to dig into a little more. But first, some context…

Ultimately what we’re seeing here reflects Google’s continued evolution toward something that looks more like a personal assistant than a search tool . I think we’ll continue to see examples of this as Google tries to anticipate what people want to see, rather than waiting for them to search for it. Think about Google’s SERP evolution so far:

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