Prediction of three-dimensional ocean temperature in the South China Sea based on time series gridded data and a dynamic spatiotemporal graph neural network

Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related fields.Currently,one of the commonly used methods for ocean temperature prediction is based on data-driven,but research on this method is mostly limited to the sea surface,with few studies on the prediction of internal ocean temperature.Existing graph neural network-based methods usually use predefined graphs or learned static graphs,which cannot capture the dynamic associations among data.In this study,we propose a novel dynamic spatiotemporal graph neural network(DSTGN)to predict threedimensional ocean temperature(3D-OT),which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior knowledge.Temporal and spatial dependencies in the time series were then captured using temporal and graph convolutions.We also integrated dynamic graph learning,static graph learning,graph convolution,and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid data.In this study,we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis,with data covering the vertical variation of temperature from the sea surface to 1000 m below the sea surface.We compared five mainstream models that are commonly used for ocean temperature prediction,and the results showed that the method achieved the best prediction results at all prediction scales.

Decadal prediction skill for Eurasian surface air temperature in CMIP6 models

评估CMIP6年代际预测试验对季节平均SAT的预测技巧的结果表明:模式不能有效预测冬季和秋季SAT的年代际变率.IPSL-CM6A-LR和多模式集合平均对于春季SAT展现了预测技巧,其中对于变率的预测技巧好于振幅的结果.基于蒙古和我国华北地区的显著预测技巧,模式对于夏季SAT表现出最佳的预测水平.与外部强迫相比,模式对于SAT的预测技巧可能来自初始化.模式中的一个明显系统性误差值得注意,即模式中冬季SAT的变率可以持续到其他季节,而在观测中其他季节的SAT变化与冬季SAT相对独立.

Probabilistic Multimodel Ensemble Prediction of Decadal Variability of East Asian Surface Air Temperature Based on IPCC-AR5 Near-term Climate Simulations

Based on near-term climate simulations for IPCC-AR5 （The Fifth Assessment Report）, probabilistic multimodel ensemble prediction （PMME） of decadal variability of surface air temperature in East Asia （20°-50°N, 100°- 145°E） was conducted using the multivariate Gaussian ensemble kernel dressing （GED） methodology. The ensemble system exhibited high performance in hindcasting the deeadal （1981-2010） mean and trend of temperature anomalies with respect to 1961-90, with a RPS of 0.94 and 0.88 respectively. The interpretation of PMME for future decades （2006-35） over East Asia was made on the basis of the bivariate probability density of the mean and trend. The results showed that, under the RCP4.5 （Representative Concentration Pathway 4.5 W m-2） scenario, the annual mean temperature increases on average by about 1.1-1.2 K and the temperature trend reaches 0.6-0.7 K （30 yr）-1. The pattern for both quantities was found to be that the temperature increase will be less intense in the south. While the temperature increase in terms of the 30-yr mean was found to be virtually certain, the results for the 30-yr trend showed an almost 25% chance of a negative value. This indicated that, using a multimodel ensemble system, even if a longer-term warming exists for 2006-35 over East Asia, the trend for temperature may produce a negative value. Temperature was found to be more affected by seasonal variability, with the increase in temperature over East Asia more intense in autumn （mainly）, faster in summer to the west of 115°E, and faster still in autumn to the east of 115°E.

High-temperature performance prediction of iron ore fines and the ore-blending programming problem in sintering

The high-temperature performance of iron ore fmes is an important factor in optimizing ore blending in sintering. However, the application of linear regression analysis and the linear combination method in most other studies always leads to a large deviation from the desired results. In this study, the fuzzy membership functions of the assimilation ability temperature and the liquid fluidity were proposed based on the fuzzy mathematics theory to construct a model for predicting the high-temperature performance of mixed iron ore. Comparisons of the prediction model and experimental results were presented. The results illustrate that the prediction model is more accurate and effective than previously developed models. In addition, fuzzy constraints for the high-temperature performance of iron ore in this research make the results of ore blending more comparable. A solution for the quantitative calculation as well as the programming of fuzzy constraints is also introduced.

