Impact of ocean data assimilation on the seasonal forecast of the 2014/15 marine heatwave in the Northeast Pacific Ocean

A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of Information Science(NUIST-CFS 1.0)to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine heatwave.The sea surface temperature(SST)nudging scheme assimilates SST only,while the deterministic ensemble Kalman filter(EnKF)scheme assimilates observations from the surface to the deep ocean.The latter notably improves the forecasting skill for subsurface temperature anomalies,especially at the depth of 100-300 m(the lower layer),outperforming the SST nudging scheme.It excels in predicting both horizontal and vertical heat transport in the lower layer,contributing to improved forecasts of the lower-layer warming during the Blob.These improvements stem from the assimilation of subsurface observational data,which are important in predicting the upper-ocean conditions.The results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.

探讨Chinese way术式在关节镜下治疗巨大肩袖损伤的应用价值

目的探讨Chinese way术式在关节镜下治疗巨大肩袖损伤的应用价值,为临床治疗提供参考。方法选取2022年3月至2024年3月于扬州市江都中医院接受治疗的70例巨大肩袖损伤患者的临床资料,进行回顾性分析。根据治疗方式不同分为双排锚钉缝线桥组和Chinese way组,每组35例。双排锚钉缝线桥组患者采用关节镜下双排锚钉缝线桥固定术治疗,Chinese way组患者采用关节镜下Chinese way术式治疗。比较两组患者肩关节活动度、并发症发生情况、Fugl-Meyer评定量表(FMA-UE)评分。结果术后3个月,两组患者前屈、后伸、内旋、外旋的肩关节活动度均增加,且Chinese way组均大于双排锚钉缝线桥组(均P<0.05)。Chinese way组患者并发症总发生率低于双排锚钉缝线桥组(均P<0.05)。术后3个月,两组患者运动功能、感觉功能、平衡功能、关节活动度、疼痛的各项FMA-UE评分均升高,且Chinese way组均高于双排锚钉缝线桥组(均P<0.05)。结论Chinese way术式在关节镜下治疗巨大肩袖损伤的效果较好,可有效提升患者肩关节活动度和上肢运动功能,且安全性较高,值得临床应用。

Short-term forecasting optimization algorithms for wind speed along Qinghai-Tibet railway based on different intelligent modeling theories

To protect trains against strong cross-wind along Qinghai-Tibet railway, a strong wind speed monitoring and warning system was developed. And to obtain high-precision wind speed short-term forecasting values for the system to make more accurate scheduling decision, two optimization algorithms were proposed. Using them to make calculative examples for actual wind speed time series from the 18th meteorological station, the results show that: the optimization algorithm based on wavelet analysis method and improved time series analysis method can attain high-precision multi-step forecasting values, the mean relative errors of one-step, three-step, five-step and ten-step forecasting are only 0.30%, 0.75%, 1.15% and 1.65%, respectively. The optimization algorithm based on wavelet analysis method and Kalman time series analysis method can obtain high-precision one-step forecasting values, the mean relative error of one-step forecasting is reduced by 61.67% to 0.115%. The two optimization algorithms both maintain the modeling simple character, and can attain prediction explicit equations after modeling calculation.

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.

Forecast correct model of overpressure attenuation during gas explosion in excavation roadway

Through analyzing experimental data of gas explosions in excavation roadwaysand the forecast models of the literature, Found that there is no direct proportional linearcorrelation between overpressure and the square root of the accumulated volume of gas,the square root of the propagation distance multiplicative inverse.Also, attenuation speedof the forecast model calculation is faster than that of experimental data.Based on theoriginal forecast models and experimental data, deduced the relation of factors by introducinga correlation coefficient with concrete volume and distance, which had been verifiedby the roadway experiment data.The results show that it is closer to the roadway experimentaldata and the overpressure amount increases first then decreases with thepropagation distance.

