Role of Parameter Errors in the Spring Predictability Barrier for ENSO Events in the Zebiak–Cane Model

ABSTRACT The impact of both initial and parameter errors on the spring predictability barrier （SPB） is investigated using the Zebiak Cane model （ZC model）. Previous studies have shown that initial errors contribute more to the SPB than parameter errors in the ZC model. Although parameter errors themselves are less important, there is a possibility that nonlinear interactions can occur between the two types of errors, leading to larger prediction errors compared with those induced by initial errors alone. In this case, the impact of parameter errors cannot be overlooked. In the present paper, the optimal combination of these two types of errors [i.e., conditional nonlinear optimal perturbation （CNOP） errors] is calculated to investigate whether this optimal error combination may cause a more notable SPB phenomenon than that caused by initial errors alone. Using the CNOP approach, the CNOP errors and CNOP-I errors （optimal errors when only initial errors are considered） are calculated and then three aspects of error growth are compared： （1） the tendency of the seasonal error growth; （2） the prediction error of the sea surface temperature anomaly; and （3） the pattern of error growth. All three aspects show that the CNOP errors do not cause a more significant SPB than the CNOP-I errors. Therefore, this result suggests that we could improve the prediction of the E1 Nifio during spring by simply focusing on reducing the initial errors in this model.

A multi-functional dynamic state estimator for error validation:measurement and parameter errors and sudden load changes

We propose a new and efficient algorithm to detect, identify, and correct measurement errors and branch parameter errors of power systems. A dynamic state estimation algorithm is used based on the Kalman filter theory. The proposed algorithm also successfully detects and identifies sudden load changes in power systems. The method uses three normalized vectors to process errors at each sampling time: normalized measurement residual, normalized Lagrange multiplier, and normalized innovation vector. An IEEE 14-bus test system was used to verify and demonstrate the effectiveness of the proposed method. Numerical results are presented and discussed to show the accuracy of the method.

Modeling and Sensitivity Analysis of Navigation Parameter Errors for Airborne Synthetic Aperture Radar Stereo Geolocation

For the high-resolution airborne synthetic aperture radar （SAR） stereo geolocation application, the final geolocation ac- curacy is influenced by various error parameter sources. In this paper, an airborne SAR stereo geolocation parameter error model, involving the parameter errors derived from the navigation system on the flight platform, has been put forward. Moreover, a kind of near-direct method for modeling and sensitivity analysis of navigation parameter errors is also given. This method directly uses the ground reference to calculate the covariance matrix relationship between the parameter errors and the eventual geoloeation errors for ground target points. In addition, utilizing true flight track parameters＇ errors, this paper gave a verification of the method and a corresponding sensitivity analysis for airborne SAR stereo geolocation model and proved its efficiency.

A METHOD OF ESTIMATING THE INFLUENCE OF EXPERIMENTAL ERRORS ON THE EXPERIMENTAL RESULTS OF MATERIAL PARAMETERS AND THE CRITERIA TO VALUE THIS INFLUENCE

In this paper the main sources causing the scatter of the experimental results of the material parameters are discussed. They can be divided into two parts: one is the experimental errors which are introduced because of the inaccuracy of experimental equipment, the experimental techniques, etc., and the form of the scatter caused by this source is called external distribution. The other is due to the irregularity and inhomogeneity of the material structure and the randomness of deformation process. The scatter caused by this source is inherent and then this form of the scatter is called internal distribution. Obviously the experimental distribution of material parameters combines these two distributions in some way; therefore, it is a sum distribution of the external distribution and the internal distribution. In view of this , a general method used to analyse the influence of the experimental errors on the experimental results is presented, and three criteria used to value this influence are defined. An example in which the fracture toughness KIC is analysed shows that this method is reasonable, convenient and effective.

Controller Parameter Tuning of Delta Robot Based on Servo Identification

High-speed pick-and-place parallel robot is a system where the inertia imposed on the motor shafts is real-time changing with the system configurations.High quality of computer control with proper controller parameters is conducive to overcoming this problem and has a significant effect on reducing the robot＇s tracking error.By taking Delta robot as an example,a method for parameter tuning of the fixed gain motion controller is presented.Having identifying the parameters of the servo system in the frequency domain by the sinusoidal excitation,the PD＋feedforward control strategy is proposed to adapt to the varying inertia loads,allowing the controller parameters to be tuned by minimizing the mean square tracking error along a typical trajectory.A set of optimum parameters is obtained through computer simulations and the effectiveness of the proposed approach is validated by experiments on a real prototype machine.Let the traveling plate undergoes a specific trajectory and the results show that the tracking error can be reduced by at least 50%in comparison with the conventional auto-tuning and Z-N methods.The proposed approach is a whole workspace optimization and can be applied to the parameter tuning of fixed gain motion controllers.

