Application of an Error Statistics Estimation Method to the PSAS Forecast Error Covariance Model

In atmospheric data assimilation systems, the forecast error covariance model is an important component. However, the paralneters required by a forecast error covariance model are difficult to obtain due to the absence of the truth. This study applies an error statistics estimation method to the Pfiysical-space Statistical Analysis System （PSAS） height-wind forecast error covariance model. This method consists of two components： the first component computes the error statistics by using the National Meteorological Center （NMC） method, which is a lagged-forecast difference approach, within the framework of the PSAS height-wind forecast error covariance model; the second obtains a calibration formula to rescale the error standard deviations provided by the NMC method. The calibration is against the error statistics estimated by using a maximum-likelihood estimation （MLE） with rawindsonde height observed-minus-forecast residuals. A complete set of formulas for estimating the error statistics and for the calibration is applied to a one-month-long dataset generated by a general circulation model of the Global Model and Assimilation Office （GMAO）, NASA. There is a clear constant relationship between the error statistics estimates of the NMC-method and MLE. The final product provides a full set of 6-hour error statistics required by the PSAS height-wind forecast error covariance model over the globe. The features of these error statistics are examined and discussed.

Assimilation and Simulation of Typhoon Rusa (2002) Using the WRF System

Using the recently developed Weather Research and Forecasting (WRF) 3DVAR and the WRF model, numerical experiments are conducted for the initialization and simulation of typhoon Rusa (2002). The observational data used in the WRF 3DVAR are conventional Global Telecommunications System (GTS) data and Korean Automatic Weather Station (AWS) surface observations. The Background Error Statistics (BES) via the National Meteorological Center (NMC) method has two different resolutions, that is, a 210-km horizontal grid space from the NCEP global model and a 10-km horizontal resolution from Korean operational forecasts. To improve the performance of the WRF simulation initialized from the WRF 3DVAR analyses, the scale-lengths used in the horizontal background error covariances via recursive filter are tuned in terms of the WRF 3DVAR control variables, streamfunction, velocity potential, unbalanced pressure and specific humidity. The experiments with respect to different background error statistics and different observational data indicate that the subsequent 24-h the WRF model forecasts of typhoon Rusa's track and precipitation are significantly impacted upon the initial fields. Assimilation of the AWS data with the tuned background error statistics obtains improved predictions of the typhoon track and its precipitation.

2-D Statistical Damage Detection of Concrete Structures Combining Smart Piezoelectric Materials and Scanning Laser Doppler Vibrometry

In the present study a new structural health monitoring (SHM) technique isproposed as well as a new damage index based on 2-D error statistics. The proposedtechnique combines the electromechanical impedance technique (EMI) which is based onthe use of piezoelectric Lead Zirconate Titanate (PZT) patches and Scanning LaserDoppler Vibrometry (SLDV) for damage detection purposes of concrete structures andearly age monitoring. Typically the EMI technique utilizes the direct and inversepiezoelectric effect of a PZT patch attached to a host structure via an impedance analyzerthat is used for both the actuation and sensing the response of the PZT-Host structuresystem. In the proposed technique the attached PZTs are actuated via a function generatorand the PZT-Host structure response is obtained by a Scanning Laser DopplerVibrometer. Spectrums of oscillation velocity of the surface of the attached PZTs verticalto the laser beam versus frequency are obtained and are evaluated for SHM purposes.This damage detection approach also includes the use of a damage index denoted asECAR (Ellipse to Circle Area Ratio) based on 2-D error statistics and is compared to theRoot Mean Square Deviation (RMSD) damage index commonly used in SHMapplications. Experimental results include ascending uniaxial compressive load ofconcrete cubic specimens, ascending three point bending of reinforced concrete beamspecimens and early age monitoring of concrete. Results illustrate the efficiency of theproposed technique in damage detection as well as early age monitoring as, in the firstcase, both severity and location of damage can be determined by examining the values ofdamage indices for each damaged state and in the early age monitoring case damageindices follow the strength gain curve.

