多普勒雷达回波在高空气象探测中的应用

在常规高空气象探测中,应用多普勒雷达回波,为高空探测前期的准备、气球净举力大小的估算、各类恶劣天气下放球时间的选择,提供了可靠有力的依据,对提高高空气象探测业务质量起到了重要作用。     

多普勒天气雷达产品在人工增雨中的作用探讨

多普勒天气雷达对捕捉可降水云体具有很好的探测作用,在指导人工增雨作业的时间、地点上有着自己特有的优势.对多普勒雷达资料在人工增雨作业中的深层次应用有待进一步地探讨.     

深空探测科学仪器研究进展

深空探测是对宇宙中地球以外的行星、卫星、小行星和彗星等星体或空间环境的探测活动.合理选择深空探测科学仪器,了解被测星体的物理及化学性质、地表地貌、动态特性以及有机物和水的存在,对人类了解地外水和生命信息、认识太阳系的起源和演化有着重要意义,是深空探测的重要任务.本文总结了深空探测的科学目标,介绍了近50年来典型深空探测任务中探测器所搭载的科学仪器及其特点,包括质谱仪、光谱仪、探地雷达和磁力计等,整理了其性能参数及面向的探测功能任务,最后讨论了深空探测科学仪器目前呈现出的小型化、低功耗、更强的环境适应能力和多仪器联合使用的发展趋势.     

新一代天气雷达及其遥感新技术应用研究

围绕气象业务需求和雷达气象的国际前沿,新一代天气雷达建设及雷达新技术以在灾害性天气监测和预测中的应用为主要目标,开展了新一代天气雷达资料质量控制、风场反演、三维数字组网等工作.双多普勒雷达和双多基地多普勒雷达在暴雨和对流系统三维探测、反演、中尺度动力和热力结构分析中发挥了重要作用.与业务单位和雷达生产厂家开展了广泛的合作,在暴雨和新疆对流过程风场中尺度结构分析等方面形成了有创新性的科研成果.在新一代天气雷达应用方面形成了有业务应用前景的成果.     

高空探测中探空气球的不正常跟踪

为提高高空探测业务质量,解决高空探测业务中出现的雷达异常跟踪问题,结合工作经验,对由于雷达性能、频率偏移、电磁干扰、仰角过低等因素所引起的丢球以及旁瓣跟踪等异常现象进行了分析,提出了一套应用饱和信号原理进行抓球的特殊方法,并归纳了雷达精度检查、电池正确浸泡等工作中的改进方法,在实际工作中取得了较好的效果.     

微型无人驾驶飞机探空试验

介绍采用具有自动导航和自动驾驶功能的微型无人驾驶飞机,探测温、压、湿、风等气象要素,并与59－01雷达探测进行对比。微型无人驾驶飞机探测资料与sporyol雷达探测资料趋势一致,在精度、采样密度方面微型无人驾驶飞机有明显的优势。     

CAMS多普勒天气雷达探测能力的估算

本文根据CAMS多普勒天气雷达实测参数对雷达的探测能力进行了估算。CAMS 多普勒天气雷达对降水强度的探测能力将比国内目前应用的713、714天气雷达更强,在200公里处能探测到的最小降水强度为0.08毫米/小时。它还具有较强的晴空探测能力,在低仰角探测时,有可能探测到50公里范围内的晴空回波,从而进行风暴前的流场研究。采用 VAD 方式可获取3公里以下晴空大气的风剖面廓线,与美国强风暴实验室(NSSL)的 NRO 多普勒天气雷达的晴空探测能力相近。由系统的相位噪声估算出测速精度为0.1米/秒左右。潜在的地物对消能力达—41db。     

711雷达观测及摄取回波资料方法的探讨

本文根据有关资料，结合作用多年的实践经验，对711雷达的探测内容、探测方法等进行了全面论述。文中介绍了最基本的观测内容，强调了雷达回波与其他有关资料相配合的问题。详细介绍了整个探测程序，特别指出了探测过程中容易出现的一些问题和可能遇到的困难，并根据711雷达的性能及雷达气象学原理，提出了解决方法和建议。文章着重讨论了回波资料的摄取方法，按照不同性质的降水回波或不同拍摄目的。分门别类做了介绍。     

