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.

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.

Comparative study on the performance of ConvLSTM and ConvGRU in classification problems-taking early warning of short-duration heavy rainfall as an example

卷积长短期记忆单元ConvLSTM和卷积门控循环单元ConvGRU是两种广泛应用的深度学习单元,通过将循环机制与卷积运算相结合,常常用于时空序列的预测.为了明确上述两种模型的收敛速度和分类能力,需要使用相同的模型架构对相同的分类问题进行预测.本研究将北京短时强降水区级预警问题看作深度学习中的二分类问题,使用京津冀雷达网的组合反射率数据和北京区域内的自动气象站降雨数据进行深度学习模型的训练和评估.结果表明,ConvGRU的收敛速度比ConvLSTM快约25%.ConvLSTM和ConvGRU的预警性能随地区,时间,降雨强度的变化趋势相似,但大部分ConvLSTM的得分较高,少数情况下ConvGRU的得分较高.

Enhancing the Efficiency of Multi-Electrolyzer Clusters with Lye Mixer:Topology Design and Control Strategy

The rise in hydrogen production powered by renewable energy is driving the field toward the adoption of systems comprising multiple alkaline water electrolyzers.These setups present various operational modes:independent operation and multi-electrolyzer parallelization,each with distinct advantages and challenges.This study introduces an innovative configuration that incorporates a mutual lye mixer among electrolyzers,establishing a weakly coupled system that combines the advantages of two modes.This approach enables efficient heat utilization for faster hot-startup and maintains heat conservation post-lye interconnection,while preserving the option for independent operation after decoupling.A specialized thermal exchange model is developed for this topology,according to the dynamics of the lye mixer.The study further details startup procedures and proposes optimized control strategies tailored to this structural design.Waste heat from the caustic fully heats up the multiple electrolyzers connected to the lye mixing system,enabling a rapid hot start to enhance the system’s ability to track renewable energy.A control strategy is established to reduce heat loss and increase startup speed,and the optimal valve openings of the diverter valve and the manifold valve are determined.Simulation results indicate a considerable enhancement in operational efficiency,marked by an 18.28%improvement in startup speed and a 6.11%reduction in startup energy consumption inmulti-electrolyzer cluster systems,particularlywhen the systems are synchronized with photovoltaic energy sources.The findings represent a significant stride toward efficient and sustainable hydrogen production,offering a promising path for large-scale integration of renewable energy.

冬奥会复杂山地百米尺度10m风速预报的机器学习订正对比试验

本文以传统机器学习算法XGBoost和深度学习算法CU-Net为基础,针对北京快速更新无缝隙融合与集成预报系统(RISE系统)预报的北京冬奥会延庆及张家口赛区100米分辨率的冬季近地面10 m风速数据,进行每日逐小时起报的未来逐6小时间隔的冬奥高山站点及其周边地区风速预报偏差订正方法研究和对比分析。对于站点订正,首先将RISE系统预测的10 m风速插值到对应的自动气象站站点,然后根据风速等级表归类,针对每个分类单独构建XGBoost模型,每个区间模型合并后形成L-XGBoost,使用均方根误差和预报准确率作为评分标准,结果表明风速归类的L-XGBoost算法订正效果比不归类的原始XGBoost模型有一定提升,说明在传统机器学习中加入归类方法有助于改善复杂山地站点风速预报技巧。对于站点及其周边地区风速订正,本文在CUNet模型基础上,通过引入不同深度的CU-Net子网络,构建了新的算法模型CU-Net++,并考虑了预报日变化误差和复杂地形对10 m风速的影响,以自动气象站为中心构建空间小区域样本数据,对RISE系统风速预报偏差进行订正。试验结果表明,CU-Net和CU-Net++均可以充分挖掘时间和空间维度的风场变化规律,且CU-Net++模型风速订正结果优于CU-Net模型,有效降低了RISE产品的格点风速预报误差,也发现预报误差和复杂地形的引入对10 m风速偏差订正起到重要的正向作用。

