Sentences with phrase «statistical prediction model»

Not exact matches

Expert Prediction from Eric Fox, vice president of statistical and economic modeling (VeroForecast)-- The top forecast markets shows price appreciation in the 10 % to 11 % range.

By tweaking orbital parameters and running their model repeatedly, the team could make some statistical predictions about the car's future path.

The model predictions are therefore reliable, taking some statistical uncertainty into account.

Although a more detailed understanding of the physics of the two layers is necessary to improve the computer models, the stratospheric effects can simply be used as another factor to incorporate into statistical predictions.

These statistical fluctuations produce the background noise that makes it so difficult for mathematical models to provide clear predictions with respect to individual iterations of such probabilistic processes.

We are excited to connect and compare planet formation models and their predictions to exoplanet populations; we are looking for a postdoctoral researcher with expertise in planet formation, exoplanet population studies, and / or statistical assessment of exoplanet surveys,

«Integrating multi-scale phenotypic information into prediction models for genotype by environment interaction by a synthesis of statistical - genetic and physiological models»

Since the 1950s, social scientists have been comparing the predictive abilities of traditional experts, and what are known as «statistical prediction rules,» which are just simple models.

Further, decision makers who, when provided with the output of the simple statistical model, wave off the model's predictions tend to make poorer decisions than the model.

These statistical models are very efficient at encapsulating existing information concisely and as long as things don't change much, they can provide reasonable predictions of future behaviour.

PIOMAS has been run in a forward mode (and hence without data assimilation) to yield seasonal predictions for the sea ice outlook (Zhang et al. 2008) and has also provided input to statistical forecasts (Lindsay et al. 2008) and fully - coupled models.

Prediction methods for the sea ice minima range from ad - hoc guesses to model predictions, from statistical analyses to water - cooler speculation in the blogosphere.

-- in which case, that's a statistical model prediction, which, at least in this context, we shouldn't rely on — if we actually know some things about how the climate works then it makes more sense to use that knowledge.

(As an aside, we wonder how Gray, who is largely known for prediction of hurricane behavior based on (statistical) modeling, felt about this?).

Based on this, I suggest that the best way to monitor trends would be to use a statistical correlation model (such as the above) and check if new data points fall within 2 standard deviations of the model predictions.

Yuan et al. (LDEO Columbia University), 5.08 (+ / - 0.51), Statistical The prediction is made by statistical models, which are capable to predict Arctic sea ice concentrations at grid points 3 - month in advance with reasonaStatistical The prediction is made by statistical models, which are capable to predict Arctic sea ice concentrations at grid points 3 - month in advance with reasonastatistical models, which are capable to predict Arctic sea ice concentrations at grid points 3 - month in advance with reasonable skills.

Canadian Ice Service, 4.7 (+ / - 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

As a researcher in a non-climate area I am stunned at (a) the lack of replication, (b) the tortured and frequently questionable use of certain statistical methods, © the overall underspecification of statistical models given the signifcance of the predictions being made and (d) the unwillingness of researchers to essentially treat anyone outside their immediate field with common courtesy.

The June, July, and August SIO reports received a total of 106 contributions for pan-Arctic extent predictions (based on multiple methods: statistical, dynamical models, estimates based on trends, and subjective information) along with contributions for Alaska regional extent predictions, descriptive regional contributions, and pan-Antarctic extent predictions — a new SIO feature for 2017.

As a logician and supporter of the IPCC's conclusions regarding AGW, how do you respond to the absence, in AR4, of reference to the statistical population underlying the IPCC's conclusions or of predictions from the IPCC's models.

Although the heatwave prediction for India is based on a statistical model, the model itself is based on half a century of carefully - measured temperature, heatwave and heat - related mortality data.

The statistical assessment of errors in model prediction and model estimation is of fundamental importance.

For any statistical forecasting model, a large number of observations are necessary to get reliable predictions.

Cohen received his Ph.D. in Atmospheric Sciences from Columbia University in 1994 and has since focused on conducting numerical experiments with global climate models and advanced statistical techniques to better understand climate variability and to improve climate prediction.

Type 3 statistical downscaling uses the regression relationships developed for Type 1 statistical downscaling, except using the variables from the global model prediction forced by specified real - world surface boundary conditions.