Ladle Furnace Temperature Prediction Model Based on Large-scale Data With Random Forest

In ladle furnace, the prediction of the liquid steel temperature is always a hot topic for the researchers. The most of the existing temperature prediction models use small sample set. Today, the precision of them can not satisfy practical production. Fortunately, the large sample set is accumulated from the practical production process. However, a large sample set makes it difficult to build a liquid steel temperature model. To deal with the issue, the random forest method is preferred in this paper, which is a powerful regression method with low complexity and can be designed very quickly. It is with the parallel ensemble structure,uses sample subsets,and employs a simple learning algorithm of sub-models. Then, the random forest method is applied to establish a temperature model by using the data sampled from the production process. The experiments show that the random forest temperature model is more precise than other temperature models.

Endpoint temperature prediction of molten steel in RH using improved case-based reasoning

An improved case-based reasoning （CBR） method was proposed to predict the endpoint temperature of molten steel in Ruhrstahl Heraeus （RH） process. Firstly, production data were analyzed by multiple linear regressions and a pairwise comparison matrix in analytic hierarchy process （AHP） was determined by this linear regression＇s coefficient. The weights of various influencing factors were obtained by AHP. Secondly, the dividable principles of case base including ＂0-1＂ and ＂breakpoint＂ were proposed, and the case base was divided into several homogeneous parts. Finally, the improved CBR was compared with ordinary CBR, which is based on the even weight and the single base. The results show that the improved CBR has a higher hit rate for predicting the endpoint temperature of molten steel in RH.

Pressure Prediction for High-Temperature and High-Pressure Formation and Its Application to Drilling in the Northern South China Sea

There are plentiful potential hydrocarbon resources in the Yinggehai and Qiongdongnan basins in the northern South China Sea. However, the special petrol-geological condition with high formation temperature and pressure greatly blocked hydrocarbon exploration. The conventional means of drills, including methods in the prediction and monitoring of underground strata pressure, can no longer meet the requirements in this area. The China National Offshore Oil Corporation has allocated one well with a designed depth of 3200 m and pressure coefficient of 2.3 in the Yinggehai Basin (called test well in the paper) in order to find gas reservoirs in middle-deep section in the Miocene Huangliu and Meishan formations at the depth below 3000 m. Therefore, combined with the '863' national high-tech project, the authors analyzed the distribution of overpressure in the Yinggehai and Qiongdongnan basins, and set up a series of key technologies and methods to predict and monitor formation pressure, and then apply the results to pressure prediction of the test well. Because of the exact pressure prediction before and during drilling, associated procedure design of casing and their allocation in test well has been ensured to be more rational. This well is successfully drilled to the depth of 3485 m (nearly 300 m deeper than the designed depth) under the formation pressure about 2.3 SG (EMW), which indicate that a new step in the technology of drilling in higher temperature and pressure has been reached in the China National Offshore Oil Corporation.

Prediction of oxygen concentration and temperature distribution in loose coal based on BP neural network

An effective method for preventing spontaneous combustion of coal stockpiles on the ground is to control the air-flow in loose coal. In order to determine and predict accurately oxygen concentrations and temperatures within coal stockpiles, it is vital to obtain information of self-heating conditions and tendencies of spontaneous coal combustion. For laboratory conditions, we designed our own experimental equipment composed of a control-heating system, a coal column and an oxygen concentration and temperature monitoring system, for simulation of spontaneous combustion of block coal （13-25 mm） covered with fine coal （0-3 mm）. A BP artificial neural network （ANN） with 150 training samples was gradually established over the course of our experiment. Heating time, relative position of measuring points, the ratio of fine coal thickness, artificial density, voidage and activation energy were selected as input variables and oxygen concentration and temperature of coal column as output variables. Then our trained network was applied to predict the trend on the untried experimental data. The results show that the oxygen concentration in the coal column could be reduced below the minimum still able to induce spontaneous combustion of coal - 6% by covering the coal pile with fine coal, which would meet the requirement to prevent spontaneous combustion of coal stockpiles. Based on the prediction of this ANN, the average errors of oxygen concentration and temperature were respectively 0.5% and 7 ℃, which meet actual tolerances. The implementation of the method would provide a practical guide in understanding the course of self-heating and spontaneous combustion of coal stockpiles.

