正压大气非线性波的二级近似(二)——位涡度方程的求解

本文是用摄动法求取正压原始方程一类非线性大尺度慢波解的二级近似的第二部分,提出了一种在电子计算机上进行某些初等函数非数值运算的方法,由此求得了二级问题中位涡度方程的解.数值试验的结果表明,所求得的二级近似是合理的.     

Adaptive observations for hurricane prediction

Summary This study proposes a method that can be used to provide guidelines to aircraft reconnaissance for hurricane observations. The method combines numerical weather prediction (NWP) model with a statistical approach to target adaptive observations over areas where the hurricane predictions are very sensitive to the initial analysis for the NWP-model. A single model experiment is performed using regular initial analysis, while 50 other ensemble runs are performed from randomly perturbed initial states. Under the perfect model assumption, the single model experiment serves as a true state. The method first computes the forecast error variances at a certain verification time, e.g. hour 48, and then locates the maximum centers of variances. After the locations of the maximum forecast error variances are known, various correlations of different variables between these maximum variance points and the perturbation fields at the target time, e.g. hour 12, are calculated to identify those locations at the target time, over where the observational errors might be responsible for the growth of forecast error variances at the verification time. Statistically, these correlation fields indicate where the most sensitive areas are at the target time, i.e. where the need for additional observations is suggested. Hurricane Fran of 1996 is used to test the proposed method. The reason for choosing this case is that, during the first 48 hour forecast, the track forecast from NWP-model was very close to the best track. Two additional experiments were designed to examine the method. One experiment updates predicted variables at the target time (12 h) over the areas, to where the proposed method indicates the forecast would be sensitive. The updating combines observations (or truth) with the first guess (predicted) fields. Another experiment also modifies predicted variables at the target time (12 h), but over the areas where the method indicates the forecast errors are less correlated to. The results show that the modification has greatly reduced the forecast error variances at the verification time (48 h) in the first experiment, however it has a very little impact on the variance fields at the forecast hour (48 h) in the second experiment. It is very clear from our experiments, that the proposed method is able to identify sensitive areas, where additional observations can help to reduce hurricane forecast errors from an NWP-model. Received July 19, 1999 Revised November 28, 1999     

Comparison of three methods for estimating the sea level rise effect on storm surge flooding

Two linear methods, including the simple linear addition and linear addition by expansion, and numerical simulations were employed to estimate storm surges and associated flooding caused by Hurricane Andrew for scenarios of sea level rise (SLR) from 0.15 m to 1.05 m with an interval of 0.15 m. The interaction between storm surge and SLR is almost linear at the open Atlantic Ocean outside Biscayne Bay, with slight reduction in peak storm surge heights as sea level rises. The nonlinear interaction between storm surges and SLR is weak in Biscayne Bay, leading to small differences in peak storm surge heights estimated by three methods. Therefore, it is appropriate to estimate elevated storm surges caused by SLR in these areas by adding the SLR magnitude to storm surge heights. However, the magnitude and extent of inundation at the mainland area by Biscayne Bay estimated by numerical simulations are, respectively, 22–24 % and 16–30 % larger on average than those generated by the linear addition by expansion and the simple linear addition methods, indicating a strong nonlinear interaction between storm surge and SLR. The population and property affected by the storm surge inundation estimated by numerical simulations differ up to 50–140 % from that estimated by two linear addition methods. Therefore, it is inappropriate to estimate the exacerbated magnitude and extent of storm surge flooding and affected population and property caused by SLR by using the linear addition methods. The strong nonlinear interaction between surge flooding and SLR at a specific location occurs at the initial stage of SLR when the water depth under an elevated sea level is less than 0.7 m, while the interaction becomes linear as the depth exceeds 0.7 m.     

Joint variable spatial downscaling

Joint Variable Spatial Downscaling (JVSD), a new statistical technique for downscaling gridded climatic variables, is developed to generate high resolution gridded datasets for regional watershed modeling and assessments. The proposed approach differs from previous statistical downscaling methods in that multiple climatic variables are downscaled simultaneously and consistently to produce realistic climate projections. In the bias correction step, JVSD uses a differencing process to create stationary joint cumulative frequency statistics of the variables being downscaled. The functional relationship between these statistics and those of the historical observation period is subsequently used to remove GCM bias. The original variables are recovered through summation of bias corrected differenced sequences. In the spatial disaggregation step, JVSD uses a historical analogue approach, with historical analogues identified simultaneously for all atmospheric fields and over all areas of the basin under study. Analysis and comparisons are performed for 20th Century Climate in Coupled Models (20C3M), broadly available for most GCMs. The results show that the proposed downscaling method is able to reproduce the sub-grid climatic features as well as their temporal/spatial variability in the historical periods. Comparisons are also performed for precipitation and temperature with other statistical and dynamic downscaling methods over the southeastern US and show that JVSD performs favorably. The downscaled sequences are used to assess the implications of GCM scenarios for the Apalachicola-Chattahoochee-Flint river basin as part of a comprehensive climate change impact assessment.     

