由一次西周日食记载得到的地球自转变化

本文结合１９９７年３月９日全食的新疆观测及地球自转变化趋势讨论了发生在中国西周时期的一次日食的记载，以及由此得到的地球自转速度变化值。     

测氡法快速确定油气田边界及深度

本介绍了运用测氡法快速确定大庆××油气田边界，估算其埋藏深度试验，结果表明，该方法具有可行性和实用性。由于所做工作有限，尚属经验性探讨，还必须从理论和实践上进一步地工作，进而充实、完善它。     

Dynamic stochastic estimation of physical variables

A fundamental problem facing the physical sciences today is analysis of natural variations and mapping of spatiotemporal processes. Detailed maps describing the space/time distribution of groundwater contaminants, atmospheric pollutant deposition processes, rainfall intensity variables, external intermittency functions, etc. are tools whose importance in practical applications cannot be overestimated. Such maps are valuable inputs for numerous applications including, for example, solute transport, storm modeling, turbulent-nonturbulent flow characterization, weather prediction, and human exposure to hazardous substances. The approach considered here uses the spatiotemporal random field theory to study natural space/time variations and derive dynamic stochastic estimates of physical variables. The random field model is constructed in a space/time continuum that explicitly involves both spatial and temporal aspects and provides a rigorous representation of spatiotemporal variabilities and uncertainties. This has considerable advantages as regards analytical investigations of natural processes. The model is used to study natural space/time variations of springwater calcium ion data from the Dyle River catchment area, Belgium. This dataset is characterized by a spatially nonhomogeneous and temporally nonstationary variability that is quantified by random field parameters, such as orders of space/time continuity and random field increments. A rich class of covariance models is determined from the properties of the random field increments. The analysis leads to maps of continuity orders and covariances reflecting space/time calcium ion correlations and trends. Calcium ion estimates and the associated statistical errors are calculated at unmeasured locations/instants over the Dyle region using a space/time kriging algorithm. In practice, the interpretation of the results of the dynamic stochastic analysis should take into consideration the scale effects.     

Flexible spectral methods for the generation of random fields with power-law semivariograms

Random field generators serve as a tool to model heterogeneous media for applications in hydrocarbon recovery and groundwater flow. Random fields with a power-law variogram structure, also termed fractional Brownian motion (fBm) fields, are of interest to study scale dependent heterogeneity effects on one-phase and two-phase flow. We show that such fields generated by the spectral method and the Inverse Fast Fourier Transform (IFFT) have an incorrect variogram structure and variance. To illustrate this we derive the prefactor of the fBm spectral density function, which is required to generate the fBm fields. We propose a new method to generate fBm fields that introduces weighting functions into the spectral method. It leads to a flexible and efficient algorithm. The flexibility permits an optimal choice of summation points (that is points in frequency space at which the weighting function is calculated) specific for the autocovariance structure of the field. As an illustration of the method, comparisons between estimated and expected statistics of fields with an exponential variogram and of fBm fields are presented. For power-law semivariograms, the proposed spectral method with a cylindrical distribution of the summation points gives optimal results.     

Simulation of normal distributed smooth fields by Karhunen-Loéve expansion in combination with kriging

Simulation of multigaussian stochastic fields can be made after a Karhunen-Loéve expansion of a given covariance function. This method is also called simulation by Empirical Orthogonal Functions. The simulations are made by drawing stochastic coefficients from a random generator. These numbers are multiplied with eigenfunctions and eigenvalues derived from the predefined covariance model. The number of eigenfunctions necessary to reproduce the stochastic process within a predefined variance error, turns out to be a cardinal question. Some ordinary analytical covariance functions are used to evaluate how quickly the series of eigenfunctions can be truncated. This analysis demonstrates extremely quick convergence to 99.5% of total variance for the 2nd order exponential (‘gaussian’) covariance function, while the opposite is true for the 1st order exponential covariance function. Due to these convergence characteristics, the Karhunen-Loéve method is most suitable for simulating smooth fields with ‘gaussian’ shaped covariance functions. Practical applications of Karhunen-Loéve simulations can be improved by spatial interpolation of the eigenfunctions. In this paper, we suggest interpolation by kriging and limits for reproduction of the predefined covariance functions are evaluated.     

