利用卫星遥感资料估算区域尺度空气温度

空气温度是地球大气系统能量和水分循环的关键参数,气象台站观测的空气温度是单点观测的,空间代表性较差,在区域尺度模型中应用还存在一些问题,作者提出了一个从卫星遥感资料直接反演空气温度的新方法。基于NOAAAVHRR资料反演的地表温度(Land Surface Temperature,T_(LS))和地面观测的空气温度(Air Temperature,T_a)的相关关系,建立了稀疏植被区域不同高程范围的空气温度遥感估算统计方法;基于NDVI和T_(LS)的梯形空间特征关系,建立了在中、高植被覆盖区域的空气温度遥感估算物理方法。经检验,稀疏植被区域空气温度反演绝对误差在1.5～1.8℃之间,中、高植被覆盖区域空气温度反演平均绝对误差为1.61℃,表明作者提出的空气温度遥感反演方法是可行的。     

RETRIEVING ATMOSPHERIC SOUNDING PROFILES AROUND TYPHOON YUNNA USING INFRARED HYPERSPECTRAL MEASUREMENTS AIRS

In this study,we derived atmospheric profiles of temperature,moisture,and ozone,along with surface emissivity,skin temperature,and surface pressure,from infrared-sounder radiances under clear sky (cloudless) condition.Clouds were detected objectively using the Atmospheric Infrared Sounder under a relatively low spatial resolution and cloud-mask information from the Moderate Resolution Imaging Spectroradiometer under a high horizontal resolution;this detection was conducted using space matching.Newton’s nonlinear physical iterative solution technique is applied to the radiative transfer equation (RTE) to retrieve temperature profiles,relative humidity profiles,and surface variables simultaneously.This technique is carried out by using the results of an eigenvector regression retrieval as the background profile and using corresponding iterative forms for the weighting functions of temperature and water-vapor mixing ratio.The iterative forms are obtained by applying the variational principle to the RTE.We also compared the retrievals obtained with different types of observations.The results show that the retrieved atmospheric sounding profile has great superiority over other observations by accuracy and resolution.Retrieved profiles can be used to improve the initial conditions of numerical models and used in areas where conventional observations are sparse,such as plateaus,deserts,and seas.     

多普勒激光雷达风场反演方法研究

采用三维变分同化反演(3DVAR)、 四维变分同化反演(4DVAR) 对多普勒激光雷达资料反演风场的方法进行了研究, 利用车载多普勒激光雷达在2008年残奥会测试赛期间外场试验取得的数据, 反演了海面10 m高度处的风场, 并将风场反演结果与浮标资料进行了对比分析, 结果表明: 3DVAR、4DVAR风场反演方法均能实现近海面风的精细化风场反演, 并能反映出风向的变化, 反演风场与浮标数据基本一致, 在风速较大的天气情况, 3DVAR与4DVAR反演风场的一致性要好于风速较小的天气情况; 4DVAR反演方法中以浮标资料作为背景场, 使得其与浮标的符合程度要好于3DVAR方法反演风场; 反演风场的风向与浮标风向具有很好的相关关系, 反演风场的风速与浮标风速具有一定的相关关系, 反演风场的风向、风速与浮标的风向、风速之间平均均方根误差和平均绝对误差表明, 这两序列之间具有一定差别, 在风速较小的天气情况下使用时需要注意。     

GIS技术在决策气象服务系统建设中的应用

该文介绍了基于组件式GIS技术的决策气象服务系统建设的总体框架, 并阐述了系统的建设内容, 主要包括电子地图建设、气象制图输出、实时与历史资料检索、灾情分析、台风预警与损失评估以及参数化制图等功能; 同时对GIS应用于决策气象服务系统的若干关键技术问题进行了深入讨论与分析, 如数据共享与转换、气象要素插值算法、空间信息检索方法、基于插件技术的模型评估方法、三维可视化技术; 最后给出了相关结论, 并指出该系统将为全国综合性决策服务系统提供一个良好的建设思路。     

