Sentence examples for model physics and from inspiring English sources

The focus is set on uncertainties in model physics and lateral boundary conditions which are represented by a multi-parameter and a multi-boundary approach.

Specific assimilation challenges posed by inadequacies in land surface model physics and spatial support contrasts between model predictions and sensor retrievals are discussed.

The structures of different precipitation forecasting systems are constantly evolving, with improvements in forecasting techniques, increases in spatial and temporal resolution, improvements in model physics and numerical techniques, and better understanding of, and accounting for, predictive uncertainty.

Due to its sophisticated model physics and its capability to be tuned to user needs, this model not only finds wide application in real time Numerical Weather Prediction (NWP), but also in climate research, where the model can be run for long term climate simulations.

Future developments in meteorological modeling for air-quality applications will include advanced model physics and data assimilation, better coupling between meterological and chemical models, and could lead eventually to widespread use of fully integrated meteorological-chemical models for simulating and predicting air quality.

However, a full uncertainty analysis involving uncertainties in climate model physics and population projections has not been undertaken.

A combination of model physics, initial and boundary conditions, and high grid resolutions was essential for successful simulation.

The result is a quantifiable confidence in material characterization and model physics when simulating low velocity impact in structures of interest.

Hopefully, the authors would continue their efforts to model the physics and chemistry of the early evolution.

Duhem's mature position was somewhat different, containing three key ideas elaborated upon on various occasions: (1) the underdetermination of theory by fact, often referred to as the Duhem Thesis, (2) the rejection of metaphysics and models in physics, and (3) natural classification as the endpoint of physical theory.

CTSiM adopts this learning-by-design pedagogical approach (Kolodner et al. 2003), and students iteratively design, test, and revise computational models of physics and ecology.