雷达强度图在气象测报工作中的简单作用

探讨了多普勒雷达回波的特征在测报工作中的应用,认为利用多普勒雷达有探测范围广和直观等优点,可以改变传统的测报只是被动地随天气的变动记录其实况的特点,对从容做好测报工作起到重要作用。     

L波段探空雷达天馈线分系统常见故障分析

1 引言 高空气象探测是现代气象综合观测体系的重要组成部分,对天气预报、气候变化研究以及气候资源开发至关重要。吉林省延吉探空站L波段高空探测雷达已经投入业务运行两年多,长春、临江站也即将采用L波段探空雷达。L波段高空探测雷达-GTS1型数字式探空仪是新一代探空系统。它具有自动化程度高、体积小、安装方便等特点,可以综合观测地面至30km高空风向、风速、温度、气压、湿度等气象要素,是中国气象局统一布网的新型探空雷达。本文结合延吉探空站L波段雷达出现的故障,阐述了故障原因和排除办法。     

春玉米根系原位成像图片自动识别研究

微根窗成像技术推动了植物根系表型研究,但是根系长度和直径仍然需要人眼识别再绘制轨迹,消耗大量的人力和时间。为了解决这一难题,本研究将U-Net语义分割技术引入到植物根系图像识别中,研发了基于机器学习的iRoot-V02根系自动识别软件。采用iRoot-V02软件识别微根窗法获得的植物根系成像图片的根长、直径、投影面积、根尖数等参数。结果表明: iRoot-V02软件批处理600 dpi图片的速度为每张26.6 S; 获取根系的骨架信息、总根长与人眼识别结果基本一致; 按直径每0.1 mm为一级,获得不同直径的根长,与人眼识别结果的根长决定系数大于0.76;精确捕捉到根系生长旺盛期不同直径根长的变化; 分析了300 dpi和600 dpi两种分辨率根系图片的参数,两种分辨率结果具有高相关性,因此可建立低分辨率根系参数和高分辨率根系参数之间的关系方程,采用低分辨率拍摄根系图像,通过方程转化成更准确的根系参数,减轻工作量。用iRoot-V02软件的根系生长信息近似于人眼识别,相比人眼识别在大批量根系图像智能识别、自动化、快速目标检测方面具有巨大优势。     

华北平原冬小麦根系在土壤中的分布研究

小麦根系在土壤中的分布是准确量化植被与气候相互作用不可缺少的参数,也是小麦生产的科学管理和节水农业发展的重要科学依据。利用中国气象局固城生态环境与农业气象试验站大型根系观测系统,观测冬小麦“济麦-22”的根系参数,对根长、根长密度、根重密度和根系粗度在土壤中的垂直分布进行分析。结果表明：垂直方向上,冬小麦的根量（根长和根干重）随土壤深度增加呈减少分布。重旱、轻中旱、适宜和偏湿4种处理的根长垂直分布与自然对照的相关系数分别为0.91、0.29、0.90和0.86,其中重旱与自然对照的相关性最好,轻中旱较差。土壤水分可以影响冬小麦根系的生长。土壤水分充足时,有利于根系生长,使表层根系量增多,根干物质量增加;土壤水分亏缺时,则相反。轻度干旱有利于中下层根系的延伸生长。冬小麦的根长密度与根重密度的相关系数呈显著的正相关,根系粗度随土壤深度的增加减少。     

Use of nuclear magnetic resonance microscopy for noninvasive observations of root-soil water relations

Summary As part of our strategy to study root-soil water relationships, it was necessary to develop a nondestructive technique to detect small changes in water distribution in and near the root. Nuclear magnetic resonance imaging (MRI), clinically used to nondestructively and noninvasively acquire anatomical information, can also be used to observe water distribution in roots, soils and other plant tissues. In MRI, a sample is placed in a strong magnetic field and a sequence of radio frequency (rf) pulses and magnetic field gradients is used to measure the concentration and relaxation properties of protons, chiefly those associated with water. This information is then reconstructed into a digital image representing the spatial distribution of water in plant tissues and soil. Today, intact roots less than 1 mm in diameter growing in soil or synthetic media can be clearly imaged in less than 4 minutes at resolutions typically less than 30µm. This permits rapid production of images that simultaneously distinguish temporal changes in water distribution in root tissue, the rhizosphere and the adjacent soil at microscopic levels. Applications of this technique for investigating plant-soil water relationships will be discussed.With 3 Figures     

