THE DOWNSCALING FORECASTING OF SEASONAL PRECIPITATION IN GUANGDONG BASED ON CLIMATE FORECAST SYSTEMS PRODUCTS

The Climate Forecast Systems(CFS) datasets provided by National Centers for Environmental Prediction(NCEP), which cover the time from 1981 to 2008, can be used to forecast atmospheric circulation nine months ahead. Compared with the NCEP datasets, CFS datasets successfully simulate many major features of the Asian monsoon circulation systems and exhibit reasonably high skill in simulating and predicting ENSO events. Based on the CFS forecasting results, a downscaling method of Optimal Subset Regression(OSR) and mean generational function model of multiple variables are used to forecast seasonal precipitation in Guangdong. After statistical analysis tests, sea level pressure, wind and geopotential height field are made predictors. Although the results are unstable in some individual seasons, both the OSR and multivariate mean generational function model can provide good forecasting as operational tests score more than sixty points. CFS datasets are available and updated in real time, as compared with the NCEP dataset. The downscaling forecast method based on the CFS datasets can predict three seasons of seasonal precipitation in Guangdong, enriching traditional statistical methods. However, its forecasting stability needs to be improved.

THE VARIABILITY CHARACTERISTICS AND PREDICTION OF GUANGDONG POWER LOAD DURING 2002 – 2004

The variability characteristics of Guangdong daily power load from 2002 to 2004 and its connection to meteorological variables are analyzed with wavelet analysis and correlation analysis. Prediction equations are established using optimization subset regression. The results show that a linear increasing trend is very significant and seasonal change is obvious. The power load exhibits significant quasi-weekly (5 – 7 days) oscillation, quasi-by-weekly (10 – 20 days) oscillation and intraseasonal (30 – 60 days) oscillation. These oscillations are caused by atmospheric low frequency oscillation and public holidays. The variation of Guangdong daily power load is obviously in decrease on Sundays, shaping like a funnel during Chinese New Year in particular. The minimum is found at the first and second day and the power load gradually increases to normal level after the third day during the long vacation of Labor Day and National Day. Guangdong power load is the most sensitive to temperature, which is the main affecting factor, as in other areas in China. The power load also has relationship with other meteorological elements to some extent during different seasons. The maximum of power load in summer, minimum during Chinese New Year and variation during Labor Day and National Day are well fitted and predicted using the equation established by optimization subset regression and accounting for the effect of workdays and holidays.

Experimental Design of Measuring Soil-Water Characteristic Curve of Unsaturated Soil Using Bayesian Approach

Soil-water characteristic curve (SWCC) is significant to estimate the site-specific unsaturated soil properties (such as unsaturated shear strength and coefficient of permeability) for geotechnical analyses involving unsaturated soils. Determining SWCC can be achieved by fitting data points obtained according to the prescribed experimental scheme, which is specified by the number of measuring points and their corresponding values of the control variable. The number of measuring points is limited since direct measurement of SWCC is often costly and time-consuming. Based on the limited number of measuring points, the estimated SWCC is unavoidably associated with uncertainties, which depends on measurement data obtained from the prescribed experimental scheme. Therefore, it is essential to plan the experimental scheme so as to reduce the uncertainty in the estimated SWCC. This study presented a Bayesian approach, called OBEDO, for probabilistic experimental design optimization of measuring SWCC based on the prior knowledge and information of testing apparatus. The uncertainty in estimated SWCC is quantified and the optimal experimental scheme with the maximum expected utility is determined by Subset Simulation optimization (SSO) in candidate experimental scheme space. The proposed approach is illustrated using an experimental design example given prior knowledge and the information of testing apparatus and is verified based on a set of real loess SWCC data, which were used to generate random experimental schemes to mimic the arbitrary arrangement of measuring points during SWCC testing in practice. Results show that the arbitrary arrangement of measuring points of SWCC testing is hardly superior to the optimal scheme obtained from OBEDO in terms of the expected utility. The proposed OBEDO approach provides a rational tool to optimize the arrangement of measuring points of SWCC test so as to obtain SWCC measurement data with relatively high expected utility for uncertainty reduction.

Optimization of the Number and Location of Boreholes for Gassy Soil Site Investigation Considering the Statistical Uncertainty

The research addresses the prevalence of gassy soil, containing methane (CH4), within the soil particles of southeast coastal areas of China, such as the Quaternary deposit in the Hangzhou Bay area. This soil exhibits spatial variability in the distribution of gas pressure, posing a potential threat of engineering disasters, including fire outbreaks and blasting, during the construction of underground projects. Consequently, it is crucial to assess the risk state of gas pressure, involving accurate identification and reduction of associated uncertainty, through site investigation. This is indispensable prior to the commencement of underground projects. However, during the site investigation stage, the random field parameters that quantify the spatial variability distribution of gas pressure (e.g., mean value, standard deviations, and scale of fluctuation) are unknown, introducing corresponding statistical uncertainty. Therefore, the most significant consideration for planning site investigation from an engineering perspective involves determining the risk state of gas pressure while considering the statistical uncertainty of these random field parameters. This consideration heavily relies on the engineering experience gained from current site investigation practices. To address this challenge, the study introduces a probabilistic site investigation optimization method designed for planning the site investigation scheme for gassy soils, including determining the number and locations of boreholes. The method is based on the expected state-identification probability, representing the probability of identifying the risk state of gas pressure, and takes into account the statistical uncertainty of random field parameters. The proposed method aims to determine an optimal investigation scheme before conducting the site investigation, leveraging prior knowledge. This optimal scheme is identified using Subset Simulation Optimization (SSO) in the space of candidate site investigations, maximizing the value of the expected state-identification probability at the minimal value point. Finally, the paper illustrates the proposed approach through a case study.