A Deep Learning Method for Bias Correction of ECMWF 24–240 h Forecasts

Correcting the forecast bias of numerical weather prediction models is important for severe weather warnings.The refined grid forecast requires direct correction on gridded forecast products,as opposed to correcting forecast data only at individual weather stations.In this study,a deep learning method called CU-net is proposed to correct the gridded forecasts of four weather variables from the European Centre for Medium-Range Weather Forecast Integrated Forecasting System global model(ECMWF-IFS): 2-m temperature,2-m relative humidity,10-m wind speed,and 10-m wind direction,with a forecast lead time of 24 h to 240 h in North China.First,the forecast correction problem is transformed into an image-toimage translation problem in deep learning under the CU-net architecture,which is based on convolutional neural networks.Second,the ECMWF-IFS forecasts and ECMWF reanalysis data(ERA5) from 2005 to 2018 are used as training,validation,and testing datasets.The predictors and labels(ground truth) of the model are created using the ECMWF-IFS and ERA5,respectively.Finally,the correction performance of CU-net is compared with a conventional method,anomaly numerical correction with observations(ANO).Results show that forecasts from CU-net have lower root mean square error,bias,mean absolute error,and higher correlation coefficient than those from ANO for all forecast lead times from 24 h to 240 h.CU-net improves upon the ECMWF-IFS forecast for all four weather variables in terms of the above evaluation metrics,whereas ANO improves upon ECMWF-IFS performance only for 2-m temperature and relative humidity.For the correction of the 10-m wind direction forecast,which is often difficult to achieve,CU-net also improves the correction performance.

Human-Robot Interface for Unmanned Aerial Vehicle via a Leap Motion

The unmanned aircraft vehicles industry is in the ascendant while traditional interaction ways for an unmanned aerial vehicle(UAV)are not intuitive enough.It is difficult for a beginner to control a UAV,therefore natural interaction methods are preferred.This paper presents a novel interactive control method for a UAV through operator's gesture,and explores the natural interaction method for the UAV.The proposed system uses the leap motion controller as an input device acquiring the gesture position and orientation data.It is found that the proposed human-robot interface can track the movement of the operator with satisfactory accuracy.The biggest advantage of the proposed method is its capability to control the UAV by just one hand instead of a joystick.A series of experiments verified the feasibility of the proposed human-robot interface.The results demonstrate that non-professional operators can easily operate a remote UAV by just using this system.

Damage to Wistar rat livers after hypo-fractionated radiation and oxaliplatin

Objective The purpose of this study was to clarify whether hypo-fractionated radiation therapy combined with oxaliplatin can aggravate liver damage, in order to determine its safety for clinical application. Methods Eighty Wistar rats were randomly divided into four groups： the control group, the chemotherapy treatment group, the radiation treatment group, and the concurrent chemoradiotherapy group. The rats＇ liver tissues were then collected for histological evaluation at the first, second, fourth, sixth, and eight week after irradiation. The tissues were histologically evaluated using hematoxylin and eosin staining, and immunohis-tochemistry to analyze the expression of Bcl-2 and Bax. Results Histological examination revealed swollen hepatocellular cells in the experimental groups, with visible liver degeneration and necrosis. Alanine aminotransferase and aspartate aminotransferase levels were significantly different between the groups （F = 85.869 and 214.663; P 〈 0.001）. The intra-group expressions of Bcl-2 and Bax were also significantly different between each time point （F = 6.047 and 43.344; P 〈 0.05）. Bax expression was significantly different between each group （F = 8.122; P 〈 0.05）, although no inter-group differences were observed for Bol-2 expression （F = 0.808; P 〉 0.05）. Conclusion Chemoradiotherapy may aggravate liver injury, possible via overexpression of Bcl-2 and reduced expression of Bax. Therefore, this treatment should be used carefully in the clinic.

