Looking at the Statistical Texture Approach Applied to Weather Radar Rainfall Fields

Texture analysis methods have been used in a variety of applications, for instance in remote sensing. Though widely used in electrical engineering, its application in atmospheric sciences is still limited. This paper reviews some concepts of digital texture and statistical texture approach, applying them to a set of specific maps to analyze the correlation between texture measurements used in most papers. It is also proposed an improvement of the method by setting free a distance parameter and the use of a new texture measurement based on the Kullback-Leibler divergence. Eight statistical measurements were used: mean, contrast, standard deviation, cluster shade, cluster prominence, angular second moment, local homogeneity and Shannon entropy. The above statistical measurements were applied to simple maps and a set of rainfall fields measured with weather radar. The results indicate some high correlations, e.g. between the mean and the contrast or between the angular second moment, local homogeneity and the Shannon entropy, besides the potentiality of the method to discriminate maps.

Identifying Possible Climate Change Signals Using Meteorological Parameters in Short-Term Fire Weather Variability for Russian Boreal Forest in the Republic of Sakha (Yakutia)

The Boreal forest is a terrestrial ecosystem highly vulnerable to the impacts of short-term climate and weather variabilities. Detecting abrupt, rapid climate-induced changes in fire weather and related changes in fire seasonality can provide important insights to assessing impacts of climate change on forestry. This paper, taking the Sakha Republic of Russia as study area, aims to suggest an approach for detecting signals indicating climate-induced changes in fire weather to express recent fire weather variability by using short-term ranks of major meteorological parameters such as air temperature and atmospheric precipitation. Climate data from the “Global Summary of the Day Product” of NOAA (the United States National Oceanic and Atmospheric Administration) for 1996 to 2018 were used to investigate meteorological parameters that drive fire activity. The detection of the climate change signals is made through a 4-step analysis. First, we used descriptive statistics to grasp monthly, annual, seasonal and peak fire period characteristics of fire weather. Then we computed historical normals for WMO reference period, 1961-1990, and the most recent 30-year period for comparison with the current means. The variability of fire weather is analyzed using standard deviation, coefficient of variation, percentage departures from historical normals, percentage departures from the mean, and precipitation concentration index. Inconsistency and abrupt changes in the evolution of fire weather are assessed using homogeneity analysis whilst a Mann-Kendall test is used to detect significant trends in the time series. The results indicate a significant increase of temperature during spring and fall months, which extends the fire season and potentially contributes to increase of burned areas. We again detected a significant rainfall shortage in September which extended the fire season. Furthermore, this study suggests a new approach in statistical methods appropriate for the detection of climate change signals on fire weather variability using short-term climate ranks and evaluation of its impact on fire seasonality and activity.

Analysis of Short-term Heavy Precipitations in a Regional Heavy Rainstorm in Shannxi Province

[Objective] This study aimed to analyze the cause of the generation of short-term heavy precipitations in a regional heavy rainstorm in Shannxi Province. [Method] Taking a heavy rainstorm covering most parts of Shaanxi Province in late July 2010 as an example, data of five Doppler weather radars in Shaanxi Province were employed for a detailed analysis of the evolution of the heavy rainstorm pro- cess. [Result] Besides the good large-scale weather background conditions, the de- velopment and evolution of some mesoscale and small-scale weather systems direct- ly led to short-term heavy precipitations during the heavy rainstorm process, involv- ing the intrusion of moderate IS-scale weak cold air and presence of small-scale wind shear, convergence and adverse wind area. In addition, small-scale convection echoes were arranged in lines and formed a ＂train effect＂, which would also con- tribute to the generation of short-term heavy precipitation. [Conclusion] This study provided basic information for more clear and in-depth analysis of the formation mechanism of short-term heavy precipitations.

