Sentences with phrase «forecasts on time scales»

Climate models are being subjected to more comprehensive tests, including, for example, evaluations of forecasts on time scales from days to a year.

Not exact matches

Oct 30 (Reuters)- Hurricane Sandy appears to have easily caused more losses than last year's Hurricane Irene, but final totals will be hard to come by for some time because of the scale of the disaster, catastrophe forecasting companies said on Tuesday.

Predictions of weather and climate for months, seasons and decades ahead lie between normal weather forecasts and climate projections for the coming century — concerning both what influences the climate on these time - scales, and the methods required to make such predictions.

This approach complements traditional forecast simulations, which are very accurate for a short period of time but lose their reliability on timescales that are required to understand the fate of the spill on the scale from days to weeks.»

«We're good at placing these forecasts and probabilistic terms on a geological time scale, but we're not good at putting it at scales that matter to you and me.»

The interactions on intra-seasonal time scales represent a potential source of predictability for weather forecasting.

A few climate models have been tested for (and shown) capability in initial value predictions, on time scales from weather forecasting (a few days) to seasonal forecasting (annual).

Pressing the frontiers of climate science and related research is vital, but it's wishful thinking to expect further science to substantially narrow uncertainties on time scales that matter when it comes to regional or short - term climate forecasting, the range of possible warming from a big buildup of carbon dioxide, the impact of greenhouse forcing on rare extremes and the like.

If this forecast is correct, it will take a long time or big technological innovations on the production side to induce large - scale fossil - fuel production from high - cost areas such as the Arctic Ocean, regardless of sea - ice conditions.

So at best this is useful for seasonal forecasts, unless someone is forecasting sea ice extent on longer time scales.

From the perspective of business, weather forecasts on the sub-seasonal time scale provides an opportunity because it lies between the well - established application of daily weather forecasts and the increasing use of seasonal forecasts.

2) Soil moisture: memory in soil moisture can last several weeks which can influence the atmosphere through changes in evaporation and surface energy budget and can affect the forecast of air temperature and precipitation in certain areas during certain times of the year on intraseasonal time scales;

Many numerical weather prediction centers now use coupled ocean - atmosphere models to produce ensemble forecasts on the subseasonal time scale.

However, forecasting on the subseasonal time scale (two weeks to two months) has received much less attention, in part because this time horizon has been considered a «predictability desert».

We need to focus more on improving weather forecasting on longer time scales than the current 7 - 8 days so that vulnerable communities have more time to prepare for adversity.

The climate models got scores far worse than a random walk, indicating a complete failure to provide valid forecast information at the regional level, even on long time scales.

But forecasts of an increase in amplitude or even a phase reversal of the AO can lead to improved temperature forecasts on the weekly and monthly time scales.

Now if we say area B has a great effect on our forecast area A, and see which line with up with that using a time proportional scale like a few weeks and pick those ones, then haven't we done about the same thing?

Modelling on this time scale involves much the same techniques as in the longer - term climate forecasting.

What is more, she is trying to claim the models are wrong period by using data on a decadal time frame (full well knowing that they struggle on such time scales), but at the same time is informing CFAN clients that they can provide forecasts on a decadal time scale.

PROFESSIONAL SUMMARY * Experiencing machine learning real word project of MS Azure Subscription Automation (auto Scaling) for sequence to sequence time series forecasting, classifications and decision on MS Azure machine learning platform and storage blobs.