Tonghai Wu

Xi'an Jiaotong University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Nengcheng Chen

China University of Geosciences

Wenying Du

China-Japan Friendship Hospital

Lei Xu

China University of Geosciences

Hongchu Yu

Wuhan University of Technology

Yu Lv

China University of Geosciences

Zhengwei Li

Harbin Institute of Technology

Xiaochong Sui

Harbin University of Science and Technology

Shan Ji

Wuhan University of Technology

Sameer Bikram Shah

Scripps Research Institute

Qing Hu

Chinese Academy of Sciences

Cooperative Institutions

Scripps Research Institute

China University of Geosciences

Soochow University

Wuhan University

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Wuhan University of Technology

Capital Normal University

Beihang University

Harbin Institute of Technology

Chinese Academy of Sciences

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Flexible and air-stable n-type oleylamine/carbon nanotube hybrid yarns for high-performance wearable thermoelectric generators

Chemical Engineering Journal (2024)

Fei Yin Xiaogang Luo Xuejiao Wang Yunxia Liang Tonghai Wu

Thermoelectric generator

10.1016/j.cej.2024.155233

Cite

Citations (2)

Global Prediction of Flash Drought Using Machine Learning

Geophysical Research Letters (2024)

Lei Xu Xihao Zhang Tonghai Wu Hongchu Yu Wenying Du

Abstract Flash droughts are rapidly developing extreme weather events with sudden onset and quick intensification. Global prediction of flash droughts at sub‐seasonal time scales remains a great challenge. Current state‐of‐the‐art dynamic models subject to large errors and demonstrate low skills in global flash drought prediction. Here, we develop a machine learning‐based framework that uses meteorological forecasts as inputs to predict global root‐zone soil moisture and flash droughts from 1 day to 2 week lead times. The results indicate that 33% and 24% global flash drought onset and termination events can be correctly predicted by machine learning at 7 day lead time, versus 19% and 11% fractions by state‐of‐the‐art dynamic model. The developed machine learning model demonstrates substantial improvements over dynamic model in global soil moisture prediction, and thus enhances global flash drought forecasting skills in space and time. The presented framework may benefit global flash drought prediction and early warning at sub‐seasonal scales.

Flash flood

10.1029/2024gl111134

Cite

Citations (0)

53BP1 deficiency leads to hyperrecombination using break-induced replication (BIR)

Nature Communications (2024)

Sameer Bikram Shah Youhang Li Shibo Li Qing Hu Tonghai Wu

Replication

10.1038/s41467-024-52916-z

Cite

Citations (1)

Refill Friction Stir Spot Welding of a Graphene-Reinforced AA 6061 Aluminum Alloy