Study on Refined Forecast Method of Daily Maximum Temperature in Wugang City from July to September

[Objective] The aim was to study the refined forecast method of daily highest temperature in Wugang City from July to September. IM[ethod] By dint of ECMWF mode product and T231 in 2009 and 2010 and daily maximum temperature in the station in corresponding period, multi-factors similar forecast method to select forecast sample, multivariate regression multi-mode integration MOS method, after dynamic corrected mode error and regression error, dynamic forecast equation was concluded to formulate the daily maximum temperature forecast in 24 -120 h in Wugang City from July to September. [ Result] Through selection, error correction, the daily maximum temperature equation in Wugang City from July to September was concluded. Through multiple random sampling, F test was made to pass test with significant test of 0.1. [ Conclusionl The method integrated domestic and foreign forecast mode, made full use of useful information of many modes, absorbed each others advantages, con- sidered local regional environment, lessen mode and regression error, and improved forecast accuracy.

The Application of a Grey Markov Model to Forecasting Annual Maximum Water Levels at Hydrological Stations

Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Markov theory into a higher precision model.The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values,and thus gives forecast results involving two aspects of information.The procedure for forecasting annul maximum water levels with the GMM contains five main steps:1) establish the GM(1,1) model based on the data series;2) estimate the trend values;3) establish a Markov Model based on relative error series;4) modify the relative errors caused in step 2,and then obtain the relative errors of the second order estimation;5) compare the results with measured data and estimate the accuracy.The historical water level records(from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin,China are utilized to calibrate and verify the proposed model according to the above steps.Every 25 years' data are regarded as a hydro-sequence.Eight groups of simulated results show reasonable agreement between the predicted values and the measured data.The GMM is also applied to the 10 other hydrological stations in the same estuary.The forecast results for all of the hydrological stations are good or acceptable.The feasibility and effectiveness of this new forecasting model have been proved in this paper.

An ANN-Based Short-Term Temperature Forecast Model for Mass Concrete Cooling Control

Concrete temperature control during dam construction(e.g.,concrete placement and curing)is important for cracking prevention.In this study,a short-term temperature forecast model for mass concrete cooling control is developed using artificial neural networks(ANN).The development workflow for the forecast model consists of data integration,data preprocessing,model construction,and model application.More than 80000 monitoring samples are collected by the developed intelligent cooling control system in the Baihetan Arch Dam,which is the largest hydropower project in the world under construction.Machine learning algorithms,including ANN,support vector machines,long short-term memory networks,and decision tree structures,are compared in temperature prediction,and the ANN is determined to be the best for the forecast model.Furthermore,an ANN framework with two hidden layers is determined to forecast concrete temperature at intervals of one day.The root mean square error of the forecast precision is 0.15∘C on average.The application on concrete blocks verifies that the developed ANN-based forecast model can be used for intelligent cooling control during mass concrete construction.

ConvLSTM Based Temperature Forecast Modification Model for North China

The correction of model forecast is an important step in evaluating weather forecast results.In recent years,post-processing models based on deep learning have become prominent.In this paper,a deep learning model named EDConvLSTM based on encoder-decoder structure and ConvLSTM is developed,which appears to be able to effectively correct numerical weather forecasts.Compared with traditional post-processing methods and convolutional neural networks,ED-ConvLSTM has strong collaborative extraction ability to effectively extract the temporal and spatial features of numerical weather forecasts and fit the complex nonlinear relationship between forecast field and observation field.In this paper,the post-processing method of ED-ConvLSTM for 2 m temperature prediction is tested using The International Grand Global Ensemble dataset and ERA5-Land data from the European Centre for Medium-Range Weather Forecasts(ECMWF).Root mean square error and temperature prediction accuracy are used as evaluation indexes to compare ED-ConvLSTM with the method of model output statistics,convolutional neural network postprocessing methods,and the original prediction by the ECMWF.The results show that the correction effect of EDConvLSTM is better than that of the other two postprocessing methods in terms of the two indexes,especially in the long forecast time.

