Topic: Advancements in Ensemble Data Assimilation

Friday, March 08, 2024 from 15-17 UTC

Organisers and Conveners: Hristo Chipilski (Florida State University, US), Laura Slivinsky (CIRES; NOAA SPL, US), Javier Amezcua (Tec. de Monterrey, Mexico; U. Reading, UK)

Ensemble forecasting has become an essential tool for prediction in many fields. This practice readily equips predictions with a measure of uncertainty, and with statistical relationships amongst variables which can be used by data assimilation methods. However, statistics coming from raw ‘small’ ensembles often contain sampling errors, leading to the need for extra steps before using them. The most traditional method in this area is the ensemble Kalman filter, but recent advances go far beyond its original formulation. We encourage submissions on the following areas: challenging problems in localisation and inflation (e.g. time-dependent localisation), extensions beyond the Kalman filter (e.g. filters that use alternatives to linear regression), fully non-linear ensemble methods, multi-scale ensemble data assimilation, data-driven ensemble methods, etc.

Program / Abstracs (PDF Download)

Presentations:

  • Filtering Dynamical Systems Using Observations of Statistics
    Eviatar Bach, Tim Colonius, Isabel Scherl, Andrew Stuart
  • Unbiased fully nonlinear data assimilation: the Stochastic Particle Flow Filter
    Hao-Lun Yeh, Peter Jan van Leeuwen
  • DA for multi-time and multi-scale models of the turbulent energy cascade
    Vikrant Gupta, Minping Wan, Francesco Fossella, Luca Biferale, Massimo Cencini, Alberto Carrassi, Chunxue Yang
  • Regularization of the ensemble Kalman filter using a non-stationary, non-parametric spatial model
    Michael Tsyrulnikov, Arseniy Sotskiy
  • Nonlinear Data Assimilation in Chaotic Systems Using Deep Reinforcement Learning
    Mohamad Abed El Rahman Hammoud, Naila Raboudi, Edriss S. Titi, Omar Knio, Ibrahim Hoteit

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