Xiangde Xu

Biographic Data

ID7992956
NAMEXiangde Xu
GIVEN NAMESXiangde
FAMILY NAMEXu
SIGNATUREXU X
AFFILIATIONSChinese Academy of Meteorological Sciences
ORCID0000-0003-0772-5234
VERIFIEDYes
TOTAL WORKS5
TOTAL CITATIONS0
AUTHOR COUNT5
EDITOR COUNT0
FIRST PUBLICATION YEAR2019
LATEST PUBLICATION YEAR2026
H-INDEX0

  • Impact of deep-inland typhoon track uncertainty on the 2023 record-breaking rainfall over North China

    Open AccessRui Chen, Hongxiong Xu et al.ARTICLEEnvironmental Research Letters2026

    Extreme rainfall associated with typhoons remains a persistent forecasting challenge, particularly in regions influenced by complex multiscale interactions. Although typhoon-induced precipitation has been extensively studied, the influence of forecast uncertainty on inland rainfall—particularly over complex terrain—remains insufficiently understood. In this study, we applied ensemble sensitivity analysis to European Centre for Medium-Range Weathe…

  • Exploring the integration of a global AI model with traditional data assimilation in weather forecasting

    Open AccessHongxiong Xu, Yihong Duan et al.ARTICLEEnvironmental Research Letters2024

    Recent advancements in artificial intelligence (AI) have profoundly transformed weather forecasting, challenging traditional reliance on numerical weather prediction (NWP) models. Despite notable progress, AI models still depend heavily on traditional NWP systems to generate analysis fields, a dependency that increases computational demands and might limit forecast accuracy. This study explored the integration of gridpoint statistical interpolati…

  • Improvement of disastrous extreme precipitation forecasting in North China by Pangu-weather AI-driven regional WRF model

    Open AccessHongxiong Xu, Yang Zhao et al.ARTICLEEnvironmental Research Letters2024

    In the realm of weather forecasting, the implementation of Artificial Intelligence (AI) represents a transformative approach. However, AI weather forecasting method still faces challenges in accurately predicting meso- and smaller-scale processes and failing to directly capture extreme precipitation due to regression algorithm’s nature, coarse resolution, and limitations in key variables like precipitation. Therefore, we propose a state-of-the-ar…

  • Evaluating AI’s capability to reflect physical mechanisms

    Open AccessHongxiong Xu, Yihong Duan et al.ARTICLEEnvironmental Research Letters2024

    Tropical cyclones not only induce extreme precipitation events but also exert indirect influences on precipitation, a factor often underestimated in forecasting. Traditionally, these influences are identified using numerical sensitivity experiments with numerical models like the Weather Research and Forecasting (WRF) model, which require substantial computational resources. This study investigates the potential of the Artificial intelligence (AI)…

  • Drivers of improved PM 2.5 air quality in China from 2013 to 2017

    Open AccessQiang Zhang, Yixuan Zheng et al.ARTICLEProceedings of the National…2019

    From 2013 to 2017, with the implementation of the toughest-ever clean air policy in China, significant declines in fine particle (PM 2.5 ) concentrations occurred nationwide. Here we estimate the drivers of the improved PM 2.5 air quality and the associated health benefits in China from 2013 to 2017 based on a measure-specific integrated evaluation approach, which combines a bottom-up emission inventory, a chemical transport model, and epidemiolo…