Sentences with phrase «weather prediction models with»

This is what makes paleoreconstructions possible, and what makes it possible to initialize global numerical weather prediction models with so few observations.)

Not exact matches

The method combines a model for systems such as weather or climate with real - world data points to develop predictions about the future.

Lapenta foresees a day in the next decade when the increasing capabilities of new radars and satellites will be coupled with an evolving generation of finely detailed weather - prediction models running in real time on computers at speeds exceeding a quintillion computations a second.

The statistics of the weather make short term climate prediction very difficult — particularly for climate models that are not run with any kind of initialization for observations — this has been said over and over.

The researchers compared predictions of 22 widely used climate «models» — elaborate schematics that try to forecast how the global weather system will behave — with actual readings gathered by surface stations, weather balloons and orbiting satellites over the past three decades.

Specific examples of additional impacts include a reduction in capital equipment acquisitions across the entire lab with computing alone sliding from $ 7 million to $ 3 million, the elimination of NCAR's lidar research facility as well as the extra-solar planet program, delays in computer modeling and prediction efforts for both weather and climate, reductions in the solar coronal observing program, a reduction in the number of post doctoral appointments, reduction of the societal impacts program, and widespread deferred maintenance and delays in equipment and instrument acquisition and replacement.

This claim is complemented with a broad literature synthesis of past work in numerical weather prediction, observations, dynamical theory, and modeling in the central U.S. Importantly, the discussion also distills some notoriously confusing aspects of the super-parameterization approach into clear language and diagrams, which are a constructive contribution to the literature.

This capability would enable a model to continuously update and improve parameterization approaches on the fly, with the potential to improve climate predictions and short - term weather forecasts.

More importantly, it is my understanding that weather is chaotic and that calculations with Numerical Weather Prediction (NWP) models / codes are consistent with that assumption.

There are some source of predictability that are still not fully resolved (including those dealing with improving climate models, but also related to unexplored initial conditions or driving conditions), and a great benefit of these predictability studies is that they mimic the practice of weather prediction by confronting models with observations at the relevant time and spatial scales, leading to the necessary inspiration for this model improvement.

The 2001 Intergovernmental Panel on Climate Change (IPCC) Report that governments accept as certain predictions of future weather says, «In climate research and modeling, we should recognize that we are dealing with a coupled non-linear chaotic system, and therefore that the long - term prediction of future climate states is not possible.»

Traditionally numerical weather prediction has advanced progressively by improving single, «deterministic» forecasts with an increasing model accuracy and decreasing initial condition errors.

Here are my climate change predictions bases on my own model (which I won't share with anybody because they might either try and take the credit for it or try and find something wrong with it) and on no data at all beyond vague memories of weather I have experienced and what I remember reading.

We can perhaps learn from numerical weather prediction where the benefits of developing global prediction models with high vertical and horizontal resolution are clear cut (confirmed most recently by predictions of Sandy).

ERA - Interim combines information from meteorological observations with background information from a forecast model, using the data assimilation approach developed for numerical weather prediction.

That requires considerable sensitivity research with state - of - the art numerical weather prediction (and climate) models... This hand - waving theory may not hold up when a rigorous scientific hypothesis is tested, yet McKibben does not provide a citation or reference aside from Masters» quotations, which are not peer - reviewed in the slightest.»

This graph shows the predictions of various IPCC global climate models (lines with no squares or circles) compared to global temperature measurements made by weather balloons (circles) and satellites (squares).

For even if the models are proven to be wrong with respect to their predictions of atmospheric warming, extreme weather, glacial melt, sea level rise, or any other attendant catastrophe, those who seek to regulate and reduce CO2 emissions have a fall - back position, claiming that no matter what happens to the climate, the nations of the Earth must reduce their greenhouse gas emissions because of projected direct negative impacts on marine organisms via ocean acidification.

The statistics of the weather make short term climate prediction very difficult — particularly for climate models that are not run with any kind of initialization for observations — this has been said over and over.

I think it's safe to assume that when most people are presented with a crime prediction algorithm they expect that model to take into account a number of features (e.g., the weather / time of year, the unemployment rate, foreclosure rate, etc.).