Yahoo奇摩 網頁搜尋

搜尋結果

  1. Dr. Jiali Wang is an atmospheric scientist in Environmental Science Division at Argonne. Dr. Wang received her Ph.D degree in atmospheric science in 2012, and has been working at Argonne since then. She also holds a fellowship member with NAISE at Northwestern University, as well as CASE at University of Chicago.

  2. Jiali Wang, Argonne atmospheric and Earth scientist. Predicted changes in the number of days and area with extremely high drought index, from historical period to late 21 st century. Shading indicates changes in the number of days; significant changes are hashed. Dots indicate that a grid cell has an index value greater than 600 in the future.

  3. 其他人也問了

  4. 2022年8月3日 · In a new study, a team of collaborators including Jiali Wang of DOE ’ s Argonne National Laboratory used high resolution regional model experiments to explore how lake surface temperatures may affect the climate of the Great Lakes region.

  5. 2019年11月12日 · Researchers develop low-cost models to predict how short-term and long-term changes in weather patterns affect the local scale — down to neighborhoods or specific critical infrastructure. Jiali Wang and Rao Kotamarthi were co-authors on the Geoscientific Model Development that focused on the planetary boundary layer.

  6. Impacts of Lake Surface Temperature on the Summer Climate Over the Great Lakes Region. Authors. Wang, Jiali; Xue, Pengfei; Pringle, William; Yang, Zhao; Qian, Yun. Abstract. The surface of the Great Lakes interacts with the atmosphere, influencing the weather and climate over the Great Lakes Region ( GLR ).

  7. 2015年1月26日 · Image courtesy Jiali Wang. Regional climate models (RCMs) are a standard tool for downscaling climate forecasts to finer spatial scales. The evaluation of RCMs against observational data is an important step in building confidence in the use of RCMs for future prediction.

  8. 2019年11月22日 · Alex Norton of Hyperion Research and Argonne’s Jiali Wang after her team received Hyperion's HPC Innovation Excellence Award for its climate data contribution to AT&T’s Climate Resiliency Visualization Tool. Winners were announced November 19, at the Supercomputing 19 conference in Denver. (Image by Hyperion Research.)