Interannual Variation and Statistical Prediction of Summer Dry and Hot Days in South China from 1970 to 2018

The frequent occurrence of dry and hot(DH)days in South China in summer has a negative impact on social development and human health.This study explored the variation characteristics of DH days and the possible reasons for this knotty problem.The findings revealed a notable increase in the number of DH days across most stations,indicating a significant upward trend.Additionally,DH events were observed to occur frequently.The number of DH days increased during 1970-1990,decreased from 1991 to 1997,and stayed stable after 1997.The key climate factors affecting the interannual variability of the number of DH days were the Indian Ocean Basin warming(IOBW)in spring and the East Asian Summer Monsoon(EASM).Compared with the negative phase of IOBW,in the positive phase of IOBW,500 hPa and 850 hPa geopotential height enhanced,the West Pacific subtropical high strengthened and extended abnormally to the west,more solar radiation reached the surface,surface outgoing longwave radiation increased,and there was an anomalous anticyclone in eastern South China.The atmospheric circulation characteristics of the positive and negative phases of ESAM were opposite to those of IOBW,and the abnormal circulation of the positive(negative)phases of ESAM was unfavorable(favorable)for the increase in the number of DH days.A long-term prediction model for the number of summer DH days was established using multiple linear regression,incorporating the key climate factors.The correlation coefficient between the observed and predicted number of DH days was 0.65,and the root-mean-square error was 2.8.In addition,independent forecasts for 2019 showed a deviation of just 1 day.The results of the independent recovery test confirmed the stability of the model,providing evidence that climatic factors did have an impact on DH days in South China.

The Statistical Prediction of the Vitrinite Reflectance and Study of the Ancient Geothermal Field in Songliao Basin,China

The resource of the gas from coal and coal measures deep in Songliao Basin has been drawing more and more attention to . It is necessary to find out the evolution regularity of the geothermal field of the basin in addition to a series of geological studies in order to predict its resources because the ancient geothermal field of the basin is one of the main factors controlling the generation , evolution and disappearance of oil and gas . In the recent twenty years , it is generally believed that vitrinite reflectance is the best quantitative marker for the ancient geothermal field . In the present paper , a systematic study of the vitrinite reflectance value of Songliao Basin and its influence factors is made by multiple statistical analysis so as to reconstruct the evolutional process of the Moho and the corresponding geothermal field . Then , an overall prediction is made of the vitrinite reflectance and the distribution of J3-K1 fault basin group at the bottom of Songliao Basin , which provides the evidence for the further prediction of the gas potentiality from coal and coal measures deep in the basin .

A STATISTICAL PREDICTION MODEL FOR SPRING RAINFALL OVER NORTHERN CHINA BASED ON THE INTERANNUAL INCREMENT APPROACH

The spring (March-April-May) rainfall over northern China (SPRNC) is predicted by using the interannual increment approach. DY denotes the difference between the current year and previous years. The seasonal forecast model for the DY of SPRNC is constructed based on the data that are taken from the 1965-2002 period (38 years), in which six predictors are available no later than the current month of February. This is favorable so that the seasonal forecasts can be made one month ahead. Then, SPRNC and the percentage anomaly of SPRNC are obtained by the predicted DY of SPRNC. The model performs well in the prediction of the inter-annual variation of the DY of SPRNC during 1965-2002, with a correlation coefficient between the predicted and observed DY of SPRNC of 0.87. This accounts for 76% of the total variance, with a low value for the average root mean square error (RMSE) of 20%. Both the results of the hindcast for the period of 2003-2010 (eight years) and the cross-validation test for the period of 1965-2009 (45 years) illustrate the good prediction capability of the model, with a small mean relative error of 10%, an RMSE of 17% and a high rate of coherence of 87.5% for the hindcasts of the percentage anomaly of SPRNC.