Characters of Temperature Variation and Minimal Temperature Forecast inside of Solar Greenhouse in Winter in Shouguang City of Shandong Province

[Objective] The aim was to improve meteorological service of protected agriculture and to reduce effects of meteorological disasters. [Method] Characters of temperature variation in solar greenhouse and minimal temperature forecast models in winter were analyzed based on meteorological data inside and outside of solar greenhouse in winter during 2008-2011, as per correlation and stepwise regression method. [Result] Temperature was of significant changes in solar greenhouse in sunny and cloudy days and the change was higher in sunny days. In overcast days, temperature in solar greenhouse was lower and plants were affected seriously. In addition, the minimal temperature was of good correlation with outside temperature and humidity, temperature and soil temperature in greenhouse. [Conclusion] The minimal temperature forecast model of solar greenhouse is established and the average absolute error of the forecasted minimums in different types of weather was less than 1 ℃ and the average relative error was lower than 10%.

A Short-Range Quantitative Precipitation Forecast Algorithm Using Back-Propagation Neural Network Approach

A back-propagation neural network （BPNN） was used to establish relationships between the shortrange （0-3-h） rainfall and the predictors ranging from extrapolative forecasts of radar reflectivity, satelliteestimated cloud-top temperature, lightning strike rates, and Nested Grid Model （NGM） outputs. Quan- titative precipitation forecasts （QPF） and the probabilities of categorical precipitation were obtained. Results of the BPNN algorithm were compared to the results obtained from the multiple linear regression algorithm for an independent dataset from the 1999 warm season over the continental United States. A sample forecast was made over the southeastern United States. Results showed that the BPNN categorical rainfall forecasts agreed well with Stage Ⅲ observations in terms of the size and shape of the area of rainfall. The BPNN tended to over-forecast the spatial extent of heavier rainfall amounts, but the positioning of the areas with rainfall ≥25.4 mm was still generally accurate. It appeared that the BPNN and linear regression approaches produce forecasts of very similar quality, although in some respects BPNN slightly outperformed the regression.

Change Characteristics and Forecast Research of Road Surface Temperature on Huyu Expressway (Hubei Section)

In this paper,the monitoring data of road surface temperature,air temperature,wind speed,wind direction,relative humidity and precipitation from the automatic weather stations of Hurongxi,Hanyi,Wuhuang and Huanghuang on Huyu expressway from June 2013 to August 2014 were used to investigate the change characteristics of different sections' road surface temperatures in different seasons and sky conditions. The forecast models of the maximum and minimum road surface temperatures were established on different sections by statistical analysis methods,and the forecast results were verified. The results showed that the road surface temperature and air temperature of Hurongxi,Hanyi,Wuhuang and Huanghuang displayed obvious diurnal variation,but the difference between the road surface temperature and air temperature was larger. Compared with the other three sections,the maximum difference between the road surface temperature and air temperature on Hurongxi increased in winter and decreased in summer overall. The road surface temperature was close to air temperature on Hurongxi after sunset on sunny to cloudy and overcast in winter,while less than air temperature on Hanyi,Wuhuang and Huanghuang. The air temperature was less than road surface temperature on the four sections on rainy day and significant on Hurongxi. In summer,the air temperature was less than road surface temperature on the four sections under three sky conditions and the difference between them in afternoon was the biggest on sunny to cloudy and overcast. The road surface temperature was very close to each other among the four sections in January,while which was rising with the decrease of altitude in April,July and October. The forecast result of the road surface temperature was close to actual result on Hurongxi and Huanghuang,so which can be for reference. But there were some big errors between the forecast result and actual result in several timings on Hanyi and Wuhuang,so the forecast result should be corrected for actual business work.

Relationship between rock uniaxial compressive strength and digital core drilling parameters and its forecast method

The rock uniaxial compressive strength(UCS)is the basic parameter for support designs in underground engineering.In particular,the rock UCS should be obtained rapidly for underground engineering with complex geological conditions,such as soft rock,fracture areas,and high stress,to adjust the excavation and support plan and ensure construction safety.To solve the problem of obtaining real-time rock UCS at engineering sites,a rock UCS forecast idea is proposed using digital core drilling.The digital core drilling tests and uniaxial compression tests are performed based on the developed rock mass digital drilling system.The results indicate that the drilling parameters are highly responsive to the rock UCS.Based on the cutting and fracture characteristics of the rock digital core drilling,the mechanical analysis of rock cutting provides the digital core drilling strength,and a quantitative relationship model(CDP-UCS model)for the digital core drilling parameters and rock UCS is established.Thus,the digital core drilling-based rock UCS forecast method is proposed to provide a theoretical basis for continuous and quick testing of the surrounding rock UCS.

