Topic: Math and Methods
Thursday, 15 January 2026 from 15-17UTC
Organizers and Conveners: Robin Armstrong (Cornell University), Javier Amezcua (U. Iberoamericana, Mexico; U. Reading, UK)
This session is on the fundamental ideas of data assimilation. We invite abstracts dealing with recent advancements in the methodology of data assimilation, as well as the mathematical developments behind these. Traditional methods (variational and Kalman-filter-based) and novel methods (non-parametric filters, particle filters and flows, optimal transport) are encompassed.
Abstracts/Program (PDF download)
Session Recordings (RIKEN website)
Questions asked during the event (Yopad link)
Presentations [each 20 minutes]:
Efficient continuous nonlinear data assimilation
Peter Jan van Leeuwen- Inaccuracy of ensemble-based covariance propagation, beyond sampling error
Shay Gilpin - Parametric error forecasts for operational air quality assimilation under highly uncertain emissions
Annika Vogel, Richard Menard, James Abu, Jack Chen - A novel solution for multivariate updates to state quantities constrained by summation: theory and application to sea ice
Molly M. Wieringa, Jeffrey L. Anderson - Ensemble Conjugate Transform Inversion (ECTI): A transport-based framework for estimating uncertain model parameters
Hristo G. Chipilski, Derek J. Posselt
Time Zones:
15 – 17 UTC
Europe: 03 – 05 pm GMT (London) | 04 – 06 pm CET (Berlin)
Asia/Australia: 11 – 01 am CST (Shanghai) | 00 – 02 am JST (Tokyo) | 02 – 04 am AEDT (Sydney)
Americas: 07 – 09 am PST (San Fran.) | 08 – 10 am MST (Denver) | 10 – 12 am EST (New York)
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