多普勒雷达资料循环同化在台风“鲇鱼”预报中的应用

高分辨率的中尺度预报模式ARPS及其3DVAR/云分析系统,针对2010年登陆福建的超强台风“鲇鱼”,研究对流可分辨尺度下,每1h循环同化沿海新一代多普勒雷达网资料分析、研究对台风初始场和预报场的改进作用.结果表明:单独同化雷达资料可显著改善初始场中的台风内核区动力和热力结构,以及台风强度和位置,进而提高18h台风强度、路径和降水预报,但预报路径和降水分布与实况仍存在差异.在雷达资料同化基础上加入常规观测资料,对初始场中台风内核区结构改进不大.但在显著调整大尺度背景场,从而进一步减少台风路径预报误差,能准确预报出福建沿海两个强降水区域的位置和强度.总体而言,雷达资料同化主要提高台风结构分析,而常规观测资料同化主要改善环境场分析,两者有效结合使得预报结果和实况最为接近.     

Calculating the Climatology and Anomalies of Surface Cloud Radiative Effect Using Cloud Property Histograms and Cloud Radiative Kernels

Cloud radiative kernels (CRK) built with radiative transfer models have been widely used to analyze the cloud radiative effect on top of atmosphere (TOA) fluxes, and it is expected that the CRKs would also be useful in the analyses of surface radiative fluxes, which determines the regional surface temperature change and variability. In this study, CRKs at the surface and TOA were built using the Rapid Radiative Transfer Model (RRTM). Longwave cloud radiative effect (CRE) at the surface is primarily driven by cloud base properties, while TOA CRE is primarily decided by cloud top properties. For this reason, the standard version of surface CRK is a function of latitude, longitude, month, cloud optical thickness (τ) and cloud base pressure (CBP), and the TOA CRK is a function of latitude, longitude, month, τ and cloud top pressure (CTP). Considering that the cloud property histograms provided by climate models are functions of CTP instead of CBP at present, the surface CRKs on CBP-τ histograms were converted to CTP-τ fields using the statistical relationship between CTP, CBP and τ obtained from collocated CloudSat and MODIS observations. For both climate model outputs and satellites observations, the climatology of surface CRE and cloud-induced surface radiative anomalies calculated with the surface CRKs and cloud property histograms are well correlated with those calculated from surface radiative fluxes. The cloud-induced surface radiative anomalies reproduced by surface CRKs and MODIS cloud property histograms are not affected by spurious trends that appear in Clouds and the Earth's Radiant Energy System (CERES) surface irradiances products.     

Relationships between Cloud Droplet Spectral Relative Dispersion and Entrainment Rate and Their Impacting Factors

Cloud microphysical properties are significantly affected by entrainment and mixing processes. However, it is unclear how the entrainment rate affects the relative dispersion of cloud droplet size distribution. Previously, the relationship between relative dispersion and entrainment rate was found to be positive or negative. To reconcile the contrasting relationships, the Explicit Mixing Parcel Model is used to determine the underlying mechanisms. When evaporation is dominated by small droplets, and the entrained environmental air is further saturated during mixing, the relationship is negative. However, when the evaporation of big droplets is dominant, the relationship is positive. Whether or not the cloud condensation nuclei are considered in the entrained environmental air is a key factor as condensation on the entrained condensation nuclei is the main source of small droplets. However, if cloud condensation nuclei are not entrained, the relationship is positive. If cloud condensation nuclei are entrained, the relationship is dependent on many other factors. High values of vertical velocity, relative humidity of environmental air, and liquid water content, and low values of droplet number concentration, are more likely to cause the negative relationship since new saturation is easier to achieve by evaporation of small droplets. Further, the signs of the relationship are not strongly affected by the turbulence dissipation rate, but the higher dissipation rate causes the positive relationship to be more significant for a larger entrainment rate. A conceptual model is proposed to reconcile the contrasting relationships. This work enhances the understanding of relative dispersion and lays a foundation for the quantification of entrainment-mixing mechanisms.     

