Idealized Experiments for Optimizing Model Parameters Using a 4D-Variational Method in an Intermediate Coupled Model of ENSO

Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational （4D-Vat） data assimilation system was developed for an intermediate coupled model （ICM） and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer （Te）, which is empirically and explicitly related to sea level （SL） variation. The strength of the thermocline effect on SST （referred to simply as ＂the thermocline effect＂） is represented by an introduced parameter, （l＇Te. A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having their initial condition optimized only, and having their initial condition plus this additional model parameter optimized, are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameters and initial conditions together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Vat data assimilation system implemented in the ICM are also discussed.

Automated estimation of stellar fundamental parameters from low resolution spectra: the PLS method

PLS （Partial Least Squares regression） is introduced into an automatic estimation of fundamental stellar spectral parameters. It extracts the most correlative spectral component to the parameters （Teff, log g and [Fe/H]）, and sets up a linear regression function from spectra to the corresponding parameters. Considering the properties of stellar spectra and the PLS algorithm, we present a piecewise PLS regression method for estimation of stellar parameters, which is composed of one PLS model for Teff, and seven PLS models for log g and [Fe/H] estimation. Its performance is investigated by large experiments on flux calibrated spectra and continuum normalized spectra at different signal-to-noise ratios （SNRs） and resolutions. The results show that the piecewise PLS method is robust for spectra at the medium resolution of 0.23 nm. For low resolution 0.5 nm and 1 nm spectra, it achieves competitive results at higher SNR. Experiments using ELODIE spectra of 0.23 nm resolution illustrate that our piecewise PLS models trained with MILES spectra are efficient for O ～ G stars： for flux calibrated spectra, the systematic offsets are 3.8%, 0.14 dex, and -0.09 dex for Teff, log g and [Fe/H], with error scatters of 5.2%, 0.44 dex and 0.38 dex, respectively; for continuum normalized spectra, the systematic offsets are 3.8%, 0.12dex, and -0.13 dex for Teff, log g and [Fe/H], with error scatters of 5.2%, 0.49 dex and 0.41 dex, respectively. The PLS method is rapid, easy to use and does not rely as strongly on the tightness of a parameter grid of templates to reach high precision as Artificial Neural Networks or minimum distance methods do.

核心稳定性训练对脑卒中偏瘫患者步态时空参数和对称性参数的影响

目的:应用步态分析,观察核心稳定性训练对脑卒中偏瘫患者步态时空参数和对称性参数的影响。方法:选取脑卒中偏瘫患者60例,按随机数字表法将其分为观察组及对照组,每组30例。两组均进行常规治疗,观察组在此基础上给予核心稳定性训练。分别于治疗前和治疗6周后使用三维步态分析仪器检测并获得两组患者的步态参数。结果:治疗6周后,两组患者步频、步幅、步速、患侧摆动相和健侧摆动相均较治疗前明显提高(P<0.01),步宽、步态周期、双支撑相、患侧支撑相、健侧支撑相、步长偏差、健侧患侧支撑相比值和患侧健侧摆动相比值均较治疗前显著减小(P<0.01)。组间比较显示,观察组患者的步频、步幅、步速、步宽、步态周期、双支撑相、健侧支撑相、健侧摆动相、步长偏差、健侧患侧支撑相比值和患侧健侧摆动相比值改善均明显优于对照组(P<0.05或0.01)。结论:核心稳定性训练能有效改善脑卒中偏瘫患者步态时空参数和对称性参数,提高脑卒中偏瘫患者的步行功能和步态的对称性。