Research on sea surface temperature retrieval by the one-dimensional synthetic aperture microwave radiometer, 1D-SAMR

Due to the low spatial resolution of sea surface temperature(T_S)retrieval by real aperture microwave radiometers,in this study,an iterative retrieval method that minimizes the differences between brightness temperature(T_B)measured and modeled was used to retrieve sea surface temperature with a one-dimensional synthetic aperture microwave radiometer,temporarily named 1 D-SAMR.Regarding the configuration of the radiometer,an angular resolution of 0.43°was reached by theoretical calculation.Experiments on sea surface temperature retrieval were carried out with ideal parameters;the results show that the main factors affecting the retrieval accuracy of sea surface temperature are the accuracy of radiometer calibration and the precision of auxiliary geophysical parameters.In the case of no auxiliary parameter errors,the greatest error in retrieved sea surface temperature is obtained at low T_S scene(i.e.,0.7106 K for the incidence angle of 35°under the radiometer calibration accuracy of0.5 K).While errors on auxiliary parameters are assumed to follow a Gaussian distribution,the greatest error on retrieved sea surface temperature was 1.3305 K at an incidence angle of 65°in poorly known sea surface wind speed(W)(the error on W of 1.0 m/s)over high W scene,for the radiometer calibration accuracy of 0.5 K.

Effects of microlens array orientation errors on plenoptic imaging of flame radiative properties and uncertainty analysis

Plenoptic imaging(PI)systems can flexibly record both spatial and angular information on flame radiation,enabling volumetric reconstruction of complex flames.The accuracy and efficiency of the reconstruction are significantly affected by the orientation parameters of the microlens array(MLA)in the system.To investigate the influence of potential parameter errors on flame light fields,we establish a typical orientation error model and employ ray-splitting-based Monte Carlo method to simulate the entire and sectioned PI for the three-dimensional flame under four error conditions,in which different radiative properties of flame(extinction coefficient,scattering albedo,and scattering phase function)are considered.Through the proposed uncertainty evaluation scheme,the flame image characteristics,intensity,structure deviations,and their local distribution for four error types are analyzed.The results show that the extinction coefficient and the MLA error type determine the flame PI uncertainty features,while the scattering properties only change the deviation levels.The major impact of the extinction coefficient on the transfer and accumulation of uncertainty in flame-sectioned PI is also revealed.

A weighted voltage model predictive control method for a virtual synchronous generator with enhanced parameter robustness

To address the problem of insufficient system inertia and improve the power quality of grid-connected inverters,and to enhance the stability of the power system,a method to control a virtual synchronous generator(VSG)output voltage based on model predictive control(MPC)is proposed.Parameters of the inductors,capacitors and other components of the VSG can vary as the temperature and current changes.Consequently the VSG output voltage and power control accuracy using the conventional MPC method may be reduced.In this paper,to improve the parameter robustness of the MPC method,a new weighted predictive capacitor voltage control method is proposed.Through detailed theoretical analysis,the principle of the proposed method to reduce the influence of parameter errors on voltage tracking accuracy is analyzed.Finally,the effectiveness and feasibility of the proposed method are verified by experimental tests using the Typhoon control hardware-in-the-loop experimental platform.

Error Sensitivity Analysis in 10–30-Day Extended Range Forecasting by Using a Nonlinear Cross-Prediction Error Model

Extended range forecasting of 10-30 days, which lies between medium-term and climate prediction in terms of timescale, plays a significant role in decision-making processes for the prevention and mitigation of disastrous met- eorological events. The sensitivity of initial error, model parameter error, and random error in a nonlinear cross- prediction error （NCPE） model, and their stability in the prediction validity period in 1 0-30-day extended range fore- casting, are analyzed quantitatively. The associated sensitivity of precipitable water, temperature, and geopotential height during cases of heavy rain and hurricane is also discussed. The results are summarized as follows. First, the initial error and random error interact. When the ratio of random error to initial error is small （10＂5-10-2）, minor vari- ation in random error cannot significantly change the dynamic features of a chaotic system, and therefore random er- ror has minimal effect on the prediction. When the ratio is in the range of 10-1-2 （i.e., random error dominates）, at- tention should be paid to the random error instead of only the initial error. When the ratio is around 10 2-10-1, both influences must be considered. Their mutual effects may bring considerable uncertainty to extended range forecast- ing, and de-noising is therefore necessary. Second, in terms of model parameter error, the embedding dimension m should be determined by the factual nonlinear time series. The dynamic features of a chaotic system cannot be depic- ted because of the incomplete structure of the attractor when m is small. When m is large, prediction indicators can vanish because of the scarcity of phase points in phase space. A method for overcoming the cut-off effect （m 〉 4） is proposed. Third, for heavy rains, precipitable water is more sensitive to the prediction validity period than temperat- ure or geopotential height; however, for hurricanes, geopotential height is most sensitive, followed by precipitable water.