Seasonal Prediction of June Rainfall over South China:Model Assessment and Statistical Downscaling

The performances of various dynamical models from the Asia-Pacific Economic Cooperation（APEC） Climate Center（APCC） multi-model ensemble（MME） in predicting station-scale rainfall in South China（SC） in June were evaluated.It was found that the MME mean of model hindcasts can skillfully predict the June rainfall anomaly averaged over the SC domain.This could be related to the MME＇s ability in capturing the observed linkages between SC rainfall and atmospheric large-scale circulation anomalies in the Indo-Pacific region.Further assessment of station-scale June rainfall prediction based on direct model output（DMO） over 97 stations in SC revealed that the MME mean outperforms each individual model.However,poor prediction abilities in some in-land and southeastern SC stations are apparent in the MME mean and in a number of models.In order to improve the performance at those stations with poor DMO prediction skill,a station-based statistical downscaling scheme was constructed and applied to the individual and MME mean hindcast runs.For several models,this scheme can outperform DMO at more than 30 stations,because it can tap into the abilities of the models in capturing the anomalous Indo-Paciric circulation to which SC rainfall is considerably sensitive.Therefore,enhanced rainfall prediction abilities in these models should make them more useful for disaster preparedness and mitigation purposes.

Statistical prediction of waterflooding performance by K-means clustering and empirical modeling

Statistical prediction is often required in reservoir simulation to quantify production uncertainty or assess potential risks.Most existing uncertainty quantification procedures aim to decompose the input random field to independent random variables,and may suffer from the curse of dimensionality if the correlation scale is small compared to the domain size.In this work,we develop and test a new approach,K-means clustering assisted empirical modeling,for efficiently estimating waterflooding performance for multiple geological realizations.This method performs single-phase flow simulations in a large number of realizations,and uses K-means clustering to select only a few representatives,on which the two-phase flow simulations are implemented.The empirical models are then adopted to describe the relation between the single-phase solutions and the two-phase solutions using these representatives.Finally,the two-phase solutions in all realizations can be predicted using the empirical models readily.The method is applied to both 2D and 3D synthetic models and is shown to perform well in the P10,P50 and P90 of production rates,as well as the probability distributions as illustrated by cumulative density functions.It is able to capture the ensemble statistics of the Monte Carlo simulation results with a large number of realizations,and the computational cost is significantly reduced.

Expert System for Statistical Prediction of Mineral Deposits on Middle and Large Scales

The expert system for statistical prediction of mineral deposits on middle and large scales takes the system of scientific exploration theories, criteria and methods proposed by Professor Zhao Pengda as the field expert knowledge. At present the developed system focuses on two aspects: synthetic exploration and quantitative exploration. Among the three basic theories for the prediction of deposits, it highlights the applications of seeking anomaly theory. This system is characteristic in the determination of geological background, the study of geological anomalies and the delineation of geological background, the study of geological anomalies and the delineation of mineralization anomalies. The system combines closely the knowledge base, method base and database .integrates the input and output information of multi - sources and mul-ti - variables , data , graphs and imagine processing system and inquiring system as a whole . So the system can meet in general all kinds of demands in statistical prediction of mineral deposits . Since the statistical prediction of mineral resources is a kind of systematic engineering pro ject , a further study should be carried out on the fields of theoretical exploration and ster eo - exploration on the basis of unceasingly perfecting the above-mentioned fields in order to establish a comprehensive intelligent system for scientific exploration , to provide new methods , new techniques and new ideas for fast prospecting appraisal of mineral resources .