The 3-Hour-Interval Prediction of Ground-Level Temperature in South Korea Using Dynamic Linear Models

The 3-hour-interval prediction of ground-level temperature from +00 h out to +45 h in South Korea (38 stations) is performed using the DLM (dynamic linear model) in order to eliminate the systematic error of numerical model forecasts. Numerical model forecasts and observations are used as input values of the DLM. According to the comparison of the DLM forecasts to the KFM (Kalman filter model) forecasts with RMSE and bias, the DLM is useful to improve the accuracy of prediction.

Thermal Error Modeling Method with the Jamming of Temperature-Sensitive Points'Volatility on CNC Machine Tools

Aiming at the deficiency of the robustness of thermal error compensation models of CNC machine tools, the mechanism of improving the models＇ robustness is studied by regarding the Leaderway-V450 machining center as the object. Through the analysis of actual spindle air cutting experimental data on Leaderway-V450 machine, it is found that the temperature-sensitive points used for modeling is volatility, and this volatility directly leads to large changes on the collinear degree among modeling independent variables. Thus, the forecasting accuracy of multivariate regression model is severely affected, and the forecasting robustness becomes poor too. To overcome this effect, a modeling method of establishing thermal error models by using single temperature variable under the jamming of temperature-sensitive points＇ volatility is put forward. According to the actual data of thermal error measured in different seasons, it is proved that the single temperature variable model can reduce the loss of fore- casting accuracy resulted from the volatility of tempera- ture-sensitive points, especially for the prediction of cross quarter data, the improvement of forecasting accuracy is about 5 μm or more. The purpose that improving the robustness of the thermal error models is realized, which can provide a reference for selecting the modelingindependent variable in the application of thermal error compensation of CNC machine tools.

Application of Machine-Learning-Based Objective Correction Method in the Intelligent Grid Maximum and Minimum Temperature Predictions

Post-processing correction is an effective way to improve the model forecasting result. Especially, the machine learning methods have played increasingly important roles in recent years. Taking the meteorological observational data in a period of two years as the reference, the maximum and minimum temperature predictions of Shenyang station from the European Center for Medium-Range Weather Forecasts (ECMWF) and national intelligent grid forecasts are objectively corrected by using wavelet analysis, sliding training and other technologies. The evaluation results show that the sliding training time window of the maximum temperature is smaller than that of the minimum temperature, and their difference is the largest in August, with a difference of 2.6 days. The objective correction product of maximum temperature shows a good performance in spring, while that of minimum temperature performs well throughout the whole year, with an accuracy improvement of 97% to 186%. The correction effect in the central plains is better than in the regions with complex terrain. As for the national intelligent grid forecasts, the objective correction products have shown positive skills in predicting the maximum temperatures in spring (the skill-score reaches 0.59) and in predicting the minimum temperature at most times of the year (the skill-score reaches 0.68).

Urban expressway traffic state forecasting based on multimode maximum entropy model

The accurate and timely traffic state prediction has become increasingly important for the traffic participants,especially for the traffic managements. In this paper,the traffic state is described by Micro-LOS,and a direct prediction method is introduced. The development of the proposed method is based on Maximum Entropy (ME) models trained for multiple modes. In the Multimode Maximum Entropy (MME) framework,the different features like temporal and spatial features of traffic systems,regional traffic state are integrated simultaneously,and the different state behaviors based on 14 traffic modes defined by average speed according to the date-time division are also dealt with. The experiments based on the real data in Beijing expressway prove that the MME models outperforms the already existing model in both effectiveness and robustness.

Model Simulation of Artificial Heating of the Daytime High-Latitude F-Region Ionosphere by Powerful High-Frequency Radio Waves

The large-scale disturbance of the spatial structure of the daytime high-latitude F-region ionosphere, caused by powerful high-frequency radio waves, pumped into the ionosphere by a groundbased ionospheric heater, is studied with the help of the numerical simulation. The mathematical model of the high-latitude ionosphere, developed earlier in the Polar Geophysical Institute, is utilized. The mathematical model takes into account the drift of the ionospheric plasma, strong magnetization of the plasma at F-layer altitudes, geomagnetic field declination, and effect of powerful high-frequency radio waves. The distributions of the ionospheric parameters were calculated on condition that an ionospheric heater, situated at the point with geographic coordinates of the HF heating facility near Tromso, Scandinavia, has been operated, with the ionospheric heater being located on the day side of the Earth. The results of the numerical simulation indicate that artificial heating of the ionosphere by powerful high-frequency waves ought to influence noticeably on the spatial structure of the daytime high-latitude F-region ionosphere in the vicinity of the ionospheric heater.