Deep Bimodal Fusion Approach for Apparent Personality Analysis

Personality distinguishes individuals’ patterns of feeling, thinking,and behaving. Predicting personality from small video series is an excitingresearch area in computer vision. The majority of the existing research concludespreliminary results to get immense knowledge from visual and Audio(sound) modality. To overcome the deficiency, we proposed the Deep BimodalFusion (DBF) approach to predict five traits of personality-agreeableness,extraversion, openness, conscientiousness and neuroticism. In the proposedframework, regarding visual modality, the modified convolution neural networks(CNN), more specifically Descriptor Aggregator Model (DAN) areused to attain significant visual modality. The proposed model extracts audiorepresentations for greater efficiency to construct the long short-termmemory(LSTM) for the audio modality. Moreover, employing modality-based neuralnetworks allows this framework to independently determine the traits beforecombining them with weighted fusion to achieve a conclusive prediction of thegiven traits. The proposed approach attains the optimal mean accuracy score,which is 0.9183. It is achieved based on the average of five personality traitsand is thus better than previously proposed frameworks.

Analysis on a Severe Convective Weather Process of Guangxi in 2018

Based on conventional meteorological observation data and Doppler radar data,the occurrence and development mechanism of mixed severe convective weather and evolution of convective storm in Guangxi on March 4,2018 were analyzed. The results showed that the dry line was the main trigger mechanism of this severe convective weather. Instable convection stratification of cold advection at middle layer and warm advection at low layer and abundant water vapor from low-level jet provided favorable stratification and water vapor conditions for the occurrence and development of severe convection. Cold trough at middle layer,low pressure and strong vertical wind shear at middle and lower layers may be main factors for the development and maintenance of strong storm system. Squall line developed along ground convergence line,and there was bow echo on reflectivity factor chart. Moving velocity of convective system was quick,and there was gale core and velocity ambiguity on velocity map.

Synthesis and characterization of a 1,3-dibutylimidazolium azide([BBIm][N_(3)]) : A promising green energetic ionic liquid

In the pursuit of advancing imidazolium-based energetic ionic liquids (EILs),the current study is devoted to the synthesis and characterization of 1,3-dibutyl-imidazolium azide ([BBIm][N_(3)]),as a novel member in this ionic liquids class.The chemical structure of this EIL was rigorously characterized and confirmed using FTIR spectroscopy,1D,and 2D-NMR analyses.The thermal behavior assessment was conducted through DSC and TGA experiments.DSC analysis revealed an endothermic glass transition at T_(g)=-61℃,followed by an exothermic degradation event at T_(onset)=311℃.Similarly,TGA thermograms exhibited a one-stage decomposition process resulting in 100% mass loss of the sample.Furthermore,the short-term thermal stability of the azide EIL was investigated by combining the non-isothermal TGA data with the TAS,it-KAS,and VYA/CE isoconversional kinetic approaches.Consequently,the Arrhenius parameters(E_(a)=154 kJ·mol^(-1),Log(A/s^(-1))=11.8) and the most probable reaction model g(a) were determined.The observed high decomposition temperatures and the significantly elevated activation energy affirm the enhanced thermal stability of the modified EIL.These findings revealed that[BBIm][N_(3)]EIL can be a promising candidate for advanced energetic material application.

提升自动气象站观测数据可用性方法探讨

文章分析了影响气象观测数据可用性因素,从气象资料业务系统(MDOS)数据质量控制效率、自动气象站技术保障能力和台站监控、处理能力三方面探讨提升自动气象站观测数据可用性方法。结果可满足现代气象业务对气象资料在完整性、时效性、一致性和高质量数据方面的要求,对实施气象监测精密战略任务、加快发展更高水平气象现代化业务具有重要的参考意义,可为气象防灾减灾、气象预警预测服务等提供坚实的气象基础保障。

数字化天气雷达强度标定方法

数字化天气雷达的组网观测是天气雷达发展的必然趋势 ,现有数字化天气雷达提供的强度场必须经过强度标定后再用于组网 ,因此迫切要求制定数字化天气雷达标定方法。指出了使用标定方程时应注意的量纲问题 ,针对有关文献中存在的问题提出了解决方法。

基于信息熵的危险天气下进近管制风险评估

针对危险天气下进近管制系统运行风险性大、易发不安全事件的问题,提出根据一段时期内不安全事件的信息来评估该时期进近管制系统运行的风险,从而为以后进近管制系统的安全运行管理提供依据。首先,通过分析危险天气下管制系统运行过程的风险因素,建立风险评估指标体系。其次,给出评估指标灰色关联度属性的计算方法,利用其反映出的信息熵完成指标权重的确定和风险评估模型的建立。最后,应用该模型评估某进近管制中心一段时期内管制运行的风险状态水平,得到了风险值和风险级别,验证了该方法的适用性。

