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Data Assimilation, Bayesian Methods & Uncertainty Quantification

Distinct from any one application domain, this pillar is the lab’s methodological core: non-Gaussian Bayesian filtering with polynomial chaos expansions, ensemble-based data assimilation, and uncertainty quantification. It underpins the ocean and monsoon prediction pillars, and produced the lab’s most-cited single paper — a spatio-temporal predictive modeling framework for infectious disease spread, applied to COVID-19 case forecasting and policy interventions in India.

Publications in this pillar

1. A non-Gaussian Bayesian Filter for Sequential Data Assimilation with non-intrusive Polynomial Chaos Expansion

   S. Avasarala, D. N. Subramani · International Journal for Numerical Methods in Engineering, 122(23), 7156–7181 · 2021

2. Spatio-temporal predictive modeling framework for infectious disease spread

   S. Ganesan, D. N. Subramani · Scientific Reports, 11, 6741 · 2021

3. Ensemble Forecast of COVID-19 for Vulnerability Assessment and Policy Interventions

   Ganesan, S., Subramani, D., Anandh, T., Ghose, D., Babu, G. · MedRxiv Pre-print · 2021

4. A new ensemble-based data assimilation algorithm to improve track prediction of tropical cyclones

   Subramani, D., Chandrasekar, R., Ramanujam, K.S., C. Balaji · Natural Hazards, 71, 659-682 · 2014

5. On the Effect of Non-Raining Parameters in Retrieval of Surface Rain Rate Using TRMM PR and TMI Measurements

   S. Ramanujam, C. Radhakrishnan, D. Subramani, C. Balaji · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(3), 735-743 · 2012

All Data Assimilation, Bayesian Methods & Uncertainty Quantification publications on the Publications page →