讲座简介：

Global coupled land–atmosphere snow data assimilation (DA) experiments were conducted using the global workflow with the Noah-MP land surface model for both a retrospective period (2021–2022) and a near-real-time period (2024–2025). The snow2dvar system assimilated multisource snow depth observations from the Global Telecommunication System (GTS), MADIS, European national networks, and IMS-derived products. Both control and snow2dvar experiments were performed as 6-hourly cycling coupled simulations. The performance of the experiments was evaluated against independent datasets, including ERA5, Crocus-ERA5, SNODAS, and University of Arizona (UA) products, across 14 major river basins in Asia, Europe, Russia, and North America. Results show that snow2dvar generally improves simulated snow water equivalent (SWE) and snow depth compared with the control experiment, with consistent improvements in correlation, unbiased RMSE, and Taylor Skill Score metrics. The largest improvements occur in snow-dominated regions, although localized degradations remain. These results demonstrate the capability of the JEDI-based global snow DA framework to improve snow initialization and provide a foundation for advancing near-real-time land DA applications in support of the next-generation Global Forecast System (GFS). Future work will focus on diagnosing regional degradations, refining model physics and parameterizations, improving station-based quality control, and incorporating additional observation sources, including satellite-derived snow depth and SWE products. These developments will further advance operational global land DA systems and improve coupled land–atmosphere prediction capabilities.

主讲人简介：

Prof. Youlong Xia is an internationally recognized scientist specializing in land surface modeling, land data assimilation, and numerical weather and climate prediction. He received his B.S. in Meteorology from Nanjing Institute of Meteorology, his M.S. in Atmospheric Dynamics from the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, and his Ph.D. in Applied Meteorology from Ludwig Maximilian University of Munich, Germany. He has held research positions at Beijing Meteorological College in China, the University of Munich in Germany, Macquarie University in Australia, the University of Texas at Austin, Princeton University, and NOAA’s Geophysical Fluid Dynamics Laboratory in the United States. From 2006 to 2025, he served as a Principal Scientist at NOAA’s National Centers for Environmental Prediction (NCEP), where he led the development, evaluation, and operational implementation of the North American and Global Land Data Assimilation Systems (NLDAS and GLDAS). He successfully developed and transitioned NLDAS and the GFS-focused GLDAS system into NCEP operational forecasting systems, including implementation within GFS version 16. His research spans land surface modeling, data assimilation, drought monitoring and prediction, snow hydrology, and land–atmosphere interactions. Dr. Xia has authored more than 100 peer-reviewed publications and has made significant contributions to improving weather, climate, and hydrological prediction through advanced land observations, modeling, and data assimilation.

腾讯会议ID：713-432-730