区域尺度气象因子向局部尺度聚解的灰色系统与模式识别方法研究

无论是全球气候变化研究还是降水定量预报研究,都涉及如何将区域尺度气象因子向局部尺度聚解(Downscaling)的问题。提出了两种各有特色的聚解,一种是与天气动力学模式相联系的灰色系统模型方法。在宏观或中观尺度的大气动力学模型和地面局部尺度降水变化之间,嵌入一个反映区域尺度气象因子向局部尺度聚解的灰色转化器。运用我们研究的灰变量运算法则,能够将其简化为一个确定性的聚解方程和反映聚解转化不确定性的灰区间半径系统方程。通过系统识别可分析聚解过程中的确定性规律和不确定性风险问题。另一种是模式识别的方法。它的特点是在利用模式识别理论,分别辩识天气动力学模式的特征向量和地面降水模式的特征向量。然后,设法建立二者多变量的联系。给出了两种方法在湖北省降水定量预报的一个应用实例。对动力学方法、黑箱子的模式识别方法和介于两者之间的与动力学模式耦合的灰色系统方法进行了比较与评述,指出目前需进一步研究的问题。

林业与极端天气灾害:走基于生态系统的综合风险管理之路

综合分析了2012年以来世界范围内极端天气灾害发生情况及国际应对活动,以美国和中国等为案例,探究极端天气灾害与全球气候变化的关系,评估森林在应对极端天气灾害中起到的减缓灾害和遭受破坏的双重作用,对国际社会倡导的走基于生态系统的综合风险管理进行理论阐述和案例分析,并对我国林业情况提出相应的对策建议。

自动站数据缺测分析及处理方法

自动气象站自投入运行以来,因各种原因造成数据缺测的情况时有发生,影响了记录的连续性、完整性。针对此问题,笔者以本站为个例,对自动站数据缺测的原因进行初步分析,并对自动站各种气象要素缺测的数据进行处理,利用测报软件和质量控制软件对数据进行恢复,保证了记录的连续性、完整性和真实性。

散点状危险天气区域下终端区进场空域利用率

危险天气是影响航空安全,导致航班延误的重要因素,为了衡量危险天气下终端区空域的使用情况,计算终端区的空域利用率,分析散点状危险天气区域对于航空器进场飞行的影响,基于最大流最小割的扩展定理,确定受影响航段的容量变化,从流容比角度建立散点状危险天气区域下的终端区进场空域利用率模型。选取国内某终端区,在其受散点状危险天气区域影响时,评估其小时利用率,并与正常天气下的利用率进行对比。研究表明:模型计算所得利用率符合实际情况,可体现散点状危险天气区域对于终端区进场空域利用率的影响。

短时临近天气预报在农业气象服务中的运用

随着科学技术的快速发展,气象服务也越来越全面,并为农业生产提供了非常大的支持,在一定程度上促进了农业的积极发展。通过短时临近天气预报,能够准确了解近期气候、天气变化,对于一些重要的农业生产活动来说,有着非常重要的作用。因此,为了能够更好地发挥短时临近天气预报的作用,当前需要明确实际短时临近天气预报的具体应用范围,并且针对性地提出有效应用对策,为农业生产活动提供更加准确的气象服务支持。

一种新型天气分型方法及其在光伏功率预测中的应用

为提高光伏功率预测准确率提出了一种新的天气分型方法,该方法首先按总云量大小区分晴天和云天,然后根据太阳被遮蔽的程度将云天进一步细分为三类.该方法能有效识别影响光伏出力的关键气象环境因子特征,并对其加权求和得到新型分类指标Sky Condition Factor(SCF).该方法物理意义明确,区分度较好且易于量化.对天气类型合理细分后,可消除众多气象环境因子之间的耦合关系,降低输入变量维度,易于统计建模.最后分别基于原理和统计方法进行建模验证,结果显示该方法可以有效提高光伏功率预测的准确率.

