Figure 4a illustrates the error distribution for a very low CNR of CNR =−5 dB by showing 8,000 error points from the Monte Carlo simulations.
We also present an analytical solution of Stokes flow calculated using the penalization which illustrates the error and continuity properties of the approach.
DoP (Dilution of Precision), which is also referred as geometric strength, is the indicator that illustrates the error propagation from observations to estimated parameters.
Figure 8 illustrates the error probability distribution function when using a db4 wavelet filter, where the error is defined as the time difference between the algorithm arrival times and the SC times which are determined by the NGDC.
These plots are used to illustrate the errors between forecasts and observation data according to a given temperature; in this way, we are able to control possible under/overestimation of the model depending on temperature values and it is possible to evaluate the performance of the forecasting model in different climate seasons.
To illustrate the error rates that one can expect, we give an example of choice of parameters.
Computational results illustrating the error behavior, iteration counts and performance of block-diagonal and monolithic geometric multigrid preconditioners are presented for the discrete CFOSLS system.
Experiment 1 to illustrate the error between Γ(k) and Γ(n,β): In the first experiment, we demonstrate that the asymptotic throughput Γ(k) can well approximate the exact throughput Γ(n,β).
Predicted electrical conductivities based on Equation 3 for 1% volume fraction and Equation 4 for higher volume fractions, using the measured zeta potentials given in Table 1, are also shown in Figure 2. Dotted lines are used to illustrate the error that occurs when extending Case 1 (e.g., linear increase with volume fraction) to higher volume fractions at which counter-ion condensation occurs.