Research on the mining roadway displacement forecasting based on support vector machine theory

In view of the difficulty in supporting the surrounding rocks of roadway 3-411 ofFucun Coal Mine of Zaozhuang Mining Group, a deformation forecasting model was putforward based on particle swarm optimization.The kernel function and model parameterswere optimized using particle swarm optimization.It is shown that the forecast result isvery close to the real monitoring data.Furthermore, the PSO-SVM (Particle Swarm Optimization-Support Vector Machine) model is compared with the GM(1,1) model and L-M BPnetwork model.The results show that PSO-SVM method is better in the aspect of predictionaccuracy and the PSO-SVM roadway deformation pre-diction model is feasible for thelarge deformation prediction of coal mine roadway.

STUDY ON THE FORECAST METHOD FOR THE IMPACT OF CROSSWAY ON TIDAL CURRENT FIELD IN SEA AREA

This paper’s method simulates the tidal current field near piles to derive and predict the scale oftheir impacts on the tidal current field, and presents the empirically obtained damp coefficient needed inpedicting the current field. The substituted plans used in the prediction are studied using Qingdao.Crossway as an example.

A New Way to Predict Forecast Skill

Forecast skill (Anomaly Correlated Coefficient, ACC) is a quantity to show the forecast quality of the products of numerical weather forecasting models. Predicting forecast skill, which is the foundation of ensemble forecasting, means submitting products to predict their forecast quality before they are used. Checking the reason is to understand the predictability for the real cases. This kind of forecasting service has been put into operational use by statistical methods previously at the National Meteorological Center (NMC), USA (now called the National Center for Environmental Prediction (NCEP)) and European Center for Medium-range Weather Forecast (ECMWF). However, this kind of service is far from satisfactory because only a single variable is used with the statistical method. In this paper, a new way based on the Grey Control Theory with multiple predictors to predict forecast skill of forecast products of the T42L9 of the NMC, China Meteorological Administration (CMA) is introduced. The results show: (1) The correlation coefficients between 'forecasted' and real forecast skill range from 0.56 to 0.7 at different seasons during the two-year period. (2) The grey forecasting model GM(1,8) forecasts successfully the high peaks, the increasing or decreasing tendency, and the turning points of the change of forecast skill of cases from 5 January 1990 to 29 February 1992.

Non-crossing Quantile Regression Neural Network as a Calibration Tool for Ensemble Weather Forecasts

Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive performance.Nevertheless,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated forecasts.On this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing.The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive entries.The empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models.Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all competitors.Furthermore,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks.

Research and Analysis on Refined Precipitation Forecast Method of Highway during Flood Season in Gansu Province

In this paper, T639 global spectral model numerical forecast products issued by numerical prediction center of China Meteorological Administration and precipitation data at 676 regional stations of Gansu during flood season were used firstly for interpretation. Then, through the establishment of multiple linear stepwise regression equation, daily precipitation forecast with the time interval of 6 h during 0 -72 h in May, 2013 in Gansu Province was obtained. By precipitation forecast verification analysis, it was found that the precipitation forecast in Hexi region and central Gansu Province had best effect; forecast in some places of east Hedong region had relatively good effect; part of the city or state forecast effect in the plateau slope zone was relatively lower. The refined precipitation forecast had certain reference and use value in the post-production Gansu Province highway forecasting during flood season.

Medium-term forecast of daily passenger volume of high speed railway based on DLP-WNNMedium-term forecast of dailypassenger volume of high speedrailway based on DLP-WNN

