基于IPCC预测结果的北太平洋海表面温度变化分析

利用IPCC-AR4气候模式诊断与比较计划(PCMDI)20C3M试验和A1B情景试验模拟数据,研究了在温室气体排放情景下,北太平洋海表面温度的变化及其对太平洋风应力旋度变化的响应。结果表明,温室气体中等排放A1B情景与20C3M情景相比,北太平洋年平均海表面温度表现为一致增温的趋势,且最大的增温中心位于黑潮及其延伸体区。与20C3M试验相比,CO2增加情景下北太平洋中部东风加强,增加向北的Ekman输送,使得北太平洋内区增温。风应力旋度零线也向北略有移动,导致黑潮延伸体向北移动并得到加强,从而引起延伸体区较强增温。风应力旋度零线的纬度附近产生的Rossby波,向西传播到黑潮延伸体区,进一步加强黑潮延伸体区的温度异常。海洋对北太平洋风应力场变化的局地响应及延迟响应,使黑潮延伸体海域海表面增温远大于周围海区。     

Receiver Functions from Autoregressive Deconvolution

Summary Receiver functions can be estimated by minimizing the square errors of Wiener filter in time-domain or spectrum division in frequency domain. To avoid the direct calculation of auto-correlation and cross-correlation coefficients in Toeplitz equation or of auto-spectrum and cross-spectrum in spectrum division equation as well as empirically choosing a damping parameter, autoregressive deconvolution is presented to isolate receiver function from three-component teleseismic P waveforms. The vertical component of teleseismic P waveform is modeled by an autoregressive model, which can be forward and backward, predicted respectively. The optimum length of the autoregressive model is determined by the Akaike criterion. By minimizing the square errors of forward and backward predicting filters, autoregressive filter coefficients can be recursively solved, and receiver function is also estimated in the similar procedure. Both synthetic and real data tests show that autoregressive deconvolution is an effective method to isolate receiver function from teleseismic P waveforms in time-domain.     

Quantifying urbanization-associated changes in terrestrial hydrologic system memory

A common feature of watershed urbanization is increased hydrograph ‘flashiness,’ whereby river discharge fluctuations grow more erratic. Such changes might be intuitively interpreted as a decrease in watershed-scale hydrologic system memory. Here, I investigate this hypothesis through a paired-catchment experiment. The serial correlation coefficient, a common metric of short-term time series memory, is applied to daily winter streamflow data from urbanizing and rural watersheds in the Puget Sound lowland of Washington State, USA. Statistical comparisons confirm that this metric shows highly significant decreases over time in the catchment undergoing land use change, but not in the control watershed, which remains rural over the hydrometric record. Moreover, the mean serial correlation coefficients are statistically indistinguishable between the two catchments over the early period of record, when both watersheds are largely rural, whereas the system memory is far weaker in the urbanized stream relative to the rural stream over the late period, following land use change in the former. The results appear readily interpretable in terms of the physical hydrologic changes typically associated with urbanization. The serial correlation coefficient thus appears to be an instructive measure of urbanization impacts for small streams in this region.     

Study on automatic recognition of the first motion in a seismic event

IntroductionSeismic detection is one of the most effective methods to monitor underground explosions. When an underground explosion is conducted, like an earthquake, it will radiate seismic waves. Stimulating from sources, these seismic waves travel through the Earth(s interior and surface, propagating widespread. These waves can be recorded by seismograph systems on surface. By studying the recorded seismic signals, we can obtain much detailed information on such a seismic event. We can the…     

地震事件初动点的自动判定研究

研究了地震信号到来前背景噪声的波形规律,建立起表征背景噪声的自回归模型,并编制了相应的建模程序和地震信号初动点自动判定程序．对25次地下爆炸地震事件的初动点进行判定,并给出了自动判定和人工判读的结果．      

基于ARMA模型的地磁偏角缺数处理方法

针对地震仪器记录的地磁数据存在单点缺失和连续多点缺失而不利于地震数据处理和地震预报的问题,同时为了快速处理非震异常值,本文提出将时间序列自回归移动平均（ARMA）预测模型用于地磁数据插值处理,并与均值插值、线性插值进行对比分析。结果表明,均值插值、线性插值和 ARMA 模型单点缺失的平均标准误差分别为 0.110 2、0.006 9 和 0.000 1,连续多点缺失的平均标准误差分别为 0.258 23、0.194 2 和 0.004 86,说明 ARMA 模型在单点缺失和连续多点缺失时均具有较低标准误差,且能很好地保持实际观测序列的曲线形态,插值效果较好,有望成为地磁数据序列处理的一种新方法。     

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混合模型在沉降监测中的组合预测方法

对建筑物沉降数据进行建模预测,以便掌握其变形规律并预测变形趋势,保障建筑物的安全。单一预测模型有自己的适用情形,也存在各自的缺点,已经不能满足当前的精度要求。选取灰色GM(1,1)和自回归两种常用的预测模型,通过两种不同的方式进行组合预测,并结合南京市地铁一号线百家湖段沉降监测数据进行计算分析,结果表明两种组合方法与单一预测模型相比精度均有所提高。