Analysis of Determinants for an Enhanced and Long-lasting Coastal Convective System by Means of a Case Study(26 July 2011)

A precipitation system developed continuously along the western coastline of the Korean Peninsula and created considerable precipitation both along the coast and inland on 26 July 2011. In this study, the causes for this nearshore convective system are investigated from observations and the results of model experiments. Three-dimensional radar fields clearly show that a change of wind at the surface border played an important role in the development of the nearshore convection system. The simulation results, which are very similar to the observations, show that the surface border generated and maintained the convergence zone. The roughness change enhanced the convergence, and the interaction between the deepening cold pool and downward flow maintained the convergence zone. The surface mechanical discontinuity affected by the roughness change between sea and land formed the convergence (gradient of wind stress), which induced momentum transfer to the upper layer. The cold pool created a steep gradient of potential temperature and provided the reason for the propagated convergence zone with the downward flow. The maximum value of the surface change factor, which comprises the influencing factors for the long-lasting convective system, reflects the enhancement of the system at the coast.

Discovering High-Quality Threaded Discussions in Online Forums

Archives of threaded discussions generated by users in online forums and discussion boards contain valuable knowledge on various topics. However, not all threads are useful because of deliberate abuses, such as trolling and flaming, that are commonly observed in online conversations. The existence of various users with different levels of expertise also makes it difficult to assume that every discussion thread stored online contains high-quality contents. Although finding high-quality threads automatically can help both users and search engines sift through a huge amount of thread archives and make use of these potentially useful resources effectively, no previous work to our knowledge has performed a study on such task. In this paper, we propose an automatic method for distinguishing high-quality threads from low-quality ones in online discussion sites. We first suggest four different artificial measures for inducing overall quality of a thread based on ratings of its posts. We then propose two tasks involving prediction of thread quality without using post rating information. We adopt a popular machine learning framework to solve the two prediction tasks. Experimental results on a real world forum archive demonstrate that our method can significantly improve the prediction performance across all four measures of thread quality on both tasks. We also compare how different types of features derived from various aspects of threads contribute to the overall performance and investigate key features that play a crucial role in discovering high-quality threads in online discussion sites.