HISTORICAL GIS DATA AND CHANGES IN URBAN MORPHOLOGICAL PARAMETERS FOR THE ANALYSIS OF URBAN HEAT ISLANDS IN HONG KONG

Offshore wind turbines are sensitive to tropical cyclones (TCs). Wind data from Super Typhoons Mangkhut and Saola, impacting South China, are vital for design and operation. Despite Saola's higher intensity, it caused less damage. Both had concentric eyewall structures, but Saola completed an eyewall replacement before landfall, becoming more compact. Mangkhut decayed but affected a wider area. Their wind characteristics provide insights for turbine maintenance and operation.

Adjustments for the wind-induced bias of traditional rainfall gauges are applied to data from the Hong Kong Observatory using numerical simulation results. An optical disdrometer allows us to infer the collection efficiency of the rainfall gauge. Due to the local climatology, adjustments are limited but result in a significant amount of available freshwater resources that would be missing from the calculated hydrological budget of the region should the adjustments be neglected.

A trans-scale simulation framework integrating the open-source WRF and OpenFOAM codes is proposed to numerically simulate the urban wind environment under specific weather conditions.

In recent years, there has been numerous research on tropical cyclone (TC) observation based on satellite cloud images (SCIs), but most methods are limited by low efficiency and subjectivity. To overcome subjectivity and improve efficiency of traditional methods, this paper uses deep learning technology to do further research on fingerprint identification of TCs. Results provide an automatic and objective method to distinguish TCs from SCIs and are convenient for subsequent research.

The COVID-19 induced lockdown provided a time-window to study the impact of emission decrease on atmospheric environment. A 350 m meteorological tower in the Pearl River Delta recorded the vertical distribution of pollutants during the lockdown period. The observation confirmed that an extreme emission reduction, can reduce the concentrations of fine particles and the peak concentration of ozone at the same time, which had been taken as difficult to realize in the past in many regions.