基于Item2Vec负采样优化的专题地图产品个性化推荐方法研究

建立适用于专题地图产品检索的用户偏好推荐模型是提高专题地图质量的有效方式之一,在专题地图产品推荐场景中,存在严重的内容冷启动和评论数据稀疏问题,现有的推荐算法无法为特定类用户推荐不同特征的专题地图产品,导致用户从专题地图中获取偏好信息受到限制。因此,本文构建基于负采样的连续词袋模型和基于Word2Vec的Item2Vec相结合的用户偏好推荐方法,用于专题地图产品推荐。① 计算用户行为日志文件中交互行为数据的隐性评分,以代替专题地图推荐信息流场景中稀疏的用户评论数据;② 基于负采样的连续词袋模型提取目标专题地图的前后地图序列感知特征信息,通过控制正负样本比例为1:2,提升目标专题地图潜在评分的预测精度;③ 通过Item2Vec将带有用户行为特征信息的专题地图映射到向量空间,计算用户对专题地图的相似度矩阵,根据用户偏好程度完成推荐。在构建的专题地图评分实验数据集Thematic CMaps和4个公开验证数据集MovieLens上的测试结果表明:与LFM、Personal Rank、Content Based和SVD 4种传统推荐算法相比,本文所提方法可有效提高潜在评分的预测精度,推荐性能最高达到27.85%;与以霍夫曼采样方式的Item2Vec基础方法和YouTubeNet 2种神经网络推荐算法相比,评分预测精度有一定提高,且推荐性能不断提升,最高达到2.97%和5.78%。以经典算法奇异值分解（SVD）为例,将MovieLens-20M数据集切分后,在数据量不断增大的数据子集中,本文所用方法的评分预测精度和性能均优于SVD方法。     

WIND SHEAR IDENTIFICATION WITH THE RETRIEVED WIND OF DOPPLER WEATHER RADAR

Wind shear reflects that the wind field is not uniform, which is one of the primary factors which make the retrieval of the wind field difficult. Based on volume velocity process(VVP) wind field retrieval technique, the intensity of wind shear is identified in this paper. After analyzing the traditional techniques that rely on the difference of radial velocity to identify wind shear, a fixed difference among radial velocities that may cause false identification in a uniform wind field was found. Because of the non-uniformity in wind shear areas, the difference of retrieved results between surrounding analysis volumes can be used as a measurement to show how strong the wind shear is. According to the analysis of a severe convective weather process that occurred in Guangzhou, it can be found that the areas of wind shear appeared with the strength significantly larger than in other regions and the magnitude generally larger than4.5 m/(s·km). Besides, by comparing the variation of wind shear strength during the convection, it can be found that new cells will be more likely to generate when the strength is above 3.0 m/(s·km). Therefore, the analysis of strong wind shear's movement and development is helpful to forecasting severe convections.     

RPG-HATPRO-G3地基微波辐射计反演产品评估

RPG-HATPRO-G3地基微波辐射计采用“多通道并行测量技术”,性能稳定,反演精度高。应用该微波辐射计和常规L波段探空数据,比较了微波辐射计反演数据与探空测值的差异。结果表明：温度、水汽密度与常规探空资料比较有很好的线性相关性,对仪器维护后可以提高数据质量;相对湿度数据离散度较高。比较无降水时不同高度下微波辐射计反演数据的精度,温度在1 000 m以下低层平均误差和均方根误差较小;水汽密度的平均误差和均方根误差均为近地面较大,随高度而减小;相对湿度的平均误差和均方根误差都明显较大,温度和水汽密度的准确性高于相对湿度。降水时不同高度的温度、水汽密度和相对湿度的平均误差和均方根误差变化趋势均与无降水时相似,但是误差值明显偏大;降水时反演温度在2 000 m以下误差较小,水汽密度在3 000 m以下反演值较探空测值大,相对湿度在降水时的误差较大。     