Analytical prediction and field validation of transient temperature field in asphalt pavements

This work presented the development and validation of an analytical method to predict the transient temperature field in the asphalt pavement.The governing equation for heat transfer was based on heat conduction radiation and convection.An innovative time-dependent function was proposed to predict the pavement surface temperature with solar radiation and air temperature using dimensional analysis in order to simplify the complex heat exchange on the pavement surface.The parameters for the time-dependent pavement surface temperature function were obtained through the regression analysis of field measurement data.Assuming that the initial pavement temperature distribution was linear and the influence of the base course materials on the temperature of the upper asphalt layers was negligible,a close-form analytical solution of the temperature in asphalt layers was derived using Green's function.Finally,two numerical examples were presented to validate the model solutions with field temperature measurements.Analysis results show that the solution accuracy is in agreement with field data and the relative errors at a shallower depth are greater than those at a deeper one.Although the model is not sensitive to dramatic changes in climatic factors near the pavement surface,it is applicable for predicting pavement temperature field in cloudless days.

IMPROVEMENT OF REGIONAL PREDICTION OF SEA FOG ON GUANGDONG COASTLAND USING THE FACTOR OF TEMPERATURE DIFFERENCE IN THE NEAR-SURFACE LAYER

The relationship between the factor of temperature difference of the near-surface layer(T_(1000 hPa)-T_(2m))and sea fog is analyzed using the NCEP reanalysis with a horizontal resolution of l°xl°(2000 to 2011) and the station observations(2010 to 2011).The element is treated as the prediction variable factor in the GRAPES model and used to improve the regional prediction of sea fog on Guangdong coastland.(1) The relationship between this factor and the occurrence of sea fog is explicit:When the sea fog happens,the value of this factor is always large in some specific periods,and the negative value of this factor decreases significantly or turns positive,suggesting the enhancement of warm and moist advection of air flow near the surface,which favors the development of sea fog.(2) The transportation of warm and moist advection over Guangdong coastland is featured by some stages and the jumping among these states.It also gets stronger over time.Meanwhile,the northward propagation of warm and moist advection is quite consistent with the northward advancing of sea fog from south to north along the coastland of China.(3) The GRAPES model can well simulate and realize the factor of near-surface temperature difference.Besides,the accuracy of regional prediction of marine fog,the relevant threat score and Heidke skill score are all improved when the factor is involved.

Interval finite difference method for steady-state temperature field prediction with interval parameters

A new numerical technique named interval finite difference method is proposed for the steady-state temperature field prediction with uncertainties in both physical parameters and boundary conditions. Interval variables are used to quantitatively describe the uncertain parameters with limited information. Based on different Taylor and Neumann series, two kinds of parameter perturbation methods are presented to approximately yield the ranges of the uncertain temperature field. By comparing the results with traditional Monte Carlo simulation, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed method for solving steady-state heat conduction problem with uncertain-but-bounded parameters.

Prediction of the Interface Temperature Rise in Tribochemical Polishing of CVD Diamond

Tribochemcial polishing is one of the most efficient methods for polishing CVD （Chemical Vapor Deposition） diamond film due to the use of catalytic metal. However the difficulty to control the interface temperature during polishing process often results in low material removal because of the unstable contact process. So this research investigates the contact process in the tribo- chemical polishing of CVD diamond film and proposes a dynamic contact model for predicting the actual contact area, the actual contact pressure, and the interface tem- perature in the polishing process. This model has been verified by characterizing surface metrology of the CVD diamond with Talysurf CLI2000 3D Surface Topography and measuring the polishing temperature. The theoretical and experimental results shows that the height distribution of asperities on diamond film surface in the polishing process is well evaluated by combining the height distribution of original and polished asperities. The modeled surface asperity height distribution of diamond film agrees with the actual surface metrology in polishing process. The actual contact pressure is very large due to the small actual contact area. The predicted interface temperature can reach the catalytic reaction temperature between diamond and polishing plate when the lowest rotation speed and load are 10 000 r/min and 50 N, respectively, and diamond material is significantly removed. The model may provide effective process theory for tribochemcial polishing.

