Main Detrainment Height of Deep Convection Systems over the Tibetan Plateau and Its Southern Slope

Deep convection systems (DCSs) can rapidly lift water vapor and other pollutants from the lower troposphere to the upper troposphere and lower stratosphere. The main detrainment height determines the level to which the air parcel is lifted. We analyzed the main detrainment height over the Tibetan Plateau and its southern slope based on the CloudSat Cloud Profiling Radar 2B_GEOPROF dataset and the Aura Microwave Limb Sounder Level 2 cloud ice product onboard the A-train constellation of Earth-observing satellites. It was found that the DCSs over the Tibetan Plateau and its southern slope have a higher main detrainment height (about 10?16 km) than other regions in the same latitude. The mean main detrainment heights are 12.9 and 13.3 km over the Tibetan Plateau and its southern slope, respectively. The cloud ice water path decreases by 16.8% after excluding the influences of DCSs, and the height with the maximum increase in cloud ice water content is located at 178 hPa (about 13 km). The main detrainment height and outflow horizontal range are higher and larger over the central and eastern Tibetan Plateau, the west of the southern slope, and the southeastern edge of the Tibetan Plateau than that over the northwestern Tibetan Plateau. The main detrainment height and outflow horizontal range are lower and broader at nighttime than during daytime.     

2018年常州一次罕见持续性雾-霾天气分析

利用常规气象观测资料、探空资料、污染物浓度及AQI资料、NCEP再分析资料等,对2018年11月24日至12月3日夜间常州持续11 d的强浓雾和严重霾天气过程进行了分析。结果表明：（1）此次雾-霾过程持续时间长、范围广、强度大、污染重。（2）中纬度地区高层持续纬向环流控制、中低层暖脊稳定存在,地面持续受均压场或弱倒槽顶部、弱冷锋前部影响,是这次持续性雾-霾过程的重要天气条件。（3）边界层内弱辐散、负涡度及弱的下沉气流是此次雾-霾天气得以长时间维持、发展的动力因子。近地层长时间水汽饱和且维持小风速利于雾-霾的长时间维持。（4）近地面高强度的贴地逆温长时间维持和持续较低的混合层高度是此次雾-霾形成、发展和长时间维持的重要热力条件。雾比霾的平均混合层高度明显偏低且霾等级越高混合层高度越低,混合层高度的变化先于能见度变化,对雾-霾临近预警有较好的指导作用。（5）弱冷空气渗透、风速适当增加、混合层高度的先期快速下降、负净辐射曝辐量绝对值的明显增大是雾爆发性增强的主要原因。     

2017年5月7日广州特大暴雨模拟中的背景场影响分析

2017年5月7日广州发生了特大暴雨,为研究不同背景场资料对这次暴雨过程的影响,模式背景场分别采用美国NCEP的GFS资料和中国的T639资料,利用GRAPES_Meso模式对这次暴雨过程进行了数值模拟和影响分析;数值试验结果表明,采用不同的背景场对这次暴雨过程具有显著影响,用T639资料(T639_run)作为模式背景场大致模拟出了这次暴雨过程,而采用NCEP GFS数据(GFS_run)模拟的降水明显偏北。其原因是,采用T639资料做背景场时,华南暴雨区域存在深厚的水汽输送,同时存在强烈的上升运动,可以产生极端强降水;而采用GFS资料进行数值模拟时,实际暴雨区上空的上升气流较弱,水汽输送也较弱,使强降水落区偏北。GRAPES_Meso模式模拟的华南地区的云顶高度整体偏高,云顶温度整体偏低,相对来说,采用T639_run的模拟结果优于GFSrun的结果,该研究结果可以为云降水方案中的水物质和云量计算方案的改进和优化提供一定的参考。     

公路勘测中应注意的几个问题

结合营口滨海大道勘测设计工程实例,探讨了公路勘测时坐标系统选择、平面控制点点位选定、全站仪三角高程代替四等水准、RTK技术应用等一些问题,并提出了几点结论。     

Observation on height changes of chinese tide gauges by GPS

IntroductionBecause of global temperature rising and glacierthawing,the sea level has risen about 10-25 cmin the past century[1]. The sea level change isone of the main ai ms in global change monito-ring. Presently the basic tool used in sea levelmonitori…     

单面边坡的两种动力反应形式及其临界高度

把单面边坡的动力反应分为高边坡动力反应和低边坡动力反应两类. 研究了两种不同动力反应形式临界边坡高度的影响因素. 量纲分析表明弹性条件下边坡动力反应临界高度Hthre与构成边坡材料的动弹性模量Ed的平方根成正比,与边坡材料的密度ρ成反比,与边坡动力输入的特征周期T成正比. 结合弹性波动理论,利用大量数值模拟和统计分析得到了Hthre的表达式. 当边坡的高度大于Hthre时,边坡的动力反应呈现高边坡动力反应规律,当边坡的高度小于Hthre时,边坡的动力反应呈现低边坡动力反应规律.     