Parametric sensitivity analysis of precipitation and temperature based on multi-uncertainty quantification methods in the Weather Research and Forecasting model

Sensitivity analysis(SA) has been widely used to screen out a small number of sensitive parameters for model outputs from all adjustable parameters in weather and climate models, helping to improve model predictions by tuning the parameters. However, most parametric SA studies have focused on a single SA method and a single model output evaluation function, which makes the screened sensitive parameters less comprehensive. In addition, qualitative SA methods are often used because simulations using complex weather and climate models are time-consuming. Unlike previous SA studies, this research has systematically evaluated the sensitivity of parameters that affect precipitation and temperature simulations in the Weather Research and Forecasting(WRF) model using both qualitative and quantitative global SA methods. In the SA studies, multiple model output evaluation functions were used to conduct various SA experiments for precipitation and temperature. The results showed that five parameters(P3, P5, P7, P10, and P16) had the greatest effect on precipitation simulation results and that two parameters(P7 and P10) had the greatest effect for temperature. Using quantitative SA, the two-way interactive effect between P7 and P10 was also found to be important, especially for precipitation. The microphysics scheme had more sensitive parameters for precipitation, and P10(the multiplier for saturated soil water content) was the most sensitive parameter for both precipitation and temperature. From the ensemble simulations, preliminary results indicated that the precipitation and temperature simulation accuracies could be improved by tuning the respective sensitive parameter values, especially for simulations of moderate and heavy rain.

IMPACT OF SUMMER WARMING ON DYNAMICS-STATISTICS-COMBINED METHOD TO PREDICT THE SUMMER TEMPERATURE IN CHINA

Based on NCEP/NCAR daily reanalysis data, climate trend rate and other methods are used to quantitatively analyze the change trend of China's summer observed temperature in 1983—2012. Moreover, a dynamics-statistics-combined seasonal forecast method with optimal multi-factor portfolio is applied to analyze the impact of this trend on summer temperature forecast. The results show that: in the three decades, the summer temperature shows a clear upward trend under the condition of global warming, especially over South China, East China, Northeast China and Xinjiang Region, and the trend rate of national average summer temperature was 0.27℃ per decade. However, it is found that the current business model forecast(Coupled Global Climate Model) of National Climate Centre is unable to forecast summer warming trends in China, so that the post-processing forecast effect of dynamics-statistics-combined method is relatively poor. In this study, observed temperatures are processed first by removing linear fitting trend, and then adding it after forecast to offset the deficiency of model forecast indirectly. After test, ACC average value in the latest decade was 0.44 through dynamics-statistics-combined independent sample return forecast. The temporal correlation(TCC) between forecast and observed temperature was significantly improved compared with direct forecast results in most regions, and effectively improved the skill of the dynamics-statistics-combined forecast method in seasonal temperature forecast.

CMA-GEPS极端温度预报指数及2022年夏季极端高温预报检验评估

极端预报指数(EFI)是利用集合预报获取极端天气信息的有效工具之一。为提升CMA全球集合预报系统(CMAGEPS)对极端天气的预报能力,针对CMA-GEPS历史预报数据少且再预报数据缺乏、难以合理统计模式气候分布的难题,研究利用小样本确定性预报数据形成EFI所需模式气候分布的方法。采用2020年6月15日—2022年7月22日CMA全球高分辨率(0.25°×0.25°)确定性业务预报数据,通过一种时间、空间样本扩展方法建立了与较低分辨率(0.5°×0.5°)的CMA-GEPS预报模式版本匹配的各预报时效(1—10 d)逐月模式气候分布。使用CMA-GEPS业务预报和ERA5再分析数据评估了CMA-GEPS 2 m气温EFI对2022年夏季(6—8月)中外4个代表性区域极端高温的预报能力。基于相对作用特征曲线的检验结果表明,CMA-GEPS EFI在1—10 d的短、中期预报时效上均具备区分极端高温的能力。以最大TS评分为准则,确定了用于发布极端高温预警信号的EFI临界阈值。EFI的预报能力随预报时效延长呈下降趋势,且在不同区域的表现存在差异:对中国长江中下游地区极端高温的预报能力在各时效上均优于华北地区;欧洲西部地区1—7 d时效上的EFI预报能力高于欧洲中部地区,而欧洲中部地区8—10 d时效上的EFI预报能力更好。上述结果与2 m气温的集合预报质量随预报时效与空间位置而变化有关。经济价值模型的评估结果表明,基于EFI预报信息的风险决策存在一定的经济价值和参考价值。个例分析结果进一步展现了CMA-GEPS EFI能够在中期预报时效上发出极端高温早期预警的能力。

