Topic: Non-Gaussian Data Assimilation
Friday, January 20, 2023 from 7-9 UTC
Organisers and Conveners: Steven Fletcher (CIRA/CSU, US), James Taylor (RIKEN, Japan)
One of the underlying assumptions underpinning many data assimilation schemes (including variational, Kalman filter, or ensemble-based) is that the background, observational and model errors are Gaussian in distribution. However, this assumption is often false, with errors taking a non-Gaussian distribution. In this session, we invite all contributions to the development of non-Gaussian DA.
Program / Abstracts (PDF Download)
Questions asked during the event (Yopad link)
Session recordings (RIKEN website)
Presentations:
- A Quantile Conserving Ensemble Filtering Framework: Regressing Probit-Transformed Quantile Increments to Update Unobserved Variables
Jeff ANDERSON - Revisiting and Repurposing the Gaussian Anamorphosis EnKF
Hristo G. CHIPILSKI, Ian Grooms, Mohamad El Gharamti, Jeffrey Anderson, Ricardo Baptista - A two-step nonlinear non-Gaussian framework for data assimilation applied to assimilation of wind direction observations
Ian GROOMS - Improving vortex position accuracy with a new multiscale alignment ensemble filter
Yue (Michael) YING, Jeffrey Anderson, Laurent Bertino - "Ensemblized" linear least squares (LLS)
Patrick N. RAANES
Time Zones:
07 – 09 UTC
Europe: 07 – 09 am GMT (London) | 08 – 10 am CET (Berlin)
Asia/Australia: 03 – 05 pm CST (Shanghai) | 04 – 06 pm JST (Tokyo) | 06 – 08 pm AEDT (Sydney)
Americas: 11pm – 01 am PST (San Fran.) | 00 – 02 am MST (Denver) | 02 – 04 am EST (New York)
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