水平层状非均质横向同性地层中阵列多分量感应测井响应的快速计算

应用模式匹配算法研究建立水平层状非均质横向同性地层中多分量感应测井响应的快速算法.首先,利用Fourier级数展开法将多分量感应响应的数值模拟转化为三个轴对称问题,并利用电阻率径向导数的奇异表达式,引入两个附加奇异微分算子,用于描述柱状分界面上的积累面电荷对共面线圈系电磁响应的影响.然后通过模式匹配算法求解轴对称问题,得到水平层状非均质横向同性地层中多分量感应磁场的半解析解以及测井响应计算方法,最后通过数值模拟结果对该算法进行检验并进一步考察阵列多分量感应仪器的响应特征.     

热带地区100 hPa东风气流的气候效应(Ⅰ): 与华南气候的关系

利用NCEP／NCAR再分析资料、西太平洋副热带高压(下称西太副高)指数距平资料,分析了热带地区100hPa东风强度与华南气候之间的关系。结果表明：(1)冬季东风强度与同期及3月份华南降水存在显著的负相关关系。合成分析表明,弱东风年冬季华南地区700hPa为西南风距平,有利于孟加拉湾的暖湿空气向华南上空输送。(2)前期秋冬季节平均东风强度与区域平均的华南夏季气温之间存在很好的相关关系,相关系数达到-0．617。合成分析发现,弱东风年夏季西太副高偏强,位置偏西,使得华南地区在强副高控制之下,多为晴空少云天气,气温偏高。     

A new approach for separating mixed model parameters: application to simultaneous inversion of earthquake source parameters

A method for simultaneous determination of mixed model parameters, which have different physical dimensions or different responses to data, is presented. Mixed parameter estimation from observed data within a single model space shows instabilities and trade-offs of the solutions. We separate the model space into N-subspaces based on their physical properties or computational convenience and solve the N-subspaces systems by damped least-squares and singular-value decomposition. Since the condition number of each subsystem is smaller than that of the single global system, the approach can greatly increase the stability of the inversion. We also introduce different damping factors into the subsystems to reduce the trade-offs between the different parameters. The damping factors depend on the conditioning of the subsystems and may be adequately chosen in a range from 0.1 % to 10 % of the largest singular value. We illustrate the method with an example of simultaneous determination of source history, source geometry, and hypocentral location from regional seismograms, although it is applicable to any geophysical inversion.     

复奇异值分解方法的构造及度量特征

提出并构造出研究两个移动场相互关系的新方法——复奇异值分解方法（ＣＳＶＤ），并利用两个简单的正弦波场讨论了部分度量特征     

冬季热带太平洋和印度洋SSTA对大气协同作用的数值试验

通过比较热带太平洋和印度洋典型分布的海表温度距平（ＳＳＴＡ）场以及热带西太平洋孤立海域ＳＳＴＡ对大气环流影响的数值试验结果，发现两者存在明显的差异，前者与观测事实一致，由此说明大气环流异常是热带太平洋和印度洋ＳＳＴＡ协同作用的结果。     

Multiple time scales in rainfall variability

Monthly rainfall data averaged over a selected number of stations in peninsular Malaysia with a long record was subjected to singular spectrum analysis to determine the different modes of fluctuations in the rainfall. The analysis highlights the presence of fluctuations in the QBO time scale to a very long term time scale of 18–5 years which is possibly linked to lunar tidal forcing. There is also evidence of the Malaysian rainfall responding to El-Nino Southern Oscillation. An oscillation with a 7 to 10 year cycle is also evident. The annual cycle as a regular periodic oscillation is well established by the SSA.     

旋转地球流体的一个非线性奇异孤立波精确解

本文求出了旋转地球流体的f平面浅水波方程组的一个非线性精确解.这是一个处处连续、分片光滑的凹形孤立波,在波谷处为一尖点（奇异点）,其移速比长波速度gh^1/2略小,为（1+a/h）（gh（1+a/h））^1/2,其中a<0.其水平尺度与水深几乎无关.     

热带太平洋SST异常对IAP-9 LAGCM 年际变率影响的模拟

通过1960~1989年实测的热带太平洋(30.5°N~30.5°S,120°E~70°W)SST(热带太平洋区域以外用气候平均值)强迫AGCM得到的结果,以此来研究热带SST的变化对全球大气环流年际变化的影响。首先,我们分析了南方涛动,分别给出了Tahiti和Darwin海平面气压异常及赤道附近(-5°S~5°N)外逸长波辐射(OLR)时间演变,都能很好与观测相比较。然后,讨论了全球大气环流对热带SST的变化的响应,全球主要的遥相关型都能很好地再现。最后,通过奇异值分解(SVD)技术研究了热带SST与冬季北半球500 hPa位势高度主要的耦合型,模拟的相关型与NCEP再分析资料的相关型非常相似。     

Singular spectrum analysis and forecasting of hydrological time series

The singular spectrum analysis (SSA) technique is applied to some hydrological univariate time series to assess its ability to uncover important information from those series, and also its forecast skill. The SSA is carried out on annual precipitation, monthly runoff, and hourly water temperature time series. Information is obtained by extracting important components or, when possible, the whole signal from the time series. The extracted components are then subject to forecast by the SSA algorithm. It is illustrated the SSA ability to extract a slowly varying component (i.e. the trend) from the precipitation time series, the trend and oscillatory components from the runoff time series, and the whole signal from the water temperature time series. The SSA was also able to accurately forecast the extracted components of these time series.     

A distributed water-heat coupled model for mountainous watershed of an inland river basin in Northwest China (III) using the outputs from Mesoscale model version 5

The distributed water-heat coupled (DWHC) model is calibrated, with the help of the Mesoscale model version 5 (MM5), by calculating the daily precipitation, the daily average air temperature at the 2.0 m heights and the daily potential evaporation in Heihe mountainous watershed area and its vicinity (96.786°∼102.284°E, 37.328°∼40.601°N, 17 × 10⁴ km²), from February 11 to June 30, 2003. The MM5 model periodically ran every 10 days in 3 km × 3 km grid resolution with an integral time step of 3 s. In the MM5 model, many scheme or options are consulted or adopted, such as the Grell scheme cumulus parameterization method, the Dudhia option, the cloud-radiation scheme, MRF PBL option and the modified Oregon State University Land-surface model (OSULSM). According to the projection transform methods, the MM5 outputs are interpolated to the 1 km × 1 km grid in Alberts projection by using triangle-based cubic interpolation (Cubic) and nearest neighbor interpolation (Nearest) methods, with which the DWHC model shares the same method. The result shows that, when the Nearest method is used, the Nash-Sutcliffe equation value of the daily average runoff is 0.79, the balance error is −0.79% and the goodness of fit R ² value is 0.81. Meanwhile, when the Cubic method is used, the Nash-Sutcliffe equation value, the balance error and the R ² value are 0.79, −0.65% and 0.80, respectively. Though the runoff simulation result is not favorable, it is still better than that using measured data at the meteorological and hydrological stations; the latter has a Nash-Sutcliffe equation value of 0.61. The MM5-DWHC model results also show that runoff mainly occurs on land surfaces and from shallow soil layers. According to model calibration results, certain outputs of MM5 are singular to some extent and the DWHC model is very sensitive to the initial values.