A multichannel human-swarm robot interaction system in augmented reality

Background A large number of robots have put forward the new requirements for human robot interaction.One of the problems in human-swarm robot interaction is how to naturally achieve an efficient and accurate interaction between humans and swarm robot systems.To address this,this paper proposes a new type of human-swarm natural interaction system.Methods Through the cooperation between three-dimensional(3D)gesture interaction channel and natural language instruction channel,a natural and efficient interaction between a human and swarm robots is achieved.Results First,A 3D lasso technology realizes a batch-picking interaction of swarm robots through oriented bounding boxes.Second,control instruction labels for swarm-oriented robots are defined.The instruction label is integrated with the 3D gesture and natural language through instruction label filling.Finally,the understanding of natural language instructions is realized through a text classifier based on the maximum entropy model.A head-mounted augmented reality display device is used as a visual feedback channel.Conclusions The experiments on selecting robots verify the feasibility and availability of the system.

我国基础教育数字资源及服务:现状、问题与对策

本研究对我国基础教育阶段数字教育资源及服务进行系统研究。首先,通过问卷调查和深度访谈对全国31个省(自治区、直辖市)中小学数字教育资源建设与应用现状,从教育管理者、教师、学生三个关键涉众的视角,运用社会调查法进行深入调查,从数字教育资源使用基本情况、数字教育资源使用过程等维度揭示了我国基础教育数字资源现状。其次,结合内容分析法对32个政府类教学资源平台和39个企业类教学资源平台进行数据挖掘和深入分析,从各类平台的建设管理、服务、资源共建共享、服务效果等方面着手,深入了解中小学数字教育资源服务现状。以数字教育资源供给一般过程为框架,深度分析和挖掘我国基础教育数字资源供给与需求各阶段存在的问题。最后,提出了顶层规划、统筹协调、构建多元化数字资源供给机制、开发一体化数字资源供给平台等对策建议。

我国数字教育平台资源配置与服务:问题与对策

在线教育不断发展,对数字教育平台的资源质量与服务提出了更高要求,但数字教育资源现状远远不能满足日益增长的用户需求。从资源配置与服务视角对数字教育平台现状进行调研与分析,旨在发现数字教育平台中存在的问题并提出优化资源配置与服务的对策与建议。首先通过文献分析探索数字教育资源配置与服务的关系与内涵,从中提炼出数字教育资源配置与服务的关键要素或内容作为调研维度;其次采用德尔菲法选取16个热门数字教育平台作为调研对象,运用内容分析法对平台现状进行定性与定量相结合的调研、分析与问题揭示;最后提出科学规划数字资源配置过程、加强资源服务与质量跟踪、完善和优化平台服务流程等优化对策。

Elevation Influence on Rainfall and a Parameterization Algorithm in the Beijing Area

Using a high-density automatic weather stations(AWS)dataset of hourly rainfall observations,the present study investigates the relationship between rainfall and elevation in the Beijing area,and further proposes a rainfall amount dependent parameterized algorithm considering the elevation effect on rainfall on hourly timescale.The parameterization equation is defined as a segmented nonlinear model,which calculates the mountain rainfall as a function of valley rainfall amount.Results show that there exists an evident enhancement of rainfall amount by elevation effect in the Beijing area.In particular,larger rainfall amount is generally found in higher mountains,especially for slight rain and moderate rain.Furthermore,six representative station pairs located in valleys and on mountains respectively are selected to estimate the values of optimal parameters in the parameterization equation.The parameterization algorithm of elevation dependence can produce a reduction in the root-mean-square error and obtain a much closer mountain rainfall total to the observations compared with those using no elevation dependence.Furthermore,the spatial distribution of rainfall is more realistic and accurate in mountainous terrain when elevation dependence is considered.This study helps to understand the variability of rainfall with complex terrain in the Beijing area,and gives a possible way to parameterize rainfall–elevation relationship on hourly timescale.