Monthly Mean Temperature Prediction Based on a Multi－level Mapping Model of Neural Network BP Type

In terms of 34-year monthly mean temperature series in 1946-1979,the multi-level maPPing model of neural netWork BP type was applied to calculate the system's fractual dimension Do=2'8,leading tO a three-level model of this type with ixj=3x2,k=l,and the 1980 monthly mean temperture predichon on a long-t6rm basis were prepared by steadily modifying the weighting coefficient,making for the correlation coefficient of 97% with the measurements.Furthermore,the weighhng parameter was modified for each month of 1980 by means of observations,therefore constrcuhng monthly mean temperature forecasts from January to December of the year,reaching the correlation of 99.9% with the measurements.Likewise,the resulting 1981 monthly predictions on a long-range basis with 1946-1980 corresponding records yielded the correlahon of 98% and the month-tO month forecasts of 99.4%.

Analysis on Explanation Effect of the European Numerical Prediction on Temperature

[Objective] The research aimed to analyze explanation effect of the European numerical prediction on temperature. [Method] Based on CMSVM regression method, by using 850 hPa grid point data of the European numerical prediction from 2003 to 2009 and actual data of the maximum and minimum temperatures at 8 automatic stations in Qingyang City, prediction model of the temperature was established, and running effect of the business from 2008 to 2010 was tested and evaluated. [Result] The method had very good guidance role in real-time business running of the temperature prediction. Test and evaluation found that as forecast time prolonged, prediction accuracies of the maximum and minimum temperatures declined. When temperature anomaly was higher (actual temperature was higher than historical mean), prediction accuracy increased. Influence of the European numerical prediction was bigger. [Conclusion] Compared with other methods, operation of the prediction method was convenient, modeling was automatic, running time was short, system was stable, and prediction accuracy was high. It was suitable for implementing of the explanation work for numerical prediction product at meteorological station.

Tea Plantation Frost Damage Early Warning Using a Two-Fold Method for Temperature Prediction

As the source and main producing area of tea in the world, China has formed unique tea culture, and achievedremarkable economic benefits. However, frequent meteorological disasters, particularly low temperature frostdamage in late spring has seriously threatened the growth status of tea trees and caused quality and yield reduction of tea industry. Thus, timely and accurate early warning of frost damage occurrence in specific tea garden isvery important for tea plantation management and economic values. Aiming at the problems existing in currentmeteorological disaster forecasting methods, such as difficulty in obtaining massive meteorological data, largeamount of calculation for predicted models and incomplete information on frost damage occurrence, this paperproposed a two-fold algorithm for short-term and real-time prediction of temperature using field environmentaldata, and temperature trend results from a nearest local weather station for accurate frost damage occurrence leveldetermination, so as to achieve a specific tea garden frost damage occurrence prediction in a microclimate. Timeseries meteorological data collected from a small weather station was used for testing and parameterization of atwo-fold method, and another dataset acquired from Tea Experimental Base of Zhejiang University was furtherused to validate the capability of a two-fold model for frost damage forecasting. Results showed that comparedwith the results of autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR),the proposed two-fold method using a second order Furrier fitting model and a K-Nearest Neighbor model(K = 3) with three days historical temperature data exhibited excellent accuracy for frost damage occurrence prediction on consideration of both model accuracy and computation (98.46% forecasted duration of frost damage,and 95.38% for forecasted temperature at the onset time). For field test in a tea garden, the proposed methodaccurately predicted three times frost damage occurrences, including onset time, duration and occurrence level.These results suggested the newly-proposed two-fold method was suitable for tea plantation frost damage occurrence forecasting.

