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The Hong Kong International Airport (HKIA) is among the busiest airports in the world, with total aircraft movement exceeding 400,000 in 2016. The Hong Kong Observatory (HKO), provider of aviation meteorological services to HKIA, has recently begun making the first sets of aircraft wake vortex observations at HKIA using short-range LIDARs. This paper briefly describes the preliminary observation results obtained from field measurements between 2014 and 2016, and discusses the way forward on the monitoring and prediction of wake vortex behaviour in Hong Kong.

Estimating the intensity of tropical cyclones (TCs) is usually involved as a critical step in studies on TC disaster warnings and prediction. Satellite cloud images (SCIs) are one of the most effective and preferable data sources for TC research. Despite the great achievements in various SCI-based studies, accurate and efficient estimation of TC intensity still remains a challenge. In recent years, machine learning (ML) techniques have gained fast development and shown significant potential in dealing with big data, particularly with images. This study focuses on the objective estimation of TC intensity based on SCIs via a comprehensive usage of some advanced deep learning (DL) techniques and smoothing methods. Two estimation strategies are proposed and examined which, respectively, involve one and two functional stages. The one-stage strategy uses Vision Transformer (ViT) or Deep Convolutional Neutral Network (DCNN) as the regression model for directly identifying TC intensity, while the second strategy involves a classification stage that aims to stratify SCI samples into a few intensity groups and a subsequent regression stage that specifies the TC intensity. Further efforts are made to improve the estimation accuracy by using smoothing manipulations (via four specific smoothing techniques) in the scenarios of the aforementioned two strategies and their fusion. Results show that DCNN performs better than ViT in the one-stage strategy, while using ViT as the classification model and DCNN as the regression model can result in the best performance in the two-stage strategy. It is interesting that although the strategy of singly using DCNN wins out over any concerned two-stage strategy, the fusion of the two strategies outperforms either the one-stage strategy or the two-stage strategy. Results also suggest that using smoothing techniques are beneficial for the improvement of estimation accuracy. Overall, the best performance is achieved by using a hybrid strategy that consists of the one-stage strategy, the two-stage strategy and smoothing manipulation. The associated RMSE and MAE values are 9.81 kt and 7.51 kt, which prevail over those from most existing studies. [ABSTRACT FROM AUTHOR]

Tropical Cyclones (TCs) are one of the most destructive natural disasters. For the prevention and mitigation of TC-induced disasters, real-time monitoring and prediction of TCs is essential. At present, satellite cloud images (SCIs) are utilized widely as a basic data source for such studies. Although great achievements have been made in this field, lack of concerns on the identification of TC fingerprint from SCIs have become a potential issue, since it is a prerequisite step for follow-up analyses. This paper presents a methodology which identifies TC fingerprint via Deep Convolutional Neural Network (DCNN) techniques based on SCIs of more than 200 TCs over the Northwest Pacific basin. Two DCNN models have been proposed and validated, which are able to identify the TCs from not only single-TC featured SCIs but also multi-TCs featured SCIs. Results show that both models can reach 96 % of identification accuracy. As the TC intensity strengthens, the accuracy becomes better. To explore how these models work, heat maps are further extracted and analyzed. Results show that all the fingerprint features are focused on clouds during the testing process. For the majority of TC images, the cloud features in TC’s main parts, i.e., eye, eyewall and primary rainbands, are most emphasized, reflecting a consistent pattern with the subjective method. [ABSTRACT FROM AUTHOR]

Located in the Southern China monsoon region, pollution days in Pearl River Delta (PRD) were classified into “Western type”, “Central type” or “Eastern type”, with a relative percentage of 67%, 24% and 9%, respectively. Using this classification system, three typical pollution events were selected for numerical simulations using the WRF-Chem model. The source sensitivity method for anthropogenic emissions of PM 2.5 and its precursors was applied to identify the source-receptor relationships for PM 2.5 among 9 cities in PRD. For “Western type” case, the PRD region was under control of a high-pressure system with easterly prevailing winds. The PM 2.5 concentrations in the western PRD region were higher than those in the eastern region, with emissions from cities in the eastern PRD region having higher contributions. Within the PRD's urban cluster, PM 2.5 in Huizhou, Dongguan and Shenzhen was mainly derived from local emissions, whereas the PM 2.5 in the other cities was primarily derived from external transport. For “Eastern type” case, the PRD was influenced by Typhoon Soulik with westerly prevailing winds. Emissions from cities in the western PRD region had the highest impacts on the overall PM 2.5 concentration. PM 2.5 in Jiangmen and Foshan was primarily derived from local emissions. Regarding “Central type” case, the PRD region was under control of a uniform pressure field with low wind speed. PM 2.5 concentrations of each city were primarily caused by local emissions. Overall, wind flows played a significant role in the transport and spatial distribution of PM 2.5 across the PRD region. Ideally, local governments would be wise to establish joint prevention and control measures to reduce regional atmospheric pollution, especially for “Western type” pollution. [ABSTRACT FROM AUTHOR]