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Our approach compensates for model error by learning an auxiliary dynamical model for the unknown parameters.

Treating data from a constellation of N satellites in a "single-satellite" approach thus typically results in an improvement of the model error by less than.

Small value of |Lb/Rf| allows the neglect of the model error by increasing the upper or the lower bound of the flame radius range.

We have attempted to simulate the effect of model error by using one model of GBM growth, Eqs.

In REDUCE, for instance, motifs are selected recursively in an add-on manner to reduce the model error by the largest degree at each selection.

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Figure 4 Model errors by windows and optimized through CO and SDP, respectively, and by window with RRC-shaped magnitude spectrum versus at.

It successfully resists the outliers and dynamic model errors by adaptively balancing the influence of updated dynamic model state and the measurements.

To go further, we could reduce the importance of SV model errors by co-estimating the unresolved field at degrees n>14 (and its associated uncertainties) together with the field at large length-scales, following Aubert (2014) who performed such a co-estimation using the cross-covariances between Gauss coefficients obtained from the forward integration of a geodynamo simulation.

We examined the within-city modeling error by calculating the Pearson's and Spearman's correlation coefficients (rP and rS) between the deviations of modeled and measured values from their city means from cities with at least two monitoring sites.

Fig. 3 Modeling errors by LSM without considering lens distortion Fig. 4 Modeling errors by LSM with considering radial lens distortion.

Moreover, it was determined that the model error incurred by failing to account properly for interfacial coupling was approximately an order of magnitude larger for the wetting-phase curves than it was for the nonwetting-phase curves.

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