GPS水汽遥感中的大气干延迟局地订正模型研究

在GPS遥感水汽过程中,大气干延迟模型的精度直接影响水汽遥感的精度。根据广东清远站1995～2001年的气象探空资料,计算了GPS水汽测量中的实际大气干延迟。在此资料基础上,利用地面气象要素建立了大气干延迟的年和分月局地订正模型。分析结果表明,年模型的精度优于目前广泛使用的普适模型;月模型与年模型相比,效果不是很明显,建立局地分月订正模型意义不大;在对高度角的敏感程度上,局地模型略大些;当高度角小于75时,大气干延迟弯曲路径与直线路径之差ΔS随天顶角增大而迅速增大。     

松辽盆地南部深层地震资料处理方法研究

针对松辽盆地南部深层原始地震记录接收能量弱、信噪比低、各种干扰波严重和地表条件多变等存在严重的静校正问题，尝试利用CGG、Foucs、Grisys、Promax等多套处理系统的优势模块对跨工区二维大吾4面进行深部地层的折射波静校正、反褶积、剩余静校正、DipMoveOut、偏移等关键技术的联合处理方法试验研究，形成了一套针对松辽盆地南部深层地震资料处理的有效方法。     

MODIS 1B影像几何纠正方法研究及软件实现

MODIS影像是一种新型和重要的数据。对MODIS 1B影像几何畸变原因进行了深入分析，选择了一种合适的纠正方法。对于1km分辨率MODIS 1B影像，直接采用1km分辨率的空间坐标进行几何纠正；对于250m和500m分辨率的MODIS 1B影像，先用三次样条曲线对坐标进行插值生成同分辨率的坐标，然后利用坐标插值结果对其进行几何纠正。由于MODIS影像在空问分布上的特殊性，采用前向和后向映射相结合的方式确定纠正后某一像素点在原始影像中的位置。根据该位置的条带重叠度，可以确定参与计算像素个数、搜索窗口的大小以及窗口的精确位置；采用归一化反距离加权插值法计算纠正后像素点的属性值。上述优化算法不仅保证了纠正后影像的质量，而且提高了数据处理速度。作者在Visual C 6．0环境下开发实现了上述算法。从坐标插值和几何纠正结果分析，无论是数据处理速度还是纠正后的影像质量和精度均达到要求。     

应用高光谱遥感数据估算土壤表层水分的研究

土壤水分是土壤的重要组成部分，它在陆地表层和大气之间的物质和能量交换方面扮演着重要角色，寻求快速而准确的方法估算土壤水分具有重要意义。通常，从可见光一近红外对土壤表层水分的估计多是建立在土壤水分与反射率的关系之上的。而在土壤水分含量不高时，土壤水分的增加使土壤光谱反射率在整个波长范围内降低，尤其在760nm，970nm，1190nm，1450nm，1940nm和2950nm等水分吸收波段，而在土壤水分含量较高时，土壤水分的增加会使土壤光谱反射率在某些光谱波段升高。而土壤水分的估计往往是基于土壤水分与土壤水分吸收波段的吸收强度之间的线性关系上，虽然这些经验的方法对于估算某些土壤的表层水分含量是有效的，但这些关系应用于其它条件(如不同种类土壤、土壤湿度变化范围很大的情况)时却面临很多困难，这与土壤的光谱反射率是由土壤的组成成分(土壤水分、有机质、氧化铁和粘土矿物等)的含量和它们在土壤中的分布密切相关。微分技术处理“连续”的光谱是遥感中常用的数学方法，微分技术能部分消除低频光谱成分的影响。现在微分光谱已广泛地应用于研究植被的生物物理参数、矿物和有机质等。然而利用微分光谱对土壤水分反演的研究却鲜见报道。本文通过对实验室中多种不同类型的土壤进行光谱与土壤表层水分含量进行观测，探讨了通过土壤反射率与微分光谱对土壤表层水分的反演方法。4种类型的土壤光谱数据(反射率(R)，反射率倒数的对数(log(1／R))，反射率的一阶微分光谱(dR／dλ)，反射率倒数的对数的一阶微分光谱(d(log(1／R))／dλ))与土壤表层水分之间的关系在本文中得到分析，R与log(1／R)对于不同土壤类型与土壤表层水分都很敏感，说明通过R与log(1／R)反演土壤表层水分受土壤类型的影响很大，而dR／dλ，d(log(1／R))／dλ)对土壤类型却不敏感，对土壤表层水分较为敏感，说明dR／dλ和d(log(1／R))／dλ)对于反演不同类型土壤具有很大的潜力，微分光谱与土壤水分在某些波段具有显著的相关性。通过随机对9种土壤(各具有4个土壤水分)的数据建立反演土壤水分的模型，并其他9种土壤(各具有4个土壤水分)的数据进行验证模型，结果表明，dR／dλ和d(log(1／R))／dλ)能够显著提高R与log(1／R)对于不同土壤类型土壤表层水分的反演精度，由于吸收过程是非线性的，在四种类型的土壤光谱数据中，总体来说，d(log(1／R))／dλ)具有最好的能力预测不同类型土壤的表层水分含量。     