Hilbert变换在海浪波高与周期联合分布函数中的应用

Longuet-Higgins(1983)[1]导出了波高与周期的联合分布函数,此分布函数虽然与实际数据符合良好,但存在很大的缺陷,如:由此分布函数得出的波高分布为形式较为复杂的非Rayleigh分布,很难应用于工程计算中。孙孚(1988a)[2]应用射线理论导出了一种波高与周期联合分布,虽然弥补了Longuet-Higgins的一些缺陷,但推导过程过于复杂。本文在窄谱假定下通过应用Hilbert变换方法得出新的分布函数并与前两者比较,表明Hilbert变换的方法不但简便,而且完全克服Longuet-Higgins的不足,可以方便的应用于工程计算中。本文也为Hilbert变换的方法在工程中的应用提供了理论依据。     

Long baroclinic Rossby waves with periods of about 500d near 20°N in the northwest Pacific Ocean

On the basis of maps of sea level anomalies data set from October 1992 to January 2004, pronounced low frequency variations with periods of about 500 d are detected in the area near 20°N from 160°W to 130°E. A linear two-layer model is employed to explain the mechanism. It is found that the first-mode long baroclinic Rossby waves at 20°N in the northwest Pacific propagate westward in the form of free waves at a speed of about 10.3 cm/s. This confirms that the observed low frequency variabilities appear as baroclinic Rossby waves. It further shows that these low frequency variabilities around 20°N in the northwest Pacific can potentially be predicted with a lead up to 900 d.     

Moving from plot-based to hillslope-scale assessments of savanna vegetation structure with long-range terrestrial laser scanning (LR-TLS)

Reliable quantification of savanna vegetation structure is critical for accurate carbon accounting and biodiversity assessment under changing climate and land-use conditions. Inventories of fine-scale vegetation structural attributes are typically conducted from field-based plots or transects, while large-area monitoring relies on a combination of airborne and satellite remote sensing. Both of these approaches have their strengths and limitations, but terrestrial laser scanning (TLS) has emerged as the benchmark for vegetation structural parameterization – recording and quantifying 3D structural detail that is not possible from manual field-based or airborne/spaceborne methods. However, traditional TLS approaches suffer from similar spatial constraints as field-based inventories. Given their small areal coverage, standard TLS plots may fail to capture the heterogeneity of landscapes in which they are embedded. Here we test the potential of long-range (>2000 m) terrestrial laser scanning (LR-TLS) to provide rapid and robust assessment of savanna vegetation 3D structure at hillslope scales. We used LR-TLS to sample entire savanna hillslopes from topographic vantage points and collected coincident plot-scale (1 ha) TLS scans at increasing distances from the LR-TLS station. We merged multiple TLS scans at the plot scale to provide the reference structure, and evaluated how 3D metrics derived from LR-TLS deviated from this baseline with increasing distance. Our results show that despite diluted point density and increased beam divergence with distance, LR-TLS can reliably characterize tree height (RMSE = 0.25–1.45 m) and canopy cover (RMSE = 5.67–15.91%) at distances of up to 500 m in open savanna woodlands. When aggregated to the same sampling grain as leading spaceborne vegetation products (10–30 m), our findings show potential for LR-TLS to play a key role in constraining satellite-based structural estimates in savannas over larger areas than traditional TLS sampling can provide.     

Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns

To support the adoption of precision agricultural practices in horticultural tree crops, prior research has investigated the relationship between crop vigour (height, canopy density, health) as measured by remote sensing technologies, to fruit quality, yield and pruning requirements. However, few studies have compared the accuracy of different remote sensing technologies for the estimation of tree height. In this study, we evaluated the accuracy, flexibility, aerial coverage and limitations of five techniques to measure the height of two types of horticultural tree crops, mango and avocado trees. Canopy height estimates from Terrestrial Laser Scanning (TLS) were used as a reference dataset against height estimates from Airborne Laser Scanning (ALS) data, WorldView-3 (WV-3) stereo imagery, Unmanned Aerial Vehicle (UAV) based RGB and multi-spectral imagery, and field measurements. Overall, imagery obtained from the UAV platform were found to provide tree height measurement comparable to that from the TLS (R² = 0.89, RMSE = 0.19 m and rRMSE = 5.37 % for mango trees; R² = 0.81, RMSE = 0.42 m and rRMSE = 4.75 % for avocado trees), although coverage area is limited to 1–10 km² due to battery life and line-of-sight flight regulations. The ALS data also achieved reasonable accuracy for both mango and avocado trees (R² = 0.67, RMSE = 0.24 m and rRMSE = 7.39 % for mango trees; R² = 0.63, RMSE = 0.43 m and rRMSE = 5.04 % for avocado trees), providing both optimal point density and flight altitude, and therefore offers an effective platform for large areas (10 km²–100 km²). However, cost and availability of ALS data is a consideration. WV-3 stereo imagery produced the lowest accuracies for both tree crops (R² = 0.50, RMSE = 0.84 m and rRMSE = 32.64 % for mango trees; R² = 0.45, RMSE = 0.74 m and rRMSE = 8.51 % for avocado trees) when compared to other remote sensing platforms, but may still present a viable option due to cost and commercial availability when large area coverage is required. This research provides industries and growers with valuable information on how to select the most appropriate approach and the optimal parameters for each remote sensing platform to assess canopy height for mango and avocado trees.