Simulation of normal distributed smooth fields by Karhunen-Loéve expansion in combination with kriging

Simulation of multigaussian stochastic fields can be made after a Karhunen-Loéve expansion of a given covariance function. This method is also called simulation by Empirical Orthogonal Functions. The simulations are made by drawing stochastic coefficients from a random generator. These numbers are multiplied with eigenfunctions and eigenvalues derived from the predefined covariance model. The number of eigenfunctions necessary to reproduce the stochastic process within a predefined variance error, turns out to be a cardinal question. Some ordinary analytical covariance functions are used to evaluate how quickly the series of eigenfunctions can be truncated. This analysis demonstrates extremely quick convergence to 99.5% of total variance for the 2nd order exponential (‘gaussian’) covariance function, while the opposite is true for the 1st order exponential covariance function. Due to these convergence characteristics, the Karhunen-Loéve method is most suitable for simulating smooth fields with ‘gaussian’ shaped covariance functions. Practical applications of Karhunen-Loéve simulations can be improved by spatial interpolation of the eigenfunctions. In this paper, we suggest interpolation by kriging and limits for reproduction of the predefined covariance functions are evaluated.     

日地水文学与灾害预测

研究地球水文变化的日地物理成因和规律的日地水文学（Solar-TerrestrialHydrology），用于水、旱灾害预测多次获得证实.本文从日地水文物理基础、长江淮河22年周期性大洪水、黄河大洪水和太阳活动关系、太阳活动双重衰减期北方大旱、17世纪日地水文异常变化、太阳活动对暴雨洪水中短期影响、台湾海峡两侧大暴雨洪水落区转移、全球最大洪水日地水文研究以及学科发展和应用前景九个方面，对中国近70多年来的主要成果加以回顾和总结.基础科学、高科技和应用紧密结合，促成对国民经济发展直接相关的新学科，是当代科学进步的一个显著标志，这里又是一个例证．     

被动微波遥感估算雪水当量研究进展与展望

被动微波遥感可以透过云层，全天候地提供地表一定深度的信息。星载被动微波遥感传感器的时间分辨率很高，在冰冻圈动态研究中有着重要的地位。在最近的二三十年中，大量被动微波遥感的应用都是在美国、加拿大、欧洲等地，而我国在这方面的研究相对较少。首先介绍了被动微波遥感数据在监测积雪方面的国内外研究进展，对现存的雪水当量（SWE）估算算法（和模型）的适用性进行讨论。然后，详细讨论了我国西部的青藏高原地区雪水当量的估算，阐明了利用SSM／I数据估算青藏高原地区雪水当量的复杂性，并指出了其复杂性产生的原因，提出了解决问题的方法，为该地区积雪动态的进一步研究提供了理论依据。     

Recent Progress and Future Prospects in Solar Physics, and Their Relevance for Planet Earth

Understanding our star, the Sun, is of fundamental interest for life on Earth. In this paper, the status of knowledge in solar physics of roughly two decades ago is summarised, and the most recent developments in this very active field are shown, many of them achieved by means of space based missions. The Sun–Earth connection is described and, finally, an outlook on future solar research is given.     

The possible connection between ionization in the atmosphere by cosmic rays and low level clouds

Recent analysis of monthly mean cloud data from the International Satellite Cloud Climatology Project uncovered a strong correlation between low cloud and the cosmic ray flux for extensive regions of the Earth. Additional data have been recently released covering the period up to September 2001 with which we have made a new study of the geographical variation of the correlation between low cloud and predicted ionization level from cosmic rays at an altitude of 2 km. When analysed globally, we find that the correlations do not correspond to the latitude variation of cosmic ray flux and they are not field significant. Nonetheless they appear to be marginally field significant over broad latitude and longitude bands with a peak positive correlation at 50 degrees North and South and a tendency to negative correlation at lower latitudes. The correlation is strongest over the North and South Atlantic. Several of these features are consistent with the predictions of the electroscavenging process.We use a simple model to calculate the climatic impact should the correlation be confirmed. We show that, under the most favorable conditions, a reduction in low cloud cover since the late 19th century, combined with the direct forcing by solar irradiance can explain a significant part of the global warming over the past century, but not all. However, this computation assumes that there is no feedback or changes in cloud at other levels.