脓毒症患者中横纹肌溶解综合征与预后关系的研究

目的 探讨脓毒症患者中横纹肌溶解综合征（RM）与病情严重程度及预后的关系。方法 采用前瞻性研究方法,入选2014年10月~2015年9月重庆医科大学附属第一医院中心ICU连续收治的脓毒症患者151例作为研究对象。检测入ICU当天的血清肌酸激酶（CK）,以CK≥1000U/L作为RM的诊断标准,比较发生RM的患者与未发生RM患者的感染标志物、危重症评分、器官功能障碍和28天病死率等。结果 151例脓毒症患者中37例（24.5%）发生RM。RM组的PCT水平、APACHEⅡ评分和SOFA评分较非RM组高（P<0.05）。RM组ICU期间肝脏功能障碍、循环障碍、肾脏功能障碍的发生率、人均器官障碍数、血肌酐峰值水平、接受连续肾脏替代治疗（CRRT）患者的比例和28天病死率较非RM组高（P<0.05）,而呼吸功能障碍、凝血功能障碍和神经系统障碍的发生率比较,差异无统计学意义（P>0.05）。CK对预测脓毒症患者28天死亡风险具有统计学意义,ROC曲线下面积（AUC）为0.694（P<0.05）,低于APACHEⅡ评分（0.841）和SOFA评分（0.805）（P<0.05）。结论 脓毒症患者中RM的发生率高,发生RM的患者的病情更危重,其ICU期间肝脏功能障碍、循环障碍、肾脏功能障碍的发生率和28天病死率均显著增加,CK可以作为预测脓毒症患者28天病死率的参考指标。     

使用VMware模拟双机HA环境及其应用前景

介绍使用虚拟计算机的概念和发展现状及VMware主要产品的区别,对于VMware的3个主要产品Work-station、GSX Server和ESX Server,给出了合理的使用建议,并论述了采用虚拟机所带来的好处以及在国家气象信息中心的应用前景。该文还介绍了VMware GSX Server的使用方法和注意事项。最后通过逐步的讲解,实现了一个虚拟的Suse Linux双机加共享存储阵列的高可用测试(HA)环境,以替代现实中昂贵的硬件环境。     

单波高精度测距系统的研究

本文介绍了测距精度优于１０＾－６的自动高精度测距系统的构成、原理及春应用。     

Statistical diagnosis and gross error test for semiparametric linear model

This paper systematically studies the statistical diagnosis and hypothesis testing for the semiparametric linear regression model according to the theories and methods of the statistical diagnosis and hypothesis testing for parametric regression model. Several diagnostic measures and the methods for gross error testing are derived. Especially, the global and local influence analysis of the gross error on the parameter X and the nonparameter s are discussed in detail; at the same time, the paper proves that the data point deletion model is equivalent to the mean shift model for the semiparametric regression model. Finally, with one simulative computing example, some helpful conclusions are drawn.     

Ionospheric and receiver DCB-constrained multi-GNSS single-frequency PPP integrated with MEMS inertial measurements

Single-frequency precise point positioning (SF-PPP) is a potential precise positioning technique due to the advantages of the high accuracy in positioning after convergence and the low cost in operation. However, there are still challenges limiting its applications at present, such as the long convergence time, the low reliability, and the poor satellite availability and continuity in kinematic applications. In recent years, the achievements in the dual-frequency PPP have confirmed that its performance can be significantly enhanced by employing the slant ionospheric delay and receiver differential code bias (DCB) constraint model, and the multi-constellation Global Navigation Satellite Systems (GNSS) data. Accordingly, we introduce the slant ionospheric delay and receiver DCB constraint model, and the multi-GNSS data in SF-PPP modular together. In order to further overcome the drawbacks of SF-PPP in terms of reliability, continuity, and accuracy in the signal easily blocking environments, the inertial measurements are also adopted in this paper. Finally, we form a new approach to tightly integrate the multi-GNSS single-frequency observations and inertial measurements together to ameliorate the performance of the ionospheric delay and receiver DCB-constrained SF-PPP. In such model, the inter-system bias between each two GNSS systems, the inter-frequency bias between each two GLONASS frequencies, the hardware errors of the inertial sensors, the slant ionospheric delays of each user-satellite pair, and the receiver DCB are estimated together with other parameters in a unique Kalman filter. To demonstrate its performance, the multi-GNSS and low-cost inertial data from a land-borne experiment are analyzed. The results indicate that visible positioning improvements in terms of accuracy, continuity, and reliability can be achieved in both open-sky and complex conditions while using the proposed model in this study compared to the conventional GPS SF-PPP.