Development of Real-time Urban-scale Weather Monitoring Network and Experimental Hourly Temperature Forecasts in Hong Kong

The high-density building environment and complex, heterogeneous landscape in Hong Kong result in significant spatial variability of local temperatures, winds and other weather elements down to the street level.  To enhance meteorological observation and forecasting services, the Hong Kong Observatory (HKO) has been actively developing an urban-scale weather observing network in recent years through in-house efforts and collaboration with local communities.  In this presentation, a novel real-time monitoring network, based on economical sensors deployed at various sites within the urban areas, providing basic temperature and relative humidity measurements will be introduced.  They are found to provide useful data for analyses of daily temperature variations in urban areas under very hot or prolonged heat stress episodes.  The new observations offer indispensable meteorological information and reference for studies of urban micro-climate and the impact of heat stress on public health or activities.  The data of the urban-scale weather monitoring network are then utilized to generate location-specific hourly forecasts of air temperatures and relative humidity for the next nine days through post-processing of downscaled forecasts from the global NWP models.  It is found that the mean absolute errors of daily minimum and maximum temperature forecasts at the urban-scale monitoring network stations are generally within 2 degrees Celsius in the next 5 days.   The real-time forecast products have been launched to public since mid-2022 on HKO’s Automatic Regional Weather Forecast web portal.   The automatic urban-scale forecast shows useful information for the public and serves as potential early alert for communities to get prepared for potential heat stress brought by the very hot weather in the next few days.  The methodology and recent development of the forecast products will be presented, including artificial intelligence technique to consider different meteorological factors for improving forecast of daily minimum and maximum temperatures, as well as machine learning methods to generate urban-scale forecasts under different weather regimes.