Sentences with phrase «different forecast models»

Several days ago, Andy Revkin did a nice post querying a number of weather forecasters and researchers about the relative merits of the different forecast models [link], particularly since everyone seemed to be paying attention to the European model (ECMWF) rather than NOAA's GFS model.

Not exact matches

When the company auctions that oilfield drill, for example, the goal is for its pricing model to forecast demand in the near future based on different factors, such as the price of oil, leaving Ritchie Bros. less vulnerable to market surprises.

Vlieghe told the committee: «I'm never confident of any forecast, and I think the big thing that we risk missing here is that every time there is what we call a forecast error — which means the outturn is different from the central projection — to think that «Well if only we'd had a better model we wouldn't have made that forecast error.»

Third, the specifications of their econometric models differ, thereby resulting in different results, even if they used the same economic forecasts and distributions.

Accordingly, the key difference between our DCF model and others is that we calculate the value attributable to equity shareholders over multiple (100) different forecast periods or what we call Growth Appreciation Periods (GAP)[1].

Our models compare and contrast multiple forecast scenarios so clients can assess the valuation impact of different forecasts for revenue growth, margins and capital allocation strategies.

, Fabian Hollstein, Marcel Prokopczuk and Chardin Simen test effects of different return sampling frequencies, forecast adjustments and model combinations on market beta prediction accuracy across the universe of U.S. stocks.

It considers a multitude of different models, forecasts and scenarios that all businesses should be considering in their long term planning.

It seems from one run of the models to the next we keep getting a different scenario and timing but generally I go back to my original forecast that there is a good threat of rain during the race.

Complex Forecasting Models: ElectionForecast.co.uk (Chris Hanretty) Electoral Calculus (main and local election forecast) Forecast UK UK - Elect PME Politics (Patrick English) Nigel Marriot (Uniform Regional Swing + Tactical Voting Model) Chris Prosser (GE vote shares from Local elections vote shares) Lord Ashcroft (3 models based on different turnout estiModels: ElectionForecast.co.uk (Chris Hanretty) Electoral Calculus (main and local election forecast) Forecast UK UK - Elect PME Politics (Patrick English) Nigel Marriot (Uniform Regional Swing + Tactical Voting Model) Chris Prosser (GE vote shares from Local elections vote shares) Lord Ashcroft (3 models based on different turnout estimodels based on different turnout estimates)

Forecasters merged 480 different models for the 2007 California forecast to produce a single forecast with more clearly defined uncertainties.

We use different computer forecast models that feed initial conditions — including temperatures, humidity, wind speed and wind direction from around the United States and around the world, from the surface all the way up to the jet stream — into different equations.

There are about 30 climate models available today, and each has slightly different physics, which means their forecasts do not always match.

In a study set to come out in Nature tomorrow, an international group of scientists reports that they simulated atmospheric behavior using several different models and used them to forecast anthropogenically driven changes in average annual rainfall at different latitudes from 1925 to 1999.

The researchers then turned to a different framework using the Weather Research and Forecasting regional model to study the event in more detail.

The study used more than 40 scientific publications and Australian Institute of Marine Science monitoring data from 18 Australian reefs to build and validate a model to forecast the reef's future under different conditions.

The researchers use computer models to forecast future ocean conditions such as surface temperatures, salinity, and currents, and project how the distribution of different fish species could respond to climate change.

While reading James O'Shaughnessy's Predicting the Markets of Tomorrow (full review to come later), I came across an interesting section with three different models used to forecast the Standard & Poor's 500 Index.

These criteria provide useful metrics for us to compare different alpha - forecasting models.

Combining this value with the current dividend yield of 2.0 % results in a forward one - year expected yield of 3.5 %, not dramatically different from the return forecast by Model 1.

Forecasting what may most likely happen with these factors over time (given the assumed fluctuations in the markets - which you can control every year by using different rates of return on every investment for every year - including negative rates of return, and being able to change your income goal every year) is much more important to model, than a one - dimensional probability number, to an actual investor's life.

Or said a different way, a model's value is in the collection of forecasts it encompasses — that is, the system itself — and not in the individual forecasts.

My local NOAA weather page, at the moment, reports that because two models used give rather different longrange results, the forecast is:

Scenarios A, B, and C are the same model, but with different forcings (different greenhouse gas emissions forecasts).

The UK Met Office forecast uses a very different methodology than Gray, based upon climate models rather than a statistical technique.

ECMWF, NCEP GFS, UK MetOffice Unified Model, and Canadian GEM are the top global weather models and each use somewhat different methods to forecast one atmosphere.

The breadth is inspiring: From novel bioactives for biotechnology and agri - tech, over autonomous and remote sensing for challenging environments, to modelling for space weather forecasts and sea - level rise our expertise can add value to many different sectors of industry and society.

Both the accuracy of forecast assumptions and model performance are both important, but for different reasons.

Careful calibration and judicious combination of ensembles of forecasts from different models into a larger ensemble can give higher skill than that from any single model.

Different models are used for weather prediction versus climate forecasts.

Also, some models just aren't for forecasting at all, or are for forecasting different vectors than interest some readers.

Climate modelling has a different problem: based on forecast and projection, it is by definition an inexact science, but one upon which concrete decisions must be based if governments and societies are to assess risks and plan ahead.

SWIF's performance was evaluated during disturbed conditions against standard models (e.g., climatology and persistence) and other forecasting models of different philosophy such as the TSAR that is a purely autoregressive technique and the Geomagnetically Correlated Autoregression Model — GCAM (Muhtarov et al. 2002) that is driven by the geomagnetic activity level by incorporating the cross-correlation between the foF2 and the Ap - index into the auto - correlation analysis (Tsagouri et al. 2009).

Understanding how these different climate phenomena interact, how they are simulated in models, and how they can be used for sub-seasonal to seasonal forecasting is currently a major focus of research.

And that change can be modelled in many ways which each leads to a different forecast of future atmospheric CO2 concentration.

In our climate modeling project we were trying to combine different temperature forecasts on a scale in which Africa was represented by about 600 grid boxes.

There are a wide range of sensitivities in the models used in the IPCC reports yet the individual model forecasts haven't separated themselves into different temperature ranges based on their sensitivities yet, they're still all mixed together.

BBD If a book is written that uses models to forecast future conditions in the UK, but those same models have been demonstrated to not be able to even reasonably accurately predict future rainfall at any specific location, what good is the analysis in the book that describes different conditions based on changes in rainfall?

In this graph from the new paper, gray shading shows pollution levels forecast by different models if there were no clean - energy investment.

Various hydrologic models with different complexities have been developed to represent the characteristics of river basins, improve streamflow forecasts such as seasonal volumetric flow predictions, and meet other demands from different stakeholders.

[Poitou & Bréon] It is because the climate is a chaotic system that models can forecast the Climate for conditions very different of todays.

They also show that some forecast models are better than others at different times of year.

They also recommend usage of different independently devised models to produce ensemble forecasts, for better comparison and quicker model improvements.

It's pretty funny that the GWPF is predicting no warming based on statistical analyses that can't agree whether warming stopped in 1998 or 2002, and pretty funny that the two modeling techniques used deliver quite different forecasts, making not one, not two, but four different forecasts — all of which we are apparently supposed to take more seriously than anything that is based on, you know, physics.