Sentences with phrase «predict weather like»

Now, they predict weather like this every once in a while and it never comes so we shall see what happens.

Not exact matches

For bankers it is much the same because in some ways trying to understand the economy is like trying to predict the weather.

But hurricanes are also influenced and steered by massive global trends in weather that are hard to predict: The warming or cooling of waters in the Pacific (El Niño and La Niña) and patterns like the Madden - Julian oscillation (an eastward - moving weather system that circles the globe every month or so and makes thunderstorms more likely) all play a role.

Like the nightly weather girl telling me she can predict the weather.

Discounting evolution becuase it does not explain abiogenesis is like discounting the belief gases because it does not predict the weather.

by Jerry Pallotta and illustrated by David Biedrzycki, we take a look at what might happen if some other types of animals decide that they would like to try and predict and influence the weather.

We did a lot of Christmasy things like putting our tree up which was good, and they predicted wild weather but so far it's staying away which is nice.

Much like actual bad weather being predicted, you can plan accordingly for storms with toddlers.

«It's like predicting the weather,» says Hulot.

If that's true in the Amazon, Saleska says, climate scientists will need to take into account practices like deforestation when predicting regional changes in weather patterns.

«This is clearly an important piece of evidence for the puzzle of trying to detect and predict global weather and climate patterns like the PDO,» she says.

Science, oddly, is a lot better at predicting things like the death of stars than next week's weather.

Trying to grasp this space weather without understanding the corona is like predicting hurricanes without factoring in the ocean, Guhathakurta says.

Scientists at the Department of Energy's Pacific Northwest National Laboratory are applying atmospheric science research capabilities to improve our understanding of long - term weather trends and better predict extreme weather events like these — and it all starts with studying clouds.

The models that were used in the National Ignition Campaign are essentially the same, at their guts, as the models we're using now — but in that case there was an extrapolation using the model that went too far, like a weatherman's model trying to predict the weather six months from now rather than just next week.

MIT says pretty well every big storm could have been predicted by a meteorologist like Jeff Masters, if given good weather data.

The forecast savvy people are predicting that the snow will melt away by the end of this week and that the weather will warm up a little before Narnia like snow storms come back again.

We did a lot of Christmasy things like putting our tree up which was good, and they predicted wild weather but so far it's staying away which is nice.

It can be difficult to predict what the weather will be like when getting dressed, so it's been fun to to be able to play with layers.

Much like predicting the weather, it is often hard to know what each day is going to bring.

Like predicting the weather, VAM is subject to many factors that influence the final result.

Some problems, like bad weather, air traffic delays, and mechanical issues, are hard to predict and often beyond the airlines» control.

It's kind of like retirement planning (or predicting long - range weather forecasts)-- you can use good estimates but I'm not sure how much value you get from being too detailed.

Like any surf destination, you never can truly predict what the weather will deliver on any given week.

Once the stars come out, you can enjoy the Milky Way and the Inca constellations — just like the Incan astronomers did to predict the weather.

Secondly, we don't have full information about the current conditions, and so, like for weather forecasts, if there are aspects of climate change that are chaotic, we can't predict those over the long term.

This is distinct from and more subtle than the usual «chaos fallacy» which reads something like «We can't predict next week's weather, so how can we make global warming forecasts 100 years out?

The working hypothesis is that even if the climate system may have the possibility of long - term chaos, it is nonetheless more like William's example of what happens when you change a parameter of the Lorentz model, than it is like the problem of predicting a single day's weather a year ahead.

It's just that the noise within the models is not correlated in time with the real noise; getting that right would be like predicting the weather several years out.

A weather forecast is like the attempt to predict where the next bubble is going to rise (physically this is an initial value problem).

Its like accurately predicting short term weather!

So — what do you do about things like weather fronts, that can't be predicted more than a few days ahead but have significant local effect?

The study will use a combination of complex computer models to replicate past weather patterns in the Atlantic Ocean, Caribbean Sea and Gulf, and use the results, along with estimates of future production of man - made greenhouse gases like carbon dioxide and methane to predict Gulf hurricane activity.

``... Emanuel says, and (Lorenz) made it clear that even if tracing the effects of small things is too hard to let anyone predict the weather a month ahead, the effects of large things, like the increase of carbon dioxide in the atmosphere, are not hard to discern.»

Road weather models predict the future road conditions, like road surface temperature and the slipperiness of the road.

Climate models are like weather models for the atmosphere and land, except they have to additionally predict the ocean currents, sea - ice changes, include seasonal vegetation effects, possibly even predict vegetation changes, include aerosols and possibly atmospheric chemistry, so they are not like weather models after all, except for the atmospheric dynamics, land surface, and cloud / precipitation component.

You can't predict both that well but by initializing both you can get a reasonable fit for some time period much like re-calibrating a weather model.

As climatologist Gavin Schmidt jokes, there is a simple way to produce a perfect model of our climate that will predict the weather with 100 percent accuracy: first, start with a universe that is exactly like ours; then wait 14 billion years.

From this, they further predicted the amount of energy that could be produced from weather - related energy sources like onshore and offshore wind turbines, solar photovoltaics on rooftops and in power plants, concentrated solar power plants and solar thermal plants over time.

The argument that, because GCMs produce «weather - like» behavior (and sometimes demonstrate short - term predictive skill), that they are therefore useful in predicting climate is totally bogus and unscientific.

I like to think of the climate as a metronome; the pivot at bottom of the pendulum is the short multidecadal time scales scientists measure and at the top the weight attached to the pendulum is the longer millenia time scale, as the pendulum is swinging back and forward, climate scientists take measurements from the pivot and try to predict what direction the pendulum is swinging, as the faster moving pivot is a short multidecadal time scale, this is considered the natural variability of weather.

Kind of like meterologists who sell their predictions for a living use ocean cycles to predict the weather?

In addition to providing details on the evolving cloud population critical to the MJO, the findings may also help illuminate how the MJO interacts with other climate patterns, like El Niño, allowing scientists to better predict and prepare for weather events around the world.

Its creators say it could help predict places where extreme weather events like tornadoes could happen in the future.

If all scientists were to limit thier views only to 10 years of data, we would not know anything worthwhile about the climate other than it acts like weather and you can't predict it.

the version here goes something like models cant predict the weather how can they predict the climate..

I think last winter's weather would qualify as a black swan: totally unanticipated (and counter to expectations), with widespread adverse impacts (and in hindsight, we had the information if not the knowledge to predict something like this.)

Researchers are applying atmospheric science research capabilities to improve our understanding of long - term weather trends and better predict extreme weather events like these — and it all starts with studying clouds.

As for «supposedly caused», it is exactly the sort of drought that climate scientists have predicted for a generation would result from AGW, it is clearly linked to AGW - driven changes in weather patterns, it continues to worsen and spread as I write, and it is much like similar mega-droughts already affecting major agricultural regions all over the world.

But if we want to use climate models to predict the weather, that's a lot more like me trying to predict how my difference amplifier will operate given every possible input condition and variable.