Predicting Western Pacific Subtropical High Using a Combined Tropical Indian Ocean Sea Surface Temperature Forecast

Weather and climate in East China are closely related to the variability of the western Pacific subtropical high(WPSH), which is an important part of the Asian monsoon system. The WPSH prediction in spring and summer is a critical component of rainfall forecasting during the summer flood season in China. Although many attempts have been made to predict WPSH variability, its predictability remains limited in practice due to the complexity of the WPSH evolution. Many studies have indicated that the sea surface temperature(SST) over the tropical Indian Ocean has a significant effect on WPSH variability. In this paper, a statistical model is developed to forecast the monthly variation in the WPSH during the spring and summer seasons on the basis of its relationship with SST over the tropical Indian Ocean. The forecasted SST over the tropical Indian Ocean is the predictor in this model, which differs significantly from other WPSH prediction methods. A 26-year independent hindcast experiment from 1983 to 2008 is conducted and validated in which the WPSH prediction driven by the combined forecasted SST is compared with that driven by the persisted SST. Results indicate that the skill score of the WPSH prediction driven by the combined forecasted SST is substantial.

An optimal filter based MPC for systems with arbitrary disturbances

In this study, a linear model predictive control(MPC) approach with optimal filters is proposed for handling unmeasured disturbances with arbitrary statistics. Two types of optimal filters are introduced into the framework of MPC to relax the assumption of integrated white noise model in existing approaches. The introduced filters are globally optimal for linear systems with unmeasured disturbances that have unknown statistics. This enables the proposed MPC to better handle disturbances without access to disturbance statistics. As a result, the effort required for disturbance modeling can be alleviated. The proposed MPC can achieve offset-free control in the presence of asymptotically constant unmeasured disturbances. Simulation results demonstrate that the proposed approach can provide an improved disturbance ?rejection performance over conventional approaches when applied to the control of systems with unmeasured disturbances that have arbitrary statistics.

A Hybrid Dynamical-Statistical Approach for Predicting Winter Precipitation over Eastern China

Correlation analysis revealed that winter precipitation in six regions of eastern China is closely related not only to preceding climate signals but also to synchronous atmospheric general circulation fields. It is therefore necessary to use a method that combines both dynamical and statistical predictions of winter precipitation over eastern China （hereinafter called the hybrid approach）, in this connection, seasonal real-time prediction models for winter precipitation were established for the six regions. The models use both the preceding observations and synchronous numerical predictions through a multivariate linear regression analysis. To improve the prediction accuracy, the systematic error between the original regression model result and the corresponding observation was corrected. Cross-validation analysis and real-time prediction experiments indicate that the prediction models using the hybrid approach can reliably predict the trend, sign, and interannual variation of regionally averaged winter precipitation in the six regions of concern. Averaged over the six target regions, the anomaly correlation coefficient and the rate with the same sign of anomaly between the cross-validation analysis and observation during 1982-2008 are 0.69 and 78%, respectively. This indicates that the hybrid prediction approach adopted in this study is applicable in operational practice.

An examination of the correlation between earthquake, positions of solar system bodies and solid tide

After processing 204 data of historical earthquakes for M≥4.5 from 1900 to 1996 in the area centered at Beijing (39.9°N, 116.4° E; ±3°), two correlations have been suggested: One is between earthquake and the position distribution of major solar system bodies; the other is between the earthquake magnitude and the tidal force at the epicenter. A tentative is presented for future seism prediction. Because this work is at the test stage based on a statistic analysis, further test and verification are expected.

Detailed parametric design methodology for hydrodynamics of liquid-solid circulating fluidized bed using design of experiments

A design-of-experiments methodology is used to develop a statistical model for the prediction of the hydrodynamics of a liquid–solid circulating fluidized bed. To illustrate the multilevel factorial design approach, a step by step methodology is taken to study the effects of the interactions among the independent factors considered on the performance variables. A multilevel full factorial design with three levels of the two factors and five levels of the third factor has been studied. Various statistical models such as the linear, two-factor interaction, quadratic, and cubic models are tested. The model has been developed to predict responses, viz., average solids holdup and solids circulation rate. The validity of the developed regression model is verified using the analysis of variance. Furthermore, the model developed was compared with an experimental dataset to assess its adequacy and reliability. This detailed statistical design methodology for non-linear systems considered here provides a very important tool for design and optimization in a cost-effective approach