土壤-植物-大气连续体（SPAC）系统中植物根系吸水模型研究进展

土壤植物系统是土壤-植物-大气连续体(SPAC)中的一个重要子系统。植物根系吸水过程是SPAC系统中水分运移规律研究的重要内容。从影响根系吸收土壤水分的影响因素人手，现有根系吸水模型可分为经验模型、半经验半理论模型和理论模型3类。文章综述了自20世纪60年代初第一个单根吸水模型以来的植物根系吸水模型及其最新研究进展，指出未来研究的重点是注重根系吸水机理同时，修改和完善已有根系吸水模型，简化模型参数，使之更易应用于实际。     

干旱和复水对羊草碳氮分配的影响

植物的碳氮营养及其相互关系是最重要的基本生物过程之一,阶段性干旱对植物碳氮分配的影响研究甚少。实验以中国北方草原的典型植物羊草为材料,研究不同干旱持续期复水对羊草碳氮含量、分配及其相互关系的影响。结果表明:短期和中期干旱使植株生物量、氮素水平和单株总氮量增加,但长期干旱使之降低。水分处理对碳含量的影响不显著。干旱后复水降低了各器官特别是绿叶的碳氮比。中度持续干旱的氮素利用率(NUE)最高、短期干旱最低。羊草各器官氮素绝对量占整株的百分比从大到小依次为:绿叶、根茎、根、枯叶和茎鞘,其中叶片的氮素总量占植株的一半以上;随着干旱持续期的增加,氮素对根部的投资亦加大。羊草受到适当干旱驯化后复水引发的超补偿作用可促进羊草植株生长、提高氮素水平,并在一定程度上通过碳氮分配的调节作用来适应于阶段性的干旱胁迫。     

高CO2条件下贝加尔针毛对土壤干旱胁迫响应的试验研究

贝加尔针毛 (Stipa baicalensis) 是我国内蒙古东部和东北西部主要的地带性植被之一。通过人工模拟试验分析了CO₂浓度升高对贝加尔针毛的“施肥”效应, 结果表明:贝加尔针毛的生物量、生长量随CO₂浓度的升高而增加, 根中P的含量、叶中C、N、P的含量也随CO₂浓度的升高而增加。土壤干旱胁迫对贝加尔针毛的生物量、生长量的影响均为负效应, 且干旱程度的加重使影响更明显。干旱使针毛叶、根中的C、N含量增加。     

Complex aspen forest carbon and root dynamics during drought

Drought-induced vegetation mortality has been documented on every vegetated continent in recent decades and constitutes a major uncertainty in climate change impacts on terrestrial ecosystems and carbon cycle feedbacks. While recent research has focused on specific failure mechanisms during drought-induced forest die-off, a broader understanding of the physiology of trees under drought, especially changes in growth and carbon allocation, is needed for determining the sensitivity of forests to drought and interacting mechanisms during forest mortality. I present here multi-tissue and high-resolution temporal dynamics of tree carbon resources during moderate experimental and natural drought in trembling aspen (Populus tremuloides) forests, a major forest type in western North America that recently experienced widespread drought-induced die-off. Drought led to substantial declines in inferred carbon uptake. Tree carbohydrate concentrations, however, largely increased in concert with substantial decreases in growth and severe declines in root biomass. These findings highlight that growth declines, especially in fine roots which are important to water uptake, and increased carbon allocation to root non-structural carbohydrates are key responses to drought in aspen and could play an important role in widespread die-off. They suggest multi-year consequences of drought and carbon-hydraulic interconnections. They underscore the need for a more integrated multi-tissue, multi-process, and multi-year perspective of climate-induced forest mortality.     

