Sentence examples for merits of ensemble from inspiring English sources

In addition, the multiple possible scenarios revealed two other merits of ensemble forecasts.

The results of this study demonstrated three merits of ensemble forecasts based on the outputs of the nested LETKF system.

Although we demonstrated the merits of ensemble forecasts in this paper, there are several areas where improvements are needed.

The ability to obtain information on uncertainty is one of the merits of ensemble forecasts mentioned in the "Background" section.

Although higher resolution observation data (e.g., radial wind from Doppler radars and GPS precipitable water vapor) are expected to be better able to reproduce smaller scale distributions of the initial conditions than conventional data, we did not use such high-resolution data in this study because our main purpose was to explore the merits of ensemble forecasts.

In the first subchallenge, we use a greedy search algorithm (bidirectional search) that combines the merits of ensemble modeling and kernel methods (support vector machine (SVM)) to predict the sensitivity of the breast cancer cell lines to previously untested drug compounds.

The merits of the ensemble forecasts of convective scale phenomena, such as the possible distribution of intense vortices, can be demonstrated with as few as 12 ensemble members.

This study presents the implementation and the merits of an Ensemble Kalman Filter (EnKF) algorithm with an inflation procedure on the 1D shallow water model MASCARET in the framework of operational flood forecasting on the "Adour Maritime" river (South West France).

Thus, ensemble forecasts have the following merits: (1) analyzed fields of ensemble forecasts (i.e., the ensemble average of multiple scenarios) are statistically more accurate than those of deterministic forecasts and (2) multiple possible scenarios provide information regarding uncertainty (i.e., when there is large scatter among the scenarios, the uncertainty is larger).

Benefited from the merits of combining deep learning scheme with ensemble pruning paradigm, in the empirical results, DD-ELMs-ES demonstrates better generalization performance than the basic deep ELM models and some other state-of-the-art algorithms in tackling with time series forecasting tasks.

It was that kind of ensemble show.