Purpose – This paper aims to propose a medium-term forecast model for the daily passenger volume of HighSpeed Railway (HSR) systems to predict the daily the Origin-Destination (OD) daily volume formultiple consecutivedays (e.g. 120 days).Design/methodology/approach – By analyzing the characteristics of the historical data on daily passengervolume of HSR systems, the date and holiday labels were designed with determined value ranges.In accordance to the autoregressive characteristics of the daily passenger volume of HSR, the Double LayerParallel Wavelet Neural Network (DLP-WNN) model suitable for the medium-term (about 120 d) forecast of thedaily passenger volume of HSR was established. The DLP-WNN model obtains the daily forecast result byweighed summation of the daily output values of the two subnets. Subnet 1 reflects the overall trend of dailypassenger volumes in the recent period, and subnet 2 the daily fluctuation of the daily passenger volume toensure the accuracy of medium-term forecast.Findings – According to the example application, in which the DLP-WNN modelwas used for the medium-termforecast of the daily passenger volumes for 120 days for typical O-D pairs at 4 different distances, the averageabsolute percentage error is 7%-12%, obviously lower than the results measured by the Back Propagation (BP)neural network, the ELM (extreme learning machine), the ELMAN neural network, the GRNN (generalizedregression neural network) and the VMD-GA-BP. The DLP-WNN model was verified to be suitable for themedium-term forecast of the daily passenger volume of HSR.Originality/value – This study proposed a Double Layer Parallel structure forecast model for medium-termdaily passenger volume (about 120 days) of HSR systems by using the date and holiday labels and WaveletNeural Network. The predict results are important input data for supporting the line planning, scheduling andother decisions in operation and management in HSR systems.

Applicability of Galway River Flow Forecasting and Modeling System (GFFMS) for Lake Tana Basin, Ethiopia

Flow forecasting is used in activities requiring stream flow data such as irrigation development, water supply, and flood control and hydropower development. Real time flow forecasting with special interest to flooding is one of the most important applications of hydrology for decision making in water resources. In order to meet flood and flow forecasts using hydrological models may be used and subsequently be updated in accordance with residuals. Therefore in this study, different flood forecasting methods are evaluated for their potential of stream flow forecasting using Galway River Flow Forecasting and Modeling System (GFFMS) in Lake Tana basin, upper Blue Nile basin, Ethiopia. The areal rainfall and temperature data was used for the model input. Three forecast updating methods, i.e., autoregressive (AR), linear transfer function (LTF) and neuron network updating (NNU) methods were compared for stream flow forecasting, at one to six days lead time. The most sensitive parameters were fine-tuned first and modeled for a calibration period of 1994-2004 for three selected watersheds of the Tana basin. The results indicate that with the exception of the simple linear model, an acceptable result could be obtained using models embedded in the software. Artificial neural network model performed well for Gilgel Abay (NSE = 0.87) and Gumara (NSE = 0.9) watersheds but for Megech watershed, SMAR model (NSE = 0.78) gave a better forecast result. In capturing the peak flows LTF and NNU in forecast updating mode performed better for Gilgel Abay and Megech watersheds, respectively. The results of this study implied that GFFMS can be used as a useful tool to forecast peak stream flows for flood early warning in the upper Blue Nile basin.

A Deep Learning Approach for Forecasting Thunderstorm Gusts in the Beijing–Tianjin–Hebei Region

Thunderstorm gusts are a common form of severe convective weather in the warm season in North China,and it is of great importance to correctly forecast them.At present,the forecasting of thunderstorm gusts is mainly based on traditional subjective methods,which fails to achieve high-resolution and high-frequency gridded forecasts based on multiple observation sources.In this paper,we propose a deep learning method called Thunderstorm Gusts TransU-net(TGTransUnet)to forecast thunderstorm gusts in North China based on multi-source gridded product data from the Institute of Urban Meteorology(IUM)with a lead time of 1 to 6 h.To determine the specific range of thunderstorm gusts,we combine three meteorological variables:radar reflectivity factor,lightning location,and 1-h maximum instantaneous wind speed from automatic weather stations(AWSs),and obtain a reasonable ground truth of thunderstorm gusts.Then,we transform the forecasting problem into an image-to-image problem in deep learning under the TG-TransUnet architecture,which is based on convolutional neural networks and a transformer.The analysis and forecast data of the enriched multi-source gridded comprehensive forecasting system for the period 2021–23 are then used as training,validation,and testing datasets.Finally,the performance of TG-TransUnet is compared with other methods.The results show that TG-TransUnet has the best prediction results at 1–6 h.The IUM is currently using this model to support the forecasting of thunderstorm gusts in North China.

Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models

Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes.In addition to PB models,deep learning(DL)models have been widely used in SM predictions recently.However,few pure DL models have notably high success rates due to lacking physical information.Thus,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions.To this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM predictions.The performances of the proposed hybrid models were investigated and compared with two existing hybrid models.The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models.Moreover,the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions.It is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day predictions.These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.

Assessment of ECMWF’s Precipitation Forecasting Performance for China from 2017 to 2022

This study used the China Meteorological Administration(CMA)three-source fusion gridded precipitation analysis data as a reference to evaluate the precipitation forecast performance of the European Centre for Medium-Range Weather Forecasts(ECMWF)model for China from 2017 to 2022.The main conclusions are as follows.The precipitation forecast capability of the ECMWF model for China has gradually improved from 2017 to 2022.Various scores such as bias,equitable threat score(ETS),and Fractions Skill Score(FSS)showed improvements for different categories of precipitation.The bias of light rain forecasts overall adjusted towards smaller values,and the increase in forecast scores was greater in the warm season than in the cold season.The ETS for torrential rain more intense categories significantly increased,although there were large fluctuations in bias across different months.The model exhibited higher precipitation bias in most areas of North China,indicating overprediction,while it showed lower bias in South China,indicating underprediction.The ETSs indicate that the model performed better in forecasting precipitation in the northeastern part of China without the influence of climatic background conditions.Comparison of the differences between the first period and the second period of the forecast shows that the precipitation amplitude in the ECMWF forecast shifted from slight underestimation to overestimation compared to that of CMPAS05,reducing the likelihood of missing extreme precipitation events.The improvement in ETS is mainly due to the reduction in bias and false alarm rates and,more importantly,an increase in the hit rate.From 2017 to 2022,the area coverage error of model precipitation forecast relative to observations showed a decreasing trend at different scales,while the FSS showed an increasing trend,with the highest FSS observed in 2021.The ETS followed a parabolic trend with increasing neighborhood radius,with the better ETS neighborhood radius generally being larger for moderate rain and heavy rain compared with light rain and torrential rain events.

Study on the forecasting and maintenance system of special railway subsidence in mine area

The fully mechanized caving coal mining under the railway in mine area will result in difficulty maintenance of railway because of great distortion and subsidence speed of terrene and railway. If the subsidence forecasting is incorrect and maintenance measure is not suitable in the preceding and the process of mining, the normal operation of the railway in mine area will not be ensured and perhaps the safety accident will be resulted. The railway subsidence forecasting and maintenance system for fully mechanized caving coal face are studied and developed in this connection. Based on the accurate subsidence forecasting of the terrene and railway, the maintenance measure for track and switch turnout in railway is put forward in this system.

Railway Passenger Flow Forecasting by Integrating Passenger Flow Relationship and Spatiotemporal Similarity

Railway passenger flow forecasting can help to develop sensible railway schedules,make full use of railway resources,and meet the travel demand of passengers.The structure of passenger flow in railway networks and the spatiotemporal relationship of passenger flow among stations are two distinctive features of railway passenger flow.Most of the previous studies used only a single feature for prediction and lacked correlations,resulting in suboptimal performance.To address the above-mentioned problem,we proposed the railway passenger flow prediction model called Flow-Similarity Attention Graph Convolutional Network(F-SAGCN).First,we constructed the passenger flow relations graph(RG)based on the Origin-Destination(OD).Second,the Passenger Flow Fluctuation Similarity(PFFS)algorithm is used to measure the similarity of passenger flow between stations,which helps construct the spatiotemporal similarity graph(SG).Then,we determine the weights of the mutual influence of different stations at different times through an attention mechanism and extract spatiotemporal features through graph convolution on the RG and SG.Finally,we fused the spatiotemporal features and the original temporal features of stations for prediction.The comparison experiments on a railway bureau’s accurate railway passenger flow data show that the proposed F-SAGCN method improved the prediction accuracy and reduced the mean absolute percentage error(MAPE)of 46 stations to 7.93%.