The Significant Role of Radiosonde-measured Cloud-base Height in the Estimation of Cloud Radiative Forcing

The satellite-based quantification of cloud radiative forcing remains poorly understood, due largely to the limitation or uncertainties in characterizing cloud-base height (CBH). Here, we use the CBH data from radiosonde measurements over China in combination with the collocated cloud-top height (CTH) and cloud properties from MODIS/Aqua to quantify the impact of CBH on shortwave cloud radiative forcing (SWCRF). The climatological mean SWCRF at the surface (SWCRF_SUR), at the top of the atmosphere (SWCRF_TOA), and in the atmosphere (SWCRF_ATM) are estimated to be ?97.14, ?84.35, and 12.79 W m^?2, respectively for the summers spanning 2010 to 2018 over China. To illustrate the role of the cloud base, we assume four scenarios according to vertical profile patterns of cloud optical depth (COD). Using the CTH and cloud properties from MODIS alone results in large uncertainties for the estimation of SWCRF_ATM, compared with those under scenarios that consider the CBH. Furthermore, the biases of the CERES estimation of SWCRF_ATM tend to increase in the presence of thick clouds with low CBH. Additionally, the discrepancy of SWCRF_ATM relative to that calculated without consideration of CBH varies according to the vertical profile of COD. When a uniform COD vertical profile is assumed, the largest SWCRF discrepancies occur during the early morning or late afternoon. By comparison, the two-point COD vertical distribution assumption has the largest uncertainties occurring at noon when the solar irradiation peaks. These findings justify the urgent need to consider the cloud vertical structures when calculating the SWCRF which is otherwise neglected.     

GF-5 DPC数据的云检测方法研究

高分五号（GF-5）号卫星所搭载的大气多角度偏振探测仪（DPC）能够对地球进行多波段,多角度和的连续观测,其数据对研究全球大气云分布及云辐射反馈作用提供新的视角。本文通法国多角度偏振载荷POLDER（POLarization and Directionality of the Earth’s Reflectances）云检测算法为参考,结合DPC多波段反射率、偏振反射率、表观压强等信息开发了一个适用于DPC的云检测算法。算法主要分为3个部分：首先是阈值方法对云像元进行检测,同时引入表观压强对不同高度的云（如卷云、层积云等）进行进一步的条件约束,然后利用865 nm波段偏振反射率对海表反射的太阳耀斑区进行识别,修正了反射率阈值识别云像元时受到的太阳耀斑干扰。为了验证算法的准确性,利用2018-10-01的MODIS的MOD06云掩码产品与本文云检测算法结果进行定性分析,从目视判读结果可以看出本文云检测结果与MOD06产品具有较高的吻合度;随后又利用2018-10-01—04的CALIPSO-VFM数据与本文云检测结果和MYDO6云掩码产品进行定量分析,分别计算了中低纬度区域（60°N—60°S）的云/晴空像元命中率和云/晴空像元错误预报率,计算结果显示算法云命中率均值相较MYD06云掩码产品高出13.501%的前提下云错误预报率仅高出3.561%,可表明该算法在全球中低纬度区域有着良好的云检测效果。本文提出的云检测算法,可为后续DPC的云参数、水汽、气溶胶等研究提供重要数据支撑。     

基于L波段探空的云区边界识别改进算法

利用气象业务中使用的L波段探空数据和毫米波云雷达观测资料,分析探空相对湿度在入云和出云时的变化规律,提出一种基于探空相对湿度阈值与梯度相结合的云区边界识别改进算法,并利用云雷达观测数据对算法识别结果进行验证.利用北京市南郊观象台2019年1—6月层状云样本验证分析,结果表明:改进算法相比相对湿度阈值法,对云区边界识别更...     

Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z)

Surveying techniques such as terrestrial laser scanner have recently been used to measure surface changes via 3D point cloud (PC) comparison. Two types of approaches have been pursued: 3D tracking of homologous parts of the surface to compute a displacement field, and distance calculation between two point clouds when homologous parts cannot be defined. This study deals with the second approach, typical of natural surfaces altered by erosion, sedimentation or vegetation between surveys. Current comparison methods are based on a closest point distance or require at least one of the PC to be meshed with severe limitations when surfaces present roughness elements at all scales. To solve these issues, we introduce a new algorithm performing a direct comparison of point clouds in 3D. The method has two steps: (1) surface normal estimation and orientation in 3D at a scale consistent with the local surface roughness; (2) measurement of the mean surface change along the normal direction with explicit calculation of a local confidence interval. Comparison with existing methods demonstrates the higher accuracy of our approach, as well as an easier workflow due to the absence of surface meshing or Digital Elevation Model (DEM) generation. Application of the method in a rapidly eroding, meandering bedrock river (Rangitikei River canyon) illustrates its ability to handle 3D differences in complex situations (flat and vertical surfaces on the same scene), to reduce uncertainty related to point cloud roughness by local averaging and to generate 3D maps of uncertainty levels. We also demonstrate that for high precision survey scanners, the total error budget on change detection is dominated by the point clouds registration error and the surface roughness. Combined with mm-range local georeferencing of the point clouds, levels of detection down to 6 mm (defined at 95% confidence) can be routinely attained in situ over ranges of 50 m. We provide evidence for the self-affine behaviour of different surfaces. We show how this impacts the calculation of normal vectors and demonstrate the scaling behaviour of the level of change detection. The algorithm has been implemented in a freely available open source software package. It operates in complex 3D cases and can also be used as a simpler and more robust alternative to DEM differencing for the 2D cases.     

Filtering airborne LiDAR data by embedding smoothness-constrained segmentation in progressive TIN densification

Progressive TIN densification (PTD) is one of the classic methods for filtering airborne LiDAR point clouds. However, it may fail to preserve ground measurements in areas with steep terrain. A method is proposed to improve the PTD using a point cloud segmentation method, namely segmentation using smoothness constraint (SUSC). The classic PTD has two core steps. The first is selecting seed points and constructing the initial TIN. The second is an iterative densification of the TIN. Our main improvement is embedding the SUSC between these two steps. Specifically, after selecting the lowest points in each grid cell as initial ground seed points, SUSC is employed to expand the set of ground seed points as many as possible, as this can identify more ground seed points for the subsequent densification of the TIN-based terrain model. Seven datasets of ISPRS Working Group III/3 are utilized to test our proposed algorithm and the classic PTD. Experimental results suggest that, compared with the PTD, the proposed method is capable of preserving discontinuities of landscapes and reducing the omission errors and total errors by approximately 10% and 6% respectively, which would significantly decrease the cost of the manual operation required for correcting the result in post-processing.     

多核处理器的机载激光雷达点云并行三角网渐进加密滤波方法

滤波是机载LiDAR点云数据处理的关键步骤之一,点云数据的海量化特性使得一般的串行化滤波处理方法无法满足快速成图的应用需求。提出一种基于多核计算技术的并行三角网渐进加密滤波方法,将串行方法中最耗时的三角网构建与脚点判别过程进行了并行化改造。三角网构建算法的并行化基于分治法实现,脚点判别算法的并行化采用一种随机分配策略将三角网划分为多个离散分布的三角形子集合来实现负载均衡。并行滤波方法在8核环境下多次渐进加密的实际加速比达到3.1左右。试验证明,该方法可以充分发挥多核计算优势,并且对不同分布形态点云数据具有良好的适应性。     

Terrasolid软件处理激光点云数据的研究与改进

Terrasolid软件能够进行机载LiDAR激光点云数据的粗差剔除、滤波和分类等操作,目前应用较为广泛,但由于源代码不公开,处理数据的准确性和真实度还有待商榷.文中基于Terrasolid软件开发了一个精度估算的模块,为实验参数的设置提供技术参考.该模块在点云数据处理实验中运用,取得良好的效果.