Bedartha Goswami
Principal Investigator
- bedartha.goswami@iiserpune.ac.in
- Office
- Room 515
- Phone
- +91 20 2590 8716
- ORCID
- 0000-0002-2302-166X
- Scholar
- Google Scholar
- GitHub
- github.com/bedartha
- IISER Pune
- Faculty page
- linkedin.com/in/bedartha-goswami
- CV
Research interests
Machine learning for medium-range and subseasonal weather forecasting, with a focus on India and South Asia. Interested in trustworthy, resource-efficient, and explainable models — see the research page for more.
Publications
Preprints
2024
- A hybrid deep-learning model for El Niño Southern Oscillation in the low-data regime. arXiv:2412.03743. arXiv
2023
- Inductive biases in deep learning models for weather prediction. arXiv:2304.04664. arXiv
Peer-reviewed journal articles
2025
- Intraseasonal synchronization of extreme rainfall between North India and the Sahel. Quarterly Journal of the Royal Meteorological Society 151(769), e4946. DOI
2024
- Frequency bias causes overestimation of climate change impacts on global flood occurrence. Geophysical Research Letters. DOI
- Contribution of El Niño Southern Oscillation (ENSO) diversity to low-frequency changes in ENSO variance. Geophysical Research Letters. DOI
2023
- Propagation pathways of Indo-Pacific rainfall extremes are modulated by Pacific sea surface temperatures. Nature Communications. DOI
2022
- Teleconnection patterns of different El Niño types revealed by climate network curvature. Geophysical Research Letters. DOI
2021
- Recurrence analysis of extreme event-like data. Nonlinear Processes in Geophysics. DOI
2020
- Fingerprint of volcanic forcing on the ENSO–Indian monsoon coupling. Science Advances. DOI
2019
- A brief introduction to nonlinear time series analysis and recurrence plots. Vibration. DOI
- A network-based flow accumulation algorithm for point clouds: facet-flow networks (FFNs). Journal of Geophysical Research: Earth Surface. DOI
- Holocene interaction of maritime and continental climate in Central Europe: new speleothem evidence from Central Germany. Global and Planetary Change. DOI
- Complex networks reveal global pattern of extreme-rainfall teleconnections. Nature. DOI
2018
- Abrupt transitions in time series with uncertainties. Nature Communications. DOI
2017
- Climatic and in-cave influences on δ18O and δ13C in a stalagmite from northeastern India through the last deglaciation. Quaternary Research. DOI
- A complete representation of uncertainties in layer-counted paleoclimatic archives. Climate of the Past. DOI
- Recurrence measure of conditional dependence and applications. Physical Review E. DOI
- Tropical rainfall over the last two millennia: evidence for a low-latitude hydrologic seesaw. Scientific Reports. DOI
2016
- Hydrological and climatological controls on radiocarbon concentrations in a tropical stalagmite. Geochimica et Cosmochimica Acta. DOI
- The size distribution of spatiotemporal extreme rainfall clusters around the globe. Geophysical Research Letters. DOI
2015
- A possible mechanism for the attainment of out-of-phase periodic dynamics in two chaotic subpopulations coupled at low dispersal rate. Journal of Theoretical Biology. DOI
- A random interacting network model for complex networks. Scientific Reports. DOI
2014
- Effects of symmetric and asymmetric dispersal on the dynamics of heterogeneous metapopulations: two-patch systems revisited. Journal of Theoretical Biology. DOI
- Estimation of sedimentary proxy records together with associated uncertainty. Nonlinear Processes in Geophysics. DOI
2013
- How do global temperature drivers influence each other? A network perspective using recurrences. The European Physical Journal Special Topics. DOI
2012
- COnstructing Proxy Records from Age models (COPRA). Climate of the Past. DOI
- On interrelations of recurrences and connectivity trends between stock indices. Physica A: Statistical Mechanics and its Applications. DOI
Book chapters
2015
- Teleconnections in climate networks: a network-of-networks approach to investigate the influence of sea surface temperature variability on monsoon systems. In Machine Learning and Data Mining Approaches to Climate Science. DOI