青藏高原对流云的偏振雷达观测研究

利用可移动式C波段双偏振雷达（C-POL）,以及那曲新一代天气雷达（CINRAD/CD）于2014年7月30日和8月5日在西藏那曲地区的观测资料,并通过双多普勒雷达风场反演、偏振雷达相态识别,清晰展示了这两次高原冰雹云发生发展的动力、微物理、热力结构特征。结果表明:青藏高原地区的对流云多在午后出现,水平及垂直尺度不大,但是对流云发生频繁、生消快,一般持续几十分钟。从RHI扫描的水平偏振反射率因子（Z_H）、差分反射率因子（Z_DR）,以及反演的相态（Class）分布上可以明显看出,粒子跟随"0线"抬高,不断增长,回波强度也越来越大,并最终超过主上升气流从另一侧降落,形成冰雹墙的整个动力与微物理过程。从连续时次的RHI上还观测到一次对流单体发生、发展过程中相态从湿雪到冰雹的变化,单体刚刚触发时,回波高度不高,强度还很弱,但是却出现成片的湿雪区域,说明上升气流非常旺盛,将本来落到0℃层以下的未完全融化的湿雪重新带到0℃层以上,通过凝华、凇附、攀附等物理过程,仅仅10多分钟,这些湿雪就能够迅速增长成为冰雹。这些湿雪重新凝结过程中,释放潜热,进一步促进了不稳定结构,加强了上升气流和下沉气流。因此,如果某个刚刚生成的弱回波区域内,在融化层以上出现大量的湿雪,往往预示着该区域上升气流强劲,会迅速发展成强回波单体。      

GVR机载微波辐射计反演算法适用性分析

为研究GVR(G-band water Vapor Radiometer)机载微波辐射计自带反演算法在天津地区的适用性,将2016年北京探空资料分成春夏秋冬,对垂直累积液态水和垂直累积水汽的反演精度进行数值模拟检验。结果表明:垂直累积液态水反演精度随高度变化不明显,春夏秋冬4个季节反演结果相对偏差值范围分别为29%~78%、31%~71%、36%~67%、35%~79%,绝对偏差值范围分别为0.04~0.492 mm、0.075~0.294 mm、0.074~0.315mm、0.116~0.347mm;垂直累积水汽反演精度随高度降低(3000m以上降低更为明显),春、夏、秋、冬4个季节相对偏差3000m以下时分别为2.6%~20.8%、7.9%~19.1%、4.3%~16.5%、3.4%~14.2%,3000m以上时分别为6.4%~89.7%、12.5%~36.9%、13.2%~50%、11.8%~301%。与其他类型机载微波辐射计反演精度及GVR在北极地区地基观测反演精度相比,GVR自带反演算法在天津地区的垂直累积液态水和垂直累积水汽反演精度明显偏低。     

Classifying natural-language spatial relation terms with random forest algorithm

The exponential growth of natural language text data in social media has contributed a rich data source for geographic information. However, incorporating such data source for GIS analysis faces tremendous challenges as existing GIS data tend to be geometry based while natural language text data tend to rely on natural language spatial relation (NLSR) terms. To alleviate this problem, one critical step is to translate geometric configurations into NLSR terms, but existing methods to date (e.g. mean value or decision tree algorithm) are insufficient to obtain a precise translation. This study addresses this issue by adopting the random forest (RF) algorithm to automatically learn a robust mapping model from a large number of samples and to evaluate the importance of each variable for each NLSR term. Because the semantic similarity of the collected terms reduces the classification accuracy, different grouping schemes of NLSR terms are used, with their influences on classification results being evaluated. The experiment results demonstrate that the learned model can accurately transform geometric configurations into NLSR terms, and that recognizing different groups of terms require different sets of variables. More importantly, the results of variable importance evaluation indicate that the importance of topology types determined by the 9-intersection model is weaker than metric variables in defining NLSR terms, which contrasts to the assertion of ‘topology matters, metric refines’ in existing studies.