Annual Frequency of Tropical Cyclones Directly Affecting Guangdong Province:Prediction Based on LSTM-FC

Tropical cyclone (TC) annual frequency forecasting is significant for disaster prevention and mitigation in Guangdong Province. Based on the NCEP-NCAR reanalysis and NOAA Extended Reconstructed global sea surface temperature (SST) V5 data in winter, the TC frequency climatic features and prediction models have been studied. During 1951-2019, 353 TCs directly affected Guangdong with an annual average of about 5.1. TCs have experienced an abrupt change from abundance to deficiency in the mid to late 1980 with a slightly decreasing trend and a normal distribution. 338 primary precursors are obtained from statistically significant correlation regions of SST, sea level pressure, 1000hPa air temperature, 850hPa specific humidity, 500hPa geopotential height and zonal wind shear in winter. Then those 338 primary factors are reduced into 19 independent predictors by principal component analysis (PCA). Furthermore, the Multiple Linear Regression (MLR), the Gaussian Process Regression (GPR) and the Long Short-term Memory Networks and Fully Connected Layers (LSTM-FC) models are constructed relying on the above 19 factors. For three different kinds of test sets from 2010 to 2019, 2011 to 2019 and 2010 to 2019, the root mean square errors (RMSEs) of MLR, GPR and LSTM-FC between prediction and observations fluctuate within the range of 1.05-2.45, 1.00-1.93 and 0.71-0.95 as well as the average absolute errors (AAEs) 0.88-1.0, 0.75-1.36 and 0.50-0.70, respectively. As for the 2010-2019 experiment, the mean deviations of the three model outputs from the observation are 0.89, 0.78 and 0.56, together with the average evaluation scores 82.22, 84.44 and 88.89, separately. The prediction skill comparisons unveil that LSTM-FC model has a better performance than MLR and GPR. In conclusion, the deep learning model of LSTM-FC may shed light on improving the accuracy of short-term climate prediction about TC frequency. The current research can provide experience on the development of deep learning in this field and help to achieve further progress of TC disaster prevention and mitigation in Guangdong Province.     

A DIAGNOSTIC EXPERIMENTAL STUDY OF EFFECTS OF CO_2 ENRICHMENT ON COTTON GROWTH,DEVELOPMENT AND YIELD

A series of experiments were conducted about the effects of different CO_2 concentrations andventilating time on cotton using OPT-1 open top chambers.The results show that.for all theventilating treatments,the development stages of cotton were advanced,plant height increased,thebiomass of root and stalk increased when CO_2 concentration increased: the number of bolls formed insummer was more than 85% of the total for the treatment of 700 ppm CO_2 concentration; single bollweight,the number of bolls per plant,ginned and unginned cotton weights,and stalk weightincreased obviously when CO_2 concentration increased,but the influences of CO_2 enrichment on theratio of ginned weight to the total weight of ginned and unginned cotton,fibre length and ginnedweight of 100-seed were small.     

Determination of microbial versus root‐produced CO2 in an agricultural ecosystem by means of δ13CO2 measurements in soil air

The amounts of microbial and root‐respired CO₂ in a maize/winter wheat agricultural system in south western Germany were investigated by measurements of the CO₂ mixing ratio and the ¹³C/¹²C ratio in soil air. CO₂ fluxes at the soil surface for the period of investigation (1993–1995) were also determined. Root respired CO₂ shows a strong correlation with the plant mass above ground surface of the respective vegetation (R²≥0.88); the maximum CO₂ release from roots was in August for the maize (2.0±0.5 mmol m⁻² h⁻¹) and in June for winter wheat (1.5±0.5 mmol m⁻² h⁻¹). Maximum CO₂ production by roots correlate well with the maximum amount of plant root matter. Integrating the CO₂ production over the whole growing season and normalizing to the dry root matter yields, the CO₂ production per gram dry organic root matter (DORM) of maize was found to be 0.14±0.03 gC (g DORM)⁻¹. At the sites investigated, root‐produced CO₂ contributed (16±4)% for maize, and (24±4)% for winter wheat, respectively, to the total annual CO₂ production in the soil (450±50 gC m⁻² for maize, 210±30 gC m⁻² for winter wheat).