FOURIER INFERENCE: SOME METHODS FOR THE ANALYSIS OF ARRAY AND NONGAUSSIAN SERIES DATA1

Fourier inference is a collection of analytic techniques and philosophic attitudes, for the analysis of data, wherein essential use is made of empirical Fourier transforms. This paper sets down some basic results concerning the finite Fourier transforms of stationary process data and then, to illustrate the approach, uses those results to develop procedures for: 1) estimating cloud and storm motion, 2) passive sonar and 3) fitting finite parameter models to nonGaussian time series via bispectral fitting. This last procedure is illustrated by an analysis of a stretch of Mississippi River runoff data. Examples 1), 2) refer to data having the form Y(x_j, y_j, t) for j = 1, …, J and t = 0, …, T-l say, and view that data as part of a realization of a spatial-temporal process. Such data has become common in geophysics generally and in hydrology particularly. The goal of this paper is to present some new statistical procedures pertinent to problems in the water sciences, equally it is to illustrate the genesis of those procedures and how their properties may be approximated.     

PARAMETRIC/NONPARAMETRIC MIXTURE DENSITY ESTIMATION WITH APPLICATION TO FLOOD-FREQUENCY ANALYSIS1

Much attention has been invested in the model choice problem for peak annual flows, in the context of flood frequency analysis. The authors would sidestep this dilemma through non-parametric density estimation methodology, but recognize that the standard nonparametric estimators preclude the use of prior information and related data, and furthermore have virtually no tail at all. Here we offer a remedy for these inadequacies by introducing an estimator which mixes parametric and nonparametric density estimates. We prove that our mixture rule is consistent. By this procedure, we do allow incorporation of prior information, experience, and regional data information, but nevertheless provide a safeguard against incorrect model choice.     

PAST AND FUTURE OF ANALYSIS OF WATER RESOURCES TIME SERIES1

ABSTRACT: water resources supply and demand time series consist of several or all of the four basic characteristics: tendency, intermittency, periodicity and stochasticity. Their importance changes from one type of variables to another. Historic developments of analysis of time series in hydrology have varied significantly over the past, from the stress on search for periodicities and persistence in annual series to the emphasis on the series stochastic properties. Supply and demand series are often highly interrelated, which fact is most often neglected in planning water resources systems in general, and water storage capacities in particular. The future of series analysis in water resources will likely be by a joint use of physically-based structural analysis and the use of advanced methods of treating data by stochastic processes, statistical estimation and inference techniques. The most intriguing challenge of the future of this analysis may be the treatment of nonnormal, nonlinear and in general nonstationary hydrologic and water use time series. The proper treatment of complex multivariate processes will also challenge the specialists, especially for the purposes of transfer of information between data on variables at given points, or between data at several points of a given variable, or both.     

Synchronous variation in water chemistry for 80 lakes in Southern Sweden

Variation in water chemistry was studied in 80 lakes in southern Sweden. The lakes had forest dominated catchments. The length of the time series was 14 years. Synchrony was calculated as Pearsons product moment correlation coefficients for all combinations of lakes, i.e. 3160 lake-pairs. The chemical variables studied were non-marine sulphate (SO₄ ^*), non-marine calcium (Ca^*), absorbance and acid neutralising capacity (ANC). Statistically significant synchrony occurred in 93% of all lake-pairs for SO₄ ^*, and between 58 and 67% for absorbance, Ca^* and ANC. In 70% of all lake-pairs, the synchrony was \s>0.71 for SO₄ ^*, which means that more than half of the variation in one lake could be explained by the variation in the other lake. For absorbance, Ca^* and ANC, about 25% of the lake-pairs had a synchrony \s>0.71. The relatively high synchrony for SO₄ ^* occurred during an overall downward trend in SO₄ ^* concentration.The degree of synchrony in our study was at a level comparable to other studies in northern America and England. However, our study included lakes in a much larger area, with distances of up to 500 km between the lakes, while earlier studies were made on small lake districts with lakes located within approximately 50 km. In contrast to these earlier studies, there was no correlation between synchrony and distance, lake characteristics or catchment characteristics. However, when a small subset of 15 lakes in the southeast of Sweden was selected, such relations were found.     

酒石酸钾钠溶液对氨氮标准曲线的影响

通过对酒石酸钾钠溶液存放时间和保存条件的跟踪试验,发现该试剂的存放时间越长,绘制的氨氮标准曲线的截距就越大;并分析了在绘制氨氮标准曲线时显色液易浑浊和截距常常偏大的原因;就此提出了酒石酸钾钠溶液的有效期和保存方法,以进一步提高氨氮测定的准确度。     

利用曲线补偿变时溶出示差技术测定Cu、Pb、Zn、Cd初步探讨

本文利用曲线补偿，变时溶出示差技术提高了测定Ｃｕ、Ｐｂ、Ｚｎ、Ｃｄ的灵敏度、精密度和准确度，进一步改变了谱图的分辨率，避免了Ｏ２波的干扰     

镉的激光时间分辨荧光测定

本文报道了以７－碘－８－羟基喹啉－５－磺酸（Ｆｅｒｏｎ）作荧光增强剂测定镉的激光时间分辨荧光分析方法。在１．５～１０００ｎｇ／ｍｌ的范围内，镉浓度和荧光强度呈良好的线性关系（γ＞０．９９９），方法的检出限为１．５ｎｇ／ｍｌ。可用于水中镉的测定，也可用于其它环境样品的测定。     

县级环境监测站首次计量认证工作的探讨

分析了县级环境监测站的现状,探讨了首次计量认证工作的程序、应注意的问题。     

超高层建筑风致响应中的数据挖掘

超高层建筑的风致响应问题已经越来越受到人们的重视.应用数据挖掘技术对广州中信广场大厦的实测数据进行了分析,并对该原型实测风致响应中的时间序列数据进行了相似性挖掘,发现了一些特征和规律,对该问题的进一步研究具有一定的参考价值.