Temperature prediction model for a high-speed motorized spindle based on back-propagation neural network optimized by adaptive particle swarm optimization

To predict the temperature of a motorized spindle more accurately,a novel temperature prediction model based on the back-propagation neural network optimized by adaptive particle swarm optimization(APSO-BPNN)is proposed.First,on the basis of the PSO-BPNN algorithm,the adaptive inertia weight is introduced to make the weight change with the fitness of the particle,the adaptive learning factor is used to obtain different search abilities in the early and later stages of the algorithm,the mutation operator is incorporated to increase the diversity of the population and avoid premature convergence,and the APSO-BPNN model is constructed.Then,the temperature of different measurement points of the motorized spindle is forecasted by the BPNN,PSO-BPNN,and APSO-BPNN models.The experimental results demonstrate that the APSO-BPNN model has a significant advantage over the other two methods regarding prediction precision and robustness.The presented algorithm can provide a theoretical basis for intelligently controlling temperature and developing an early warning system for high-speed motorized spindles and machine tools.

Prediction of bridge temperature field and its effect on behavior of bridge deflection based on ANN method

In recent years, the bridge safety monitoring has been paid more attention in engineering field. How- ever, the financial and material resources as well as human resources were costly for the traditional monitoring means. Besides, the traditional means of monitoring were low in accuracy. From an engineering example, based on neural network method and historical data of the bridge monitoring to construct the BP neural network model with dual hidden layer strueture, the bridge temperature field and its effect on the behavior of bridge deflection are forecasted. The fact indicates that the predicted biggest error is 3.06% of the bridge temperature field and the bridge deflection behavior under temperature field affected is 2. 17% by the method of the BP neural net-work, which fully meet the precision requirements of the construction with practical value.

Seasonal prediction skills of FIO-ESM for North Pacific sea surface temperature and precipitation

The seasonal prediction of sea surface temperature(SST) and precipitation in the North Pacific based on the hindcast results of The First Institute of Oceanography Earth System Model(FIO-ESM) is assessed in this study.The Ensemble Adjusted Kalman Filter assimilation scheme is used to generate initial conditions, which are shown to be reliable by comparison with the observations. Based on this comparison, we analyze the FIO-ESM 6-month hindcast results starting from each month of 1993–2013. The model exhibits high SST prediction skills over most of the North Pacific for two seasons in advance. Furthermore, it remains skillful at long lead times for midlatitudes. The reliable prediction of SST can transfer fairly well to precipitation prediction via air-sea interactions.The average skill of the North Pacific variability(NPV) index from 1 to 6 months lead is as high as 0.72(0.55) when El Ni?o-Southern Oscillation and NPV are in phase(out of phase) at initial conditions. The prediction skill of the NPV index of FIO-ESM is improved by 11.6%(23.6%) over the Climate Forecast System, Version 2. For seasonal dependence, the skill of FIO-ESM is higher than the skill of persistence prediction in the later period of prediction.

Fuzzy Modeling of Prediction M_s Temperature for Martensitic Stainless Steel

A method of fuzzy modeling based on fuzzy clustering and Kalman filtering was proposed for predicting M s temperature from chemical composition for martensitic stainless steel. The membership degree of each sample was calculated by the fuzzy clustering algorithm. Kalman filtering was used to identify the consequent parameters. Only Grade 95 steel are available for training and validation, and the fuzzy model is valid for the following element concentration ranges (wt%): 0.01<C<0.7; 0<Si<1.0; 0.10<Mn<1.25; 11.5<Cr< 17.5; 0<Ni<2.5; 0<Mo<1.0. Compared with that of several empirical models reported, the accuracy of the fuzzy model was almost 5 times higher than that of the best empirical model. Furthermore, the compositional dependences of Ms were successfully determined and compared with those of the empirical formulae. It was found that the specific element dependences were a function of the overall composition, something could not easily be found using conventional statistics.

Thermal stress and fracture temperature prediction for flexible pavement

Analytical solutions of thermal stresses in multilayered elastic system whose materials characteristics are dependent on temperature are derived by a transfer matrix and integral transformation method.The resulting formulation is used to calculate thermal stresses in the low temperature cracking problem of asphalt pavement.Numerical simulations and analyses are performed using different structural combinations and material characteristics of base course.And fracture temperatures are predicted for a given flexible pavement constructed with three types of asphalt mixtures based on the calculated results and experimental data.This approach serves as a better model for real pavement structure as it takes into account the relationships between the material characteristics and temperature in the pavement system.