Seasonal Characteristics of Forecasting Uncertainties in Surface PM_(2.5)Concentration Associated with Forecast Lead Time over the Beijing-Tianjin-Hebei Region

Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts.However,the particular impact of meteorological forecasting uncertainties on air quality forecasts specific to different seasons is still not well known.In this study,a series of forecasts with different forecast lead times for January,April,July,and October of 2018 are conducted over the Beijing-Tianjin-Hebei(BTH)region and the impacts of meteorological forecasting uncertainties on surface PM_(2.5)concentration forecasts with each lead time are investigated.With increased lead time,the forecasted PM_(2.5)concentrations significantly change and demonstrate obvious seasonal variations.In general,the forecasting uncertainties in monthly mean surface PM_(2.5)concentrations in the BTH region due to lead time are the largest(80%)in spring,followed by autumn(~50%),summer(~40%),and winter(20%).In winter,the forecasting uncertainties in total surface PM_(2.5)mass due to lead time are mainly due to the uncertainties in PBL heights and hence the PBL mixing of anthropogenic primary particles.In spring,the forecasting uncertainties are mainly from the impacts of lead time on lower-tropospheric northwesterly winds,thereby further enhancing the condensation production of anthropogenic secondary particles by the long-range transport of natural dust.In summer,the forecasting uncertainties result mainly from the decrease in dry and wet deposition rates,which are associated with the reduction of near-surface wind speed and precipitation rate.In autumn,the forecasting uncertainties arise mainly from the change in the transport of remote natural dust and anthropogenic particles,which is associated with changes in the large-scale circulation.

Promising Results Predict Role for Artificial Intelligence in Weather Forecasting

Artificial intelligence(AI)has already demonstrated its proficiency at difficult scientific tasks like predicting how proteins will fold and identifying new astronomical objects in masses of observational data[1].Now,recent results suggest that AI also excels at weather forecasting.For global predictions,GraphCast,an AI system developed by Google subsidiary DeepMind(London,UK),outperforms the state-of-the-art model from the European Centre for Medium-Range Weather Forecasts(ECMWF),providing more accurate projections of variables such as temperature and humidity 90%of the time[2,3].Other AI systems,including Pangu-Weather from the Chinese tech company Huawei(Shenzhen,China)[4],can also match or beat traditional global forecasting models.

Scientific Advances and Weather Services of the China Meteorological Administration’s National Forecasting Systems during the Beijing 2022 Winter Olympics

Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational techniques,and experience.This made providing meteorological services for this event particularly challenging.The China Meteorological Administration(CMA)Earth System Modeling and Prediction Centre,achieved breakthroughs in research on short-and medium-term deterministic and ensemble numerical predictions.Several key technologies crucial for precise winter weather services during the Winter Olympics were developed.A comprehensive framework,known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics,was established.Some of these advancements represent the highest level of capabilities currently available in China.The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality.This included achievements such as the“100-meter level,minute level”downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days.Around 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed,and many of these techniques have since been integrated into the CMA’s operational national forecasting systems.These accomplishments were facilitated by a dedicated weather forecasting and research initiative,in conjunction with the preexisting real-time operational forecasting systems of the CMA.This program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project(SMART2022),and continues to play an important role in their Regional Association(RA)II Research Development Project(Hangzhou RDP).Therefore,the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming highimpact weather forecasting activities.This article provides an overview and assessment of this program and the operational national forecasting systems.