Deep learning methods on the application of climate field reconstruction
My research mainly focus on the climate field reconstruction/predictions by using deep learning methods. Global surface temperature reconstructions of past millenniums temperature variability offer insights into climate sensitivity and feedback mechanisms, and also provide a useful source of information about the variability and sensitivity of the global climate system. Systematic instrumental temperature records only extend back to the nineteenth century, our knowledge of temperatures in earlier times therefore mainly relies on indirect recorders of temperature information: temperature‐sensitive proxy records.
We will use the state-of-the-art deep learning methods to make the climatic variables reconstruction by using sparse observations in order to improve our knowledge of temperature variations over the past millenniums and better understanding of global climate change both in the past and future.
- 2012 - 2015 Shanghai Ocean Shipping Co., Ltd. China
- Since 2018 PhD student at the Institute of Coastal Research, Helmholtz-Zentrum Hereon
- 2015 - 2018 Master of Engineering, Traffic information engineering and control, Dalian Maritime University. Thesis title: Intelligent Optimization of Neural Network and Its Application in Tidal Level Forecast
- 2009 - 2012 Associate Degree in Navigation Technology, Qingdao Ocean Shipping Mariners College.
- Zhang, Z., Wagner, S., Klockmann, M., & Zorita, E. (2022): Evaluation of statistical climate reconstruction methods based on pseudoproxy experiments using linear and machine-learning methods. Clim. Past, 18, 2643–2668, doi:10.5194/cp-18-2643-2022
- Zhang, Z., Stanev, E.V., & Grayek, S. (2020): Reconstruction of the basin‐wide sea level variability in the north sea using coastal data and generative adversarial networks. J. Geophys. Res. Oceans. 125, e2020JC016402, doi:10.1029/2020JC016402
- Zhang Z G , Yin J C , Wang N N , et al. A precise tidal prediction mechanism base on the combination of the harmonic analysis and ANFIS model[J]. Acta Oceanologica Sinica, 2017, 36 (11):94-105, DOI: 10.1007/s13131-017-1140-x
- Zhang Z G , Yin J C , Liu C . A Modular Real-time Tidal Prediction Model based on Grey-GMDH Neural Network[J]. Applied Artificial Intelligence, 2018, 32(2):165-185. https://doi.org/10.1080/08839514.2018.1451220
- Zhang Z G , Yin J C , Wang N N , et al. Vessel traffic flow analysis and prediction by an improved PSO-BP mechanism based on AIS data[J]. Evolving Systems, 2018:1-11.https://doi.org/10.1007/s12530-018-9243-y