Dr. Abhirup Banerjee


Abhirup Banerjee is pursuing a doctoral degree in Theoretical Physics at University of Potsdam. He is working at Potsdam Institute of Climate Impact Research as a guest researcher as part of the DFG funded NatRiskChange project. In this project, he uses recurrence analysis to compare the recurrence properties of different potential drivers underlying the temporal changes of flood hazards. He is working on further methodological developments of this technique to extend its capabilities to study event-like data (extreme events), data with uncertainties, and spatio-temporal recurrences. Abhirup has a Master’s degree in Physics from Bharathidasan University, Trichy, India.


Potsdam Institute for Climate Impact Research (PIK)
T +49 (0)331 288 2621
P.O. Box 60 12 03
14412 Potsdam


Nonlinear dynamics and time series analysis, Recurrence analysis of extreme events, Climate network analysis, Machine learning 

  • Kemter, M.Fischer, M.Luna, L. V.Schönfeldt, E.Vogel, J.Banerjee, A., et al. (2021). Cascading hazards in the aftermath of Australia's 2019/2020 Black Summer wildfiresEarth's Future9, e2020EF001884. 
  • Banerjee, A., Goswami, B., Hirata, Y., Eroglu, D., Merz, B., Kurths, J., and Marwan, N.: Recurrence analysis of extreme event like data, Nonlin. Processes Geophysics
  • A. Agarwal, RK. Guntu, A. Banerjee, M.A. Gadhawe, N. Marwan: A complex network approach to study the extreme precipitation patterns in a river basin, Choas (accepted, 2021).
  • A. Banerjee, M. Kemter, B. Goswami, B. Merz, J. Kurths, N. Marwan:  Spatial coherence patterns of extreme winter precipitation in the United States (Submitted to Climate Dynamics).
  • A. Banerjee, A. Mishra, SK. Dana, C. Hens, J. Kurths, N. Marwan: A machine learning recipe for prediction of extreme events, (in preparation).

Teaching assistance at University of Potsdam, Germany

  • GEW-DAP02 – Nonlinear Data Analysis Concepts, WiSe 2019/2020
  • GEW-DAP02 – Nonlinear Data Analysis Concepts, WiSe 2020/2021