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Forecasting tropical cyclone track and intensity is a great challenge for the meteorological community, and safeguarding the life and property of people living near the coast is an important issue. One major reason for challenging forecasts is the lack of observations over the vast oceans. During tropical cyclone Mulan between 8 and 10 August 2022 over the northern part of the South China Sea, the meteorological authority and research institutes of Chinese mainland collaborated with the meteorological service in Hong Kong on conducting the first-ever ground-space-sky observing system experiment on tropical cyclone Mulan. The enhanced targeted observations collected during the experiment include Geostationary Interferometric Infrared Sounder, round-trip radiosondes, and aircraft-launched dropsondes. This paper describes the campaign, technical details of the meteorological models used, and impact of the additional targeted observation data on the tropical cyclone forecast. Ideally, similar enhanced observation campaigns could be conducted in the future, not only in the northern part of the South China Sea, but also in other ocean basins. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) effect is one of the typical characteristics of urban climate, which makes a lot of harmful gases accumulate and cannot be diffused. The UHI effect will cause air pollution, affecting the health and living conditions of humans. In this paper, a new method for calculating UHI intensity (UHII) by using the Global Navigation Satellite Systems (GNSS) derived zenith tropospheric delay (GNSS-ZTD) dataset and atmospheric empirical model was proposed and tested by using UHII observations from ground meteorological sensors for monitoring UHII in the Hong Kong city. The method requires accurate GNSS-ZTD datasets, priori atmospheric parameters (i.e. ground pressure and water vapour partial pressure) and the coefficient of the refractive function as inputs. The GNSS-ZTD datasets were obtained by using observations from the Hong Kong GNSS continuously operating reference stations network, and priori atmospheric parameters were obtained from a prior atmospheric model based on ERA5 (fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting) dataset and the coefficient of the refractive function can be obtained from the historical radiosondes data. The ground refractivity, pressure and water vapour partial pressure can be estimated by using the above-mentioned datasets, then the time series of UHII can be obtained by using a refractivity model proposed by Smith and Weintraub (SW-UHII), or Thayer (Thayer-UHII model). The results showed that the accuracy of the SW-UHII is better than the accuracy of the Thayer-UHII, and the root mean square (RMS) of the SW-UHII is 14% lower than the RMS of the Thayer-UHII. The mean bias, RMS and standard deviation of the SW-UHII compared with meteorological data derived UHII over GNSS stations are 1.82°C, 1.9°C and 1.41°C in all seasons, respectively, and the largest RMS is in winter (day of year 1–61 and 331–365). All the above results indicated that the GNSS technique has a certain prospect for monitoring UHII. [ABSTRACT FROM AUTHOR]

• A multi-model and multi-method ensemble approach for wind resource projection is proposed. • Future wind potential in Hong Kong is assessed for the first time. • Robustness of the climate change signal is evaluated. • An increase in wind power density during summer (+23.7 %) and a decrease during autumn (–15.1 %) in 2081–2100 are projected for SSP5-8.5. Harvesting renewable wind energy is among the most cost-efficient means of reducing carbon emissions and achieving carbon neutrality. However, wind resource is susceptible to climate change impacts since the global temperature increase will reshape atmospheric circulation patterns. To facilitate the evaluation of fine-scale wind energy potential under climate variability, this paper proposes and validates a multi-model and multi-method ensemble wind resource projection approach. Then this approach is utilized to investigate the future variation of wind resources in Hong Kong based on the combined use of global climate models from Coupled Model Intercomparison Project Phase 6 and long-term observations from meteorological stations. It is found that there is a significant increase in future wind resources during summer, while a remarkable decline is projected during autumn. Nevertheless, the variations of wind resources in winter and spring are relatively insignificant. The outcomes of this study are expected to offer a framework for fine-scale wind resource assessment under climate change, and facilitate the economic and risk assessments of future wind farm projects. [ABSTRACT FROM AUTHOR]