预报水下岸坡剖面风暴变形的可能性

提出了在海浪对海岸作用过程中由于泥沙冲淤所造成的水下斜坡剖面演化的数学模式，导出了描述海底变形的方程。该方程属于非线性热传导方程一类。在确定边界条件时，曾考虑了对于给定的海浪状况下实际剖在对其均衡状态适应的程度。还介绍了在波浪原始参数和水下斜坡特征不同值时，海底由最初线性剖面的演变，指出理论预测的底形变化趋势与实测的底形变化趋势类似。     

Induction of vitellogenin in vivo and in vitro in the model teleost medaka (Oryzias latipes): comparison of gene expression and protein levels

Quantification of the egg yolk precursor vitellogenin (VTG) in fish has become a standard technique to detect estrogenic effects of known chemicals and environmental samples. In the present study, we have analysed VTG induction by estradiol, ethynylestradiol and genistein exposure in the model teleost medaka (Oryzias latipes) and demonstrate that the medaka is a suitable model system to analyse estrogenic effects. By comparing VTG gene expression and protein levels we show that in principal both techniques can be used to study VTG induction in vivo (juvenile and adult males) and in vitro (primary cultures of male liver cells). If a short term in vivo or in vitro exposure is performed, detection of mRNA might be sufficient. For long term studies with the need to detect weak estrogenic chemicals and a precise quantification, immuno-chemical detection may be favoured.     

Method for predicting cuttings transport using artificial neural networks in foam drilling

Foam is used widely in underbalanced drilling for oil and gas exploration to improve well perfor-mance.Accurate prediction of the cutting transport and pressure loss in the foam drilling is an important way to prevent stuck pipe,lost circulation and to increase the rate of penetration(ROP).In foam drilling,the cuttings transport quality may be defined in terms of cuttings consistency and downhole pressure loss,which are controlled by many factors.Therefore,it is very difficult to establish the mathematical equation that reflects nonlinear relationship among various factors.The field and experimental measurements of these parameters are time consuming and costly.In this study,the authors suggest a cuttings transport mathematical modeling using BPN(back propagation network),RBFN(radial basis function network)and GRNN(general regression neural network)based on various experiment data of cuttings transport of previous researchers and compared the result with experiment data.Results of this study show that the GRNN has a correlation coefficient of 0.99962 and an average error of 0.15 in training datasets,and a correlation coefficient of 0.99881 and an average error of 0.612 in testing datasets,which has higher accuracy and faster training velocity than the BP network or RBFN network.GRNN can be used in many mathematical problems for accurate estimation of cuttings consistency and downhole pressure loss instead of field and experimental measurements for hydraulic design in foam drilling operation.     

An Unusual Porphyritic Texture of the Fangshan Granodiorite

A remarkable character of the Fangshan (房山) granodiorite isthe abundance of the large phenocrystic orthoclase perthite throughoutthe central part of the igenous body. They project on the weathered     

Spatial Interpolation of Daily Precipitation in China:1951-2005

Climate research relies heavily on good quality instrumental data; for modeling efforts gridded data are needed. So far, relatively little effort has been made to create gridded climate data for China. This is especially true for high-resolution daily data. This work, focuses on identifying an accurate method to produce gridded daily precipitation in China based on the observed data at 753 stations for the period 1951-2005. Five interpolation methods, including ordinary nearest neighbor, local polynomial, r...     

Technical note on a track-pattern-based model for predicting seasonal tropical cyclone activity over the western North Pacific

Recently, the National Typhoon Center (NTC) at the Korea Meteorological Administration launched a track-pattern-based model that predicts the horizontal distribution of tropical cyclone (TC) track density from June to October. This model is the first approach to target seasonal TC track clusters covering the entire western North Pacific (WNP) basin, and may represent a milestone for seasonal TC forecasting, using a simple statistical method that can be applied at weather operation centers. In this note, we describe the procedure of the track-pattern-based model with brief technical background to provide practical information on the use and operation of the model. The model comprises three major steps. First, long-term data of WNP TC tracks reveal seven climatological track clusters. Second, the TC counts for each cluster are predicted using a hybrid statistical-dynamical method, using the seasonal prediction of large-scale environments. Third, the final forecast map of track density is constructed by merging the spatial probabilities of the seven clusters and applying necessary bias corrections. Although the model is developed to issue the seasonal forecast in mid-May, it can be applied to alternative dates and target seasons following the procedure described in this note. Work continues on establishing an automatic system for this model at the NTC.     

Two Types of Diurnal Variations in Heavy Rainfall during July over Korea※

This study examined the characteristics of the diurnal variations of heavy rainfall (≥110 mm in 12 hours) in Korea and the related atmospheric circulation for July from 1980?2020. During the analysis period, two dominant pattens of diurnal variation of the heavy rainfall emerged: all-day heavy rainfall (AD) and morning only heavy rainfall (MO) types. For the AD-type, the heavy rainfall is caused by abundant moisture content in conjunction with active convection in the morning (0000?1200, LST; LST = UTC + 9) and the afternoon hours (1200?2400 LST). These systems are related to the enhanced moisture inflow and upward motion induced by the strengthening of the western North Pacific subtropical high and upper-tropospheric jet. For the MO-type, heavy rainfall occurs mostly in the morning hours; the associated atmospheric patterns are similar to the climatology. We find that the atmospheric pattern related to severe heavy rainfalls in 2020 corresponds to a typical AD-type and resembles the 1991 heavy-rainfall system in its overall synoptic/mesoscale circulations. The present results imply that extremely heavy rainfall episodes in Korea during the 2020 summer may occur again in the future associated with the recurring atmospheric phenomenon related to the heavy rainfall.