Improved mathematical characterizations of forecast model error, including model parameter calibration and more accurate quantification of uncertainties in the state estimate and its covariance in the presence of systematic errors, is a topic of ongoing research [ 40- 42].
The probabilistic characteristics of the model errors, including the mean values, coefficients of variation and probability distributions, for the burst models are obtained by analyzing the ratios between the test and predicted burst pressures corresponding to the test data applicable to the model.
Since the present study controlled for sampling and mapping error, clear priorities could be identified for reducing model functional error, including improvement of sub-models for snow interception, and snowpack thermal dynamics.
This residual error includes, among other factors, model mis-specification, intra-individual variability, and measurement error.
Emphasis is placed on the derivation of the equations for evaluating the forecast sensitivity to parameters in the DAS representation of the model error statistics, including bias, standard deviation, and correlation structure.
Model errors, which include numerical solution errors and model form errors, are quantified through Gaussian process models and included in the reliability analysis.
Then, we modeled the long-wavelength error included in the interferogram as Fig. 6 GNSS velocity field.
The serial parallel estimation model is given and a filter error included tracking error and modeling error is constructed to design the weights updating law of neural networks (NNs).
The spatial Durbin error model includes spatially lagged errors and independent variables.
The spatial Durbin error model includes spatially lagged errors as well as spatially lagged beds and elderly population.