基于Prophet-XGBoost组合模型的极端温度事件下负荷预测

气候变化对城市的影响日益加剧,频发的极端温度事件导致城市电力系统供需不平衡问题凸显,精确的需求侧电力负荷预测成为提升电力系统适应性从而支持城市功能稳定性的关键。本文开发了一种适用于极端温度事件下负荷预测的组合模型,结合时间序列模型Prophet和机器学习模型XGBoost,有效表征极端温度影响下的电力负荷波动趋势。实验结果表明,相比传统单一模型,组合模型显著提高了极端温度事件下的电力负荷预测精度,在增强城市电力系统对气候变化适应性方面具有较强的有效性,从而为电力调度等电力系统应急管理工作提供了更可靠的支持。

基于LSTM和先验知识的高速公路路面温度预报

为了精准预报高速公路路面温度,为车辆安全行驶提供气象保障,采用2019—2022年南京市绕城高速公路上9个交通气象站及ERA5-land再分析数据,通过构建时间序列特征工程、引入物理机制相关数据两类方法结合先验知识,运用长短期记忆神经网络模型建立研究区域内4个交通气象站未来3 h逐10 min路面温度多步预报模型并进行验证;在此基础上,将已建立的模型应用于其他交通气象站,探究模型的适用性。结果表明:结合先验知识后,模型预报性能明显提高,准确率在85%以上,且随着预报时效的延长,性能提升更为明显,准确率最高提升36%;模型能较为准确地预报路面极端低温发生的时间和极值,且在预报时效较短时对路面极端高温的预报也具有一定参考价值;利用已建立的模型对其他交通气象站的路面温度进行预报时,准确率在62%以上,在预报时效较短时效果较好,准确率在80%以上,且交通气象站所处的下垫面背景类型对模型的选择起关键作用。

设施温室物联网智能测控系统研究

针对温室环境调控中手动控制和阈值条件调控存在调控精度低、参数超调严重等问题,以设施番茄温室为例,研究提出一种基于环境预测的精准调控策略。首先,构建专用物联网“六域”架构,采用MSP430F5438A作为主控模块,设计感知、通信等功能模块。其次,构建SSA-LSTM预测模型实现对温室环境的精准预测,并根据模型预测结果确定环境调控策略,通过PSO-PID控制模型实现对温室风口电机的精准控制。实验结果表明,相较于传统LSTM模型,SSA-LSTM预测模型的MAE降低58.52%,MAPE降低61.68%,RMSE降低63.84%。同时,相较于传统PID模型,PSO-PID控制模型的超调量降低89.99%,调节时间降低59.85%。系统经过实地部署验证,在保持种植品种和农事管理操作一致的情形下,智能调控的温室产量相较于传统温室提升约8.5%,证明了系统的有效实用性。

一种基于分数技巧评分定义的降水预报跳跃指数及其应用

基于CMA-BJ系统提供的2021年8月9日2套降水预报结果(业务数值预报模式和同化试验结果)和2022年6月4日、2023年9月17日的业务数值预报模式结果,结合定性分析,利用4种客观评价指标(不确定度、均方根误差、不一致指数和基于分数技巧评分(FSS)定义的预报跳跃指数)对该系统降水预报不一致特征进行了定量评估。3次降水过程的分析结果显示:预报跳跃指数不仅可以识别出2021年8月9日和2022年6月4日业务数值预报模式结果中降水量预报明显减小的3个预报时次,而且对于降水过程预报相对稳定的个例(2021年8月9日同化试验和2023年9月17日业务预报结果),随着预报时次逐渐临近最新预报,该指数整体呈现波动上升或者数值较大、波动较小的特征,表明15个连续降水预报特征逐渐与最新预报趋于一致或者大体相似,与定性分析结果相对吻合。其他3种指数对于降水预报不一致问题的表征存在不足,不确定度和均方根误差显著受到预报降水量的影响,同时不确定度不能反映预报不一致的时间特征,不一致指数随预报时次逐时滚动变化较大,确定的预报不一致时次较多,与定性分析结果存在明显偏差。