Sentences with phrase «weather noise as»

Kleeman, R., Y. Tang, and A.M. Moore, 2003: The calculation of climatically relevant singular vectors in the presence of weather noise as applied to the ENSO problem.

Not exact matches

They also wrote of their fear of unknown noises — caused perhaps by ice cracking, animals, or the weather — and phenomena such as the aurora borealis.

You'll know it for the world's first Blind Spot Intervention System and other progressive technologies such as Intelligent All - Wheel Drive, Active Noise Control, and up to the date traffic and weather information.

As a result of testing, several design and engineering changes were implemented to reduce wind noise, vehicle noise, vibration and harshness and improve weather protection.

During the course, we will meet a variety of distractions and real - life situations such as: strollers, skateboarders, joggers, baby strollers, children running, other dogs (who aren't in class and may or may not be under the control of their owner), strangers who want to pet your dog (and might not ask you first), loud noises, and weather fluctuations.

Specifically, any weather noise over the last 30 years in the troposphere must hold for the surface as well.

(1) In this case even if they were correct and the models failed to predict or match reality (which, acc to this post has not been adequately established, bec we're still in overlapping data and model confidence intervals), it could just as well mean that AGW stands and the modelers have failed to include some less well understood or unquantifiable earth system variable into the models, or there are other unknowns within our weather / climate / earth systems, or some noise or choas or catastrophe (whose equation has not been found yet) thing.

As mentioned above, with a single realisation, there is going to be an amount of weather noise that has nothing to do with the forcings.

I would really like some clarity as to how the ensemble of model runs are whittled down into a narrower subset without comprimising the ability of the model to «span the full range» of «weather noise».

As the time scale increases, i.e. as we move from weather to climate, the underestimation inflates, as seen by comparing the HK and «white noise» curves in these graphAs the time scale increases, i.e. as we move from weather to climate, the underestimation inflates, as seen by comparing the HK and «white noise» curves in these graphas we move from weather to climate, the underestimation inflates, as seen by comparing the HK and «white noise» curves in these graphas seen by comparing the HK and «white noise» curves in these graphs.

As far as «climate» goes, a 30 year smooth reduces most on the weather noise giving you something to fit with a reasonable error range so you don't have to magnify some obscure signal by a factor of ten to get a «fit»As far as «climate» goes, a 30 year smooth reduces most on the weather noise giving you something to fit with a reasonable error range so you don't have to magnify some obscure signal by a factor of ten to get a «fit»as «climate» goes, a 30 year smooth reduces most on the weather noise giving you something to fit with a reasonable error range so you don't have to magnify some obscure signal by a factor of ten to get a «fit».

Nevertheless, the simplistic notion that climate variations consist of an analytic trend plus multidecadal periodic cycles hidden by higher - frequency «weather noise» remains endemic among those who feature themselves as «climate scientists.»

Meanwhile it does not answer the main point of my last post, which is that that most climatologists view this aspect of the earth's environment as «weather» (or statistical noise) and that if you measure temperatures for long enough periods of time (30 + years) the effect of clouds, rain and water vapour average out and a temperature trend signal will become apparent.

The study says the global ocean heat content record robustly represents the signature of global warming, and is affected less by weather - related «noise» and climate variability such as El Niño and La Niña events.

But when, as with the Antarctica weather station data we used, there is not only a lot of missing data and «noise» but also greatly time - varying patterns of missingness (which stations have data missing), ridge regression (both mridge and iridge) can be expected to, and does, perform significantly better than TTLS.

In fact, it is often referred to as «noise» or «weather».

The other model I looked at (The canadian one) didn't have as much «weather noise» in GMST.

Warming and cooling signals in weather noise is not so easy to determine as to the cause.

AR5 3.2.2.3 says of it «Overall, the SST data should be regarded as more reliable because averaging of fewer samples is needed for SST than for HadMAT to remove synoptic weather noise.

Enough time to overcome signal: noise difficulties and represent global climate data, as opposed merely weather - scale events averaged over the globe.

The reason is that many natural events such as weather events are not independent of one another IN TIME, i.e. they are «red noise» not «white noise».

Cortana also knows when you are traveling and collects relevant, useful information from your email, such as flight info, weather at your destination, currency conversion if you are traveling internationally, and then looks out for you, filters out the noise — and helps you stay on top of what's important.

As a true all - rounder, she is physically fit and comfortable working in a garage which has dirt, odours, noise and adverse weather conditions.

It's a network of interactive sensors collecting both passive data, such as weather and air quality, and data about how people are using the area, by measuring ambient noise and counting nearby Wi - Fi — and Bluetooth - enabled devices (without identifying users).