Mesoscale numerical simulation study of warm fog dissipation by salt particles seeding

Based on the dynamic framework of WRF and Morrison 2-moment explicit cloud scheme, a salt-seeding scheme was developed and used to simulate the dissipation of a warm fog event during 6–7 November 2009 in the Beijing and Tianjin area. The seeding effect and its physical mechanism were studied. The results indicate that when seeding fog with salt particles sized 80 μm and at a quantity of 6 gm~(-2) at the fog top, the seeding effect near the ground surface layer is negative in the beginning period, and then a positive seeding effect begins to appear at 18 min, with the best effect appearing at 21 min after seeding operation. The positive effect can last about 35 min. The microphysical mechanism of the warm fog dissipation is because of the evaporation due to the water vapor condensation on the salt particles and coalescence with salt particles.The process of fog water coalescence with salt particles contributed mostly to this warm fog dissipation. Furthermore, two series of sensitivity experiments were performed to study the seeding effect under different seeding amounts and salt particles sizes. The results show that seeding fog with salt particles sized of 80 μm can have the best seeding effect, and the seeding effect is negative when the salt particle size is less than 10 μm. For salt particles sized 80 μm, the best seeding effect, with corresponding visibility of 380 m, can be achieved when the seeding amount is 30 g m~(-2).     

The impact of deformation on vortex development in a baroclinic moist atmosphere

A mathematical relation between deformation and vertical vorticity tendency is built by introducing the frontogenesis function and the complete vertical vorticity equation, which is derived by virtue of moist potential vorticity. From the mathematical relation, it is shown that properly configured atmospheric conditions can make deformation exert a positive contribution to vortex development at rates comparable to other favorable factors. The effect of deformation on vortex development is not only related to the deformation itself, but also depends on the current thermodynamic and dynamic structures of the atmosphere, such as the convective stability, moist baroclinicity and vertical wind shear(or horizontal vorticity). A diagnostic study of a heavy-rainfall case that occurred during 20–22 July 2012 shows that deformation has the most remarkable effect on the increase in vertical vorticity during the rapid development stage of the low vortex during its whole life cycle. This feature is mainly due to the existence of an approximate neutral layer(about 700 h Pa) in the atmosphere where the convective stability tends to be zero. The neutral layer makes the effect of deformation on the vertical vorticity increase significantly during the vortex development stage, and thus drives the vertical vorticity to increase.     

The mesoscale balance and imbalance and the corresponding potential vorticity inversion from the view of helicity

The static balance and the geostrophic balance are the common balances in meteorology.All the synoptic systems and most of the mesoscale systems satisfy the above two balances.However,due to the strong convection and non-geostrophic feature,many mesoscale systems usually present as static imbalance,and the quasi-geostrophic approximation is no longer attainable.This paper tried to find out a kind of balance that exists for mesoscale convective system.To do this,the concrete mathematics definitions for balance and imbalance equations were defined.Then,it is proposed that the new balance equation should include the divergence,vorticity,and vertical motion simultaneously,and the helicity equation was a good choice for the basis.Finally,the mesoscale balance and imbalance equations were constructed,as well as a new balance model that was based on the helicity,horizontal divergence,vertical vorticity,continuity,and thermal dynamic equations under same approximations.Moreover,the corresponding potential vorticity(PV)inversion technique was introduced.It was pointed out that by using the PV conservation and the potential temperature conservation,the flows of the mesoscale balance model can be deduced,and their comparison with the real fields would give the degree of the imbalance.     

南海海岛自动气象站防护应用技术探索

立足海南及南海的自然条件,结合海岛自动气象站在南海高温、高湿、高盐雾、强辐射、强风等恶劣气候环境下使用情况,通过对历年来海岛自动气象站故障及失效模式的统计和分析,总结分析故障原因。根据设备维护人员多年气象设备保障工作积累总结的经验,从海岛自动气象站结构和防护要求出发,集成应用市场上优质材料、先进工艺和多种成熟、关键设备防护技术,形成一些气象设备防护的有效方法措施,以提高海岛自动气象站对海洋环境的适应性和性能,延长设备使用寿命。     

台湾海峡西岸海雾研究现状与未来发展方向

台湾海峡是一个海雾多发的交通要道,海雾灾害往往会导致人员和财产损失。对当前有关的海雾研究现状进行总结,以海峡西岸为代表,比较台湾海峡海雾和其他海域的大雾研究进展状况,主要包括海雾发生时的天气气候学特征、监测手段、微物理特征研究和海雾数值预报研究等。结果表明,台湾海峡西岸在天气气候学方面已经取得了一定的成果;监测手段随着科学技术的发展,也不断的完善,但在如何对卫星遥感监测结果进行反演方面还需进一步研究;海雾的微物理特征研究有助于海雾模式的改进和卫星遥感海雾反演技术的提高,但台湾海峡西岸尚未开始研究,是下一步的研究重点;台湾海峡海雾数值模拟工作较少,伴随观测技术的不断进步、动力统计方法的不断完善,可通过数值预报产品与传统的天气学方法相结合,提高台湾海峡海雾预报水平。     

Validation of MODIS Vegetation Continuous Fields for monitoring deforestation and forest degradation: two cases in Mexico

This study assesses whether MODIS Vegetation Continuous Fields percent tree cover (PTC) data can detect deforestation and forest degradation. To assess the usefulness of PTC for detecting deforestation, we used a data set consisting of eight forest and seven non-forest categories. To evaluate forest degradation, we used data from two temperate forest types in three conservation states: primary (dense), secondary (moderately degraded) and open (heavily degraded) forest. Our results show that PTC can differentiate temperate forest from non-forest categories (p = 0.05) and thus suggests PTC can adequately detect deforestation in temperate forests. In contrast, single-date PTC data does not appear to be adequate to detect forest degradation in temperate forests. As for tropical forest, PTC can partially discriminate between forest and non-forest categories.     

An integrated land vehicle navigation system based on context awareness

In the complex urban environments, land vehicle navigation purely relying on GNSS cannot satisfy user needs due to the loss of satellite signals caused by obstructions such as buildings, tunnels, and trees. To solve this problem, we introduce a GPS-/MSINS-/magnetometer-integrated urban navigation system based on context awareness. In this system, the data from the Micro Strapdown Inertial Navigation System (MSINS) are used to analyze and detect the context knowledge of vehicles, whose sensor errors can be compensated by the heuristic drift reduction algorithm for different motion situations. When GPS is available, the vehicle position can be estimated by unscented Kalman Filter, whereas in the case of GPS outages, the vehicle attitude is provided by an attitude and heading reference system and the motion constraints-aided algorithm is used to complete the positioning. In the experiment validation, the integrated navigation system is set up by low-cost inertial sensors. The result shows that the proposed system can achieve high accuracy when GPS is available. For most of the time without GPS, the system can guarantee the positioning precision of 10 m and compensate the errors of MSINS effectively, which fully satisfies positioning needs in complex urban environments.     

Grid pattern recognition in road networks using the C4.5 algorithm

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.