Modeling of the Global Daily Horizontal Solar Radiation Data over Togo

Solar photovoltaic appears to be the most interesting renewable energy in developing countries where its deposit is abundant. Unfortunately, the lack of precise knowledge of solar radiation deposit and its limited data hinder optimal exploitation of solar installations. This study presents a performing model for daily global horizontal solar radiation for the five regional capitals in Togo: Lomé, Atakpamé, Sokodé, Kara and Dapaong. The data used for the study were obtained from the General Directorate of National Meteorology of Togo, for five years. The model developed combines linear and nonlinear methods with harmonic and exponential terms taking into account climatological parameters such as location latitude, daily relative humidity, daily ratio of sunshine duration and daily mean temperature. Statistical errors of the model were compared to those of two previous models elaborated for Togo and Nigeria. The results showed that the model is more efficient to predict global horizontal solar radiation over the five main cities in Togo. The comparison of estimated data and measured ones showed a good agreement between them.

A novel LAAS pseudo-range error over-bound method based on improved pseudo-range error distribution model

The high level of safety demand of civil aviation requests local area augmentation system （LAAS） extremely high navigation integrity performance. A new LAAS pseudo-range error overbound method is proposed in this paper to improve the integrity of LAAS. Firstly, a more practical pseudo-range error distribution model is established. Then, by calculating the relationship between the statistical uncertainty of the model parameter and the integrity risk, a new method is proposed to calculate the pseudo-range error over-bound model. This method can effectively reduce the inflation factor and the resulting conservativeness of the over-bound model. Comparative experiments show that the method proposed in this paper performs better and satisfies the requirements of real applications.

Machining Error Control by Integrating Multivariate Statistical Process Control and Stream of Variations Methodology

For aircraft manufacturing industries, the analyses and prediction of part machining error during machining process are very important to control and improve part machining quality. In order to effectively control machining error, the method of integrating multivariate statistical process control （MSPC） and stream of variations （SoV） is proposed. Firstly, machining error is modeled by multi-operation approaches for part machining process. SoV is adopted to establish the mathematic model of the relationship between the error of upstream operations and the error of downstream operations. Here error sources not only include the influence of upstream operations but also include many of other error sources. The standard model and the predicted model about SoV are built respectively by whether the operation is done or not to satisfy different requests during part machining process. Secondly, the method of one-step ahead forecast error （OSFE） is used to eliminate autocorrelativity of the sample data from the SoV model, and the T2 control chart in MSPC is built to realize machining error detection according to the data characteristics of the above error model, which can judge whether the operation is out of control or not. If it is, then feedback is sent to the operations. The error model is modified by adjusting the operation out of control, and continually it is used to monitor operations. Finally, a machining instance containing two operations demonstrates the effectiveness of the machining error control method presented in this paper.

Statistical errors in Weizscker-Skyrme mass model

The statistical uncertainties of 13 model parameters in the Weizscker-Skyrme（WS＊） mass model are investigated for the first time with an efficient approach,and the propagated errors in the predicted masses are estimated.The discrepancies between the predicted masses and the experimental data,including the new data in AME 2016,are almost all smaller than the model errors.For neutron-rich heavy nuclei,the model errors increase considerably,and go up to a few MeV when the nucleus approaches the neutron drip line.The most sensitive model parameter which causes the largest statistical error is analyzed for all bound nuclei.We find that the two coefficients of symmetry energy term significantly influence the mass predictions of extremely neutron-rich nuclei,and the deformation energy coefficients play a key role for well-deformed nuclei around the β-stability line.

Adapting Nanotech Research as Nano-Micro Hybrids Approach Biological Complexity,A Review

Today＇s emergence of nano-micro hybrid structures with almost biological complexity is of fundamental interest. Our ability to adapt intelligently to the challenges has ramifications all the way from fundamentally changing research itself, over applications critical to future survival, to posing globally existential dangers. Touching on specific issues such as how complexity relates to the catalytic prowess of multi-metal compounds, we discuss the increasingly urgent issues in nanotechnology also very generally and guided by the motto ＇Bio Is Nature＇s Nanotech＇. Technology belongs to macro-evolution; for example integration with artificial intelligence （AI） is inevitable. Darwinian adaptation manifests as integration of complexity, and awareness of this helps in developing adaptable research methods that can find use across a wide range of research. The second half of this work reviews a diverse range of projects which all benefited from ＇playful＇ programming aimed at dealing with complexity. The main purpose of reviewing them is to show how such projects benefit from and fit in with the general, philosophical approach, proving the relevance of the ＇big picture＇ where it is usually disregarded.