In this study, a deep learning method named U-net neural network is utilized to calibrate the gridded forecast of surface air temperature from the Global Ensemble Forecasting System (GEFS), with forecast lead times of 1–7 days in Xinjiang. The calibration performance of U-net is compared with three conventional postprocessing methods: unary linear regression (ULR), the decaying averaging method (DAM) and Quantile Mapping (QM). Results show that biases of the raw GEFS forecasts are mainly distributed in the Altai Mountains, the Junggar Basin, the Tarim Basin and the Kunlun Mountains. The four postprocessing methods effectively improve the forecast skills for all lead times, whereas U-net shows the best correction performance with the lowest mean absolute error (MAE) and the highest hit rate of 2°C (HR2) and pattern correlation coefficient (PCC). The U-net model considerably reduces the warm biases of the raw forecasts. The skill improvement magnitudes are greater in southern than northern Xinjiang, showing a higher mean absolute error skill score (MAESS). Furthermore, in order to distinguish the error sources of each forecasting scheme and to reveal their capabilities of calibrating errors of different sources, the error decomposition analysis is carried out based on the mean square errors. It shows that the bias term is the leading source of error in the raw forecasts, and barely changes as the lead time increases, which is mainly distributed in Tarim Basin and Kunlun Mountains. All four forecast calibrations effectively reduce the bias and distribution error of the raw forecasts, but only the U-net significantly reduces the sequence error.

Against the backdrop of intensified global warming, extreme weather events such as dense fog, low visibility, heavy precipitation, and extreme temperatures have been increased and enhanced to a great extent. They are likely to pose severe threats to the operation of urban transportation and associated services, which has drawn much attention in recent decades. However, there are still plenty of issues to be resolved in improving the emergency meteorological services and developing targeted urban transportation meteorological services in modern cities. The present review briefly illustrates the current cutting-edge developments and trends in the field of urban transportation meteorology in China, including the establishment of observation networks and experiments and the development of early warning and prediction technologies, as well as the related meteorological commercial services. Meanwhile, reflections and discussions are provided in terms of the state-of-the-art observation channels and methods and the application of numerical model forecasts and artificial intelligence. With the advantages of various advanced technologies from multiple aspects, researchers could further expand explorations on urban transportation meteorological observations, forecasts, early warnings, and services. Associated theoretical studies and practical investigations are also to be carried out to provide solid scientific foundations for urban transportation disaster prevention and mitigation, for implementing the action of meteorological guarantees, and for the construction of a high-quality smart society.