Sentence examples for model forecast by from inspiring English sources

In contrast, the field model forecast by the steady flow model (black solid line) is poorest at degree 3 (coefficients and ), but better at degrees 1, 2 and 4 6.

The RMS difference per degree shows that the field model forecast by the IGRF-10 SV prediction (grey solid line, circle marker) differs strongly from the CHAOS-2 model at degrees 1-5.

The average of the dynamical model forecasts is shown by the thick yellow line, and statistical model forecasts by the thick blue/green line.

Model forecasts by the U.S. Geological Survey suggest that habitat loss may cause polar bear populations to decline by two-thirds by the year 2050.

In an Ensemble Kalman Filter the uncertainty of our knowledge of the process is represented by perturbing the inputs to the model forecast equations randomly by a known variance (with zero-mean) to produce an 'ensemble' of states conceptually imagined as a 'cloud' of points in r-dimensional space.

It is shown that it is possible to reduce the discrepancy between the model forecast and the true situation by data assimilation.

Table 13 MAE-based skill scores of wind speed forecasts produced by model of Abdel-Aal et al. [34] and VAR model using SP model as reference model Forecast horizon Abdel-Aal et al. [34] model VAR model 1-hour ahead 0.08 0.06-hoursrs ahead 0.15 0.15.

There was a significant improvement of the WRF model forecasts when adjusted by the ANNs, yielding lower bias and RMSE, and an increase in the correlation coefficient.

Because Bueno de Mesquita's model forecasts the future by calculating the impact every player has on every other player, round by round, Bueno de Mesquita can go back and see when some players suddenly become more flexible midway through a negotiation.

The ensemble mean is expected to be more highly correlated with observations than are individual ensemble members because the unpredictable component of the model forecasts is reduced by averaging.

Here we estimate PCobs directly from the fraction of the variance that can be explained by model forecasts, diagnosed from the Pearson correlation (r) between observations ("predictand") and the ensemble mean of model hindcasts ("predictor") as r reflects the proportion of the predictand accounted for by the predictor [e.g., Wilks, 2006].