基于气象雷达反演和云图外推法的临近期降雨预报方法研究

开展精准化临近期降水预报是开展实时洪涝预测预警的基础。传统的临近期降水预报一般基于气候模式进行数值预报,计算时间较长、空间分辨率低,预报精度仅为15%~20%,难以满足洪涝灾害预测预警需求。为快速、高精度、高空间分辨率预报降水过程,本研究提出了一种基于气象雷达反演和云图外推法的临近期降雨预报方法。主要包括:(1)采用金字塔光流法(Pyramid Lucas Kanade Optical Flow,简称LK光流法)进行云的运动矢量计算,根据运动矢量计算云团的运移位置;(2)提出基于回波强度和梯度特征的云层类型自动识别方法;(3)利用回波反射率与降水强度间的关系进行临近期降水量的反演及预报;(4)对预报结果进行落区精度评估和量级精度评估。2019-2020年四场降水实验研究结果表明:该方法可以实现0~1 h近实时1km高空间分辨率预测,而且预测期内的降水落区预测精度较高,平均精度可达60%;对落区内雨量站的量级精度进行对比分析得出四场降水的偏差BIAS分别为0.23 mm、3.56 mm、-7.12 mm、-5.15 mm,均方根误差RMSE分别为5.09 mm、8.45 mm、21.23 mm、14.63 mm,四场降水的相对误差绝对值均值为37.21%。预测精度均随时间增加呈下降趋势,下降程度与降水系统特征密切相关。总体来说,该方法对大范围高强度的降水预测精度较高,可为实时洪涝过程模拟分析提供重要支撑。

A physical approach of the short-term wind power prediction based on CFD pre-calculated flow fields

A physical approach of the wind power prediction based on the CFD pre-calculated flow fields is proposed in this paper. The flow fields are obtained based on a steady CFD model with the discrete inflow wind conditions as the boundary conditions, and a database is established containing the important parameters including the inflow wind conditions, the flow fields and the corresponding wind power for each wind turbine. The power is predicted via the database by taking the Numerical Weather Prediction （NWP） wind as the input data. In order to evaluate the approach, the short-term wind power prediction for an actual wind farm is conducted as an example during the period of the year 2010. Compared with the measured power, the predicted results enjoy a high accuracy with the annual Root Mean Square Error （RMSE） of 15.2% and the annual MAE of 10.80%. A good performance is shown in predicting the wind power＇s changing trend. This approach is independent of the historical data and can be widely used for all kinds of wind farms including the newly-built wind farms. At the same time, it does not take much computation time while it captures the local air flows more precisely by the CFD model. So it is especially practical for engineering projects.

Latest Climate Report Sounds Alarm on Closing Window for Mitigation

In a year that had already seen dozens of heat and extreme weather records shattered[1]and temperatures approaching the limit of human survival[2],the Intergovernmental Panel on Climate Change(IPCC)published its latest large-scale report,concluding that the world has warmed 1.1℃ above pre-industrial levels[3].Along with dire warnings for the future,the authors of the March 2023 report issued the hopeful message that many feasible and effective options are currently available to reduce greenhouse gas emissions and adapt to human-caused climate change—they just need to be implemented at greater speed and scale.

富水全风化花岗岩隧道施工技术

(南)靖(龙)海高速湖山隧道进口浅埋段为富水全风化花岗岩,开挖变形大。本文对控制围岩变形的方法进行了研究。研究得出:富水全风化花岗岩自身强度低,采用降排水措施可有效降低围岩的含水率,提高全风化花岗岩强度;采用台阶法施工开挖宽度大,隧道极易塌方,改为CRD法施工缩小了开挖宽度,钢拱架很快能封闭成环,可有效控制隧道拱顶下沉,减少围岩变形。湖山隧道进口富水浅埋段采用降排水和CRD法施工后每月平均开挖26 m,未发生塌方现象,隧道施工顺利。

民机气象雷达任务过程安全性分析方法研究

针对气象雷达系统任务过程安全性问题,以基于系统论的事故模型及过程(systems-theoretic accident model and process)理论方法为基础,提出了一种案例激励安全性分析方法。在进近阶段机载气象雷达任务过程中,通过构建系统分层控制结构,识别系统任务过程中存在的不安全控制行为,并辨识与不安全控制行为关联的潜在危险致因;构建安全飞行控制结构模型,以达美航空事故为例,提出安全约束建议控制事故衍变机制来优化模型,以提高系统任务过程安全。以上分析表明,该方法能更全面地识别系统深层危险致因,为机载气象雷达的安全性设计提供技术支持。