Under a logical approach to validation, each prediction that is made by a predictive model is viewed as making a claim about the outcome of a statistical event and this claim is viewed as a logical proposition.

Dynamic and statistical downscaling is widely used to refine predictions from global climate models to smaller spatial scales.

Canadian Ice Service; 5.0; Statistical As with Canadian Ice Service (CIS) contributions in June 2009 and June 2010, the 2011 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic Multi-Year Ice (MYI) extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere, and sea ice predictors.

Prediction of September 2010 sea ice concentration in the Canadian Arctic Archipelago from a statistical model (canonical correlation analysis).

Prediction of July 2010 sea ice concentration anomalies in the Hudson Bay region from a statistical model.

This is consistent with both the June and July (Figure 3) ensemble predictions from a coupled ice - ocean model submitted by Zhang, which show considerably more ice in the East Siberian Sea compared to 2009, and it is consistent with the June statistical forecasts submitted by Tivy, which also predict a greater ice area than in 2009 and above - normal ice concentrations along the coasts.

Kapsch et al, 4.66 (± 0.59), Statistical For the prediction of the September sea - ice extent we use a simple linear regression model that is only based on the atmospheric water vapor in spring (April / May).

Global Weather Climate Logistics, 5.44, Statistical The Global Weather and Climate Logistic's prediction is based on two statistical models that incorporate between 10 and 20 surface, atmospheric and oceanicStatistical The Global Weather and Climate Logistic's prediction is based on two statistical models that incorporate between 10 and 20 surface, atmospheric and oceanicstatistical models that incorporate between 10 and 20 surface, atmospheric and oceanic variables.

Canadian Ice Service, 4.7 (± 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

Slater, 4.55 (± 0.35), Statistical I have extended my model prediction out to a lead time of 53 days.

Kapsch et al., 4.1 (± 0.5), Statistical (same as June) For the prediction of the September sea - ice extent we use a simple linear regression model that is only based on the atmospheric water vapor in spring (April / May).

Slater (SPIE), 4.4 (± 0.75), Statistical I have extended my model prediction out to a lead time of 85 days.

As with previous CIS contributions, the 2016 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness / extent, as well as winter surface air temperature, spring ice conditions and the summer temperature forecast; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent time - series into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

For the first time, scientists have compared the latest predictions for global warming with a range of statistical models, commonly used to predict the spread of malaria.

For example, the median prediction of heuristically - based contributions is 4.4 million km2, compared with 4.75 for statistical and 4.7 for modeling.

Kapsch et al, 4.75 (4.13 - 5.37), Statistical For the prediction of the September sea - ice extent we use a simple linear regression model that is only based on the atmospheric water vapor in spring (April / May).

That the duration of each prediction of a model is 50 years implies that the duration of each independent statistical event in the complete set of these events is 50 years.

Because these relationships can be subtle and complex, statistical models combining multiple parameters are expected to be more effective than individual monthly data at making predictions.

If the actual data to be plotted does not show rapid and unprecedented warming in the late 20th century, then why would the statistical science of climate model prediction give us high CO2 sensitivity and doomsday predictions for the middle / end of this century?

These weights allow for an objective, statistical prediction of global mean temperature fluctuations arising solely from SST - associated internal variability within a given model.

As discussed above, it is possible to overfit the statistical model during the calibration period, which has the effect of underestimating the prediction error.

Nor have Christy et al. corrected the serious statistical error in their 2007 International Journal of Climatology paper, «A comparison of tropical temperature trends with model predictions,» a post-Wegman paper, but it speaks to their credibility.

Finally, there's consensus that we can not look at climate forecasts — in particular, probabilistic forecasts — the same way we view weather predictions, and none of us would sell climate - model output, either at face value or after statistical analysis, as a reliable representation of the complete range of possible futures.

Liljegren (Public Contribution); 4.6 + 0.9; Statistical Prediction is based on using the JAXA sea ice extent from July 28, 2011 in a linear regression model based on July 28 sea ice extents and September minimum values since 2002.

Grumbine and Wu (National Oceanic and Atmospheric Administration); 4.6 Million Square Kilometers; Statistical and Numerical Modeling Model - based prediction using April 2010 starting conditions.