Computation of response spectra from mining tremors using neural networks

Underground coal and copper ore exploitation in two Polish mining regions causes mining tremors and a series of other negative phenomena in the environment. Although these tremors are strictly connected with human activity, they differ considerably from other paraseismic vibrations. The moment of their occurrence is not to be foreseen likewise for earthquakes. The main problem discussed in the paper was formulated as the neural network evaluation of a relation between mining tremor energies, epicentral distances and acceleration response spectra. Back-propagation neural networks with Resilient back-propagation learning method were used. Each input vector included information about the mining tremor energy and the epicentral distance. Values of acceleration response spectrum were expected as the outputs of neural networks. Neurally evaluated spectra were compared with spectra computed on the basis of experimental data. After the network is trained and tested, it can be used for mapping of new data of mining tremor energies and epicentral distances into the spectra. Then, what is the substantial advantage of neural approach, the prediction of acceleration response spectra can be performed without recording of surface vibrations. In the light of the results, it is visible that the presented way of computation of acceleration response spectra can be peculiarly applied to prognosis of mining tremors influences on structures.     

Study on comparison between absolute and relative input energy spectra and effects of ductility factor

Introduction Estimation of an attenuation relationship for strong ground motion parameters has been an interesting research subject in the field of engineering seismology and has played a very important role in seismic safety evaluation, seismic zoning, seismic hazard evaluation of major constructions, etc. At present, the generally used parameters include peak acceleration, peak velocity and elastic response spectrum. Such parameters mentioned above are essentially independent of the duration…     

Correcting method of eddy covariance fluxes over non-flat surfaces and its application in ChinaFLUX

Although Eddy Covariance (EC) technique is one of the best methods for estimating the energy and mass exchanges between underlying surface and atmosphere in micrometeorology, errors and uncertainties still exist without necessary corrections. In this paper, we will focus on the effect of coordinate system on the eddy fluxes. Based on the data observed over four sites (one farmland site, one grassland site and two forest sites), the effects of three coordinate system transforming methods (Double Rotation-DR, Triple Rotation-TR and Planar Fit-PF)on the turbulent fluxes are analyzed. It shows that (i) the corrected fluxes are more or less than the uncorrected fluxes, which is related mainly to the sloping degree of surface, wind speed and wind direction; and (ii) pitch angle has a sinusoidal dependence on wind direction, especially in the regular sloping terrain; and (iii) PF method is something like the simplification of TR or DR,and there are not obvious distinctions in correction in sloping grassland and flat farmland, but PF method is not suitable for uneven and irregular forest sites.     

浓硫酸钠水溶液中的离子对

不同浓度的Na2SO4水溶液的拉曼光谱显示了SO42-的四个拉曼活性带:980 cm-1处的SO42-的对称伸缩振动模式v1带,1 106 cm-1处的反对称伸缩振动模式v3带,448 cm-1处的变形振动模式v2带和617 cm-1处的变形振动模式v4带。482 cm-1处的肩膀峰是由于NaSO4-离子对的形成对448 cm-1的v2带的影响而形成的SO42-的一个新的振动峰。浓Na2SO4水溶液中,水共享离子对[Na+.H2O.SO42-]-是主要的离子对物种。随着Na2SO4水溶液浓度的增加,Na+和SO42-的相互作用增强,NaSO4-离子对所占的摩尔分数增加。     

Using raw regional climate model outputs for quantifying climate change impacts on hydrology

General circulation model outputs are rarely used directly for quantifying climate change impacts on hydrology, due to their coarse resolution and inherent bias. Bias correction methods are usually applied to correct the statistical deviations of climate model outputs from the observed data. However, the use of bias correction methods for impact studies is often disputable, due to the lack of physical basis and the bias nonstationarity of climate model outputs. With the improvement in model resolution and reliability, it is now possible to investigate the direct use of regional climate model (RCM) outputs for impact studies. This study proposes an approach to use RCM simulations directly for quantifying the hydrological impacts of climate change over North America. With this method, a hydrological model (HSAMI) is specifically calibrated using the RCM simulations at the recent past period. The change in hydrological regimes for a future period (2041–2065) over the reference (1971–1995), simulated using bias‐corrected and nonbias‐corrected simulations, is compared using mean flow, spring high flow, and summer–autumn low flow as indicators. Three RCMs driven by three different general circulation models are used to investigate the uncertainty of hydrological simulations associated with the choice of a bias‐corrected or nonbias‐corrected RCM simulation. The results indicate that the uncertainty envelope is generally watershed and indicator dependent. It is difficult to draw a firm conclusion about whether one method is better than the other. In other words, the bias correction method could bring further uncertainty to future hydrological simulations, in addition to uncertainty related to the choice of a bias correction method. This implies that the nonbias‐corrected results should be provided to end users along with the bias‐corrected ones, along with a detailed explanation of the bias correction procedure. This information would be especially helpful to assist end users in making the most informed decisions.