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Found 4 papers, 2 papers with code
Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems
With small ensembles, the estimated background error covariance matrix in the EnKF algorithm suffers from sampling error, leading to an erroneous estimate of the analysis state (initial condition for the next forecast cycle).
Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers
These components are 1) a deep spatial transformer added to the latent space of the U-NETs to preserve a property called equivariance, which is related to correctly capturing rotations and scalings of features in spatio-temporal data, 2) a data-assimilation (DA) algorithm to ingest noisy observations and improve the initial conditions for next forecasts, and 3) a multi-time-step algorithm, which combines forecasts from DDWP models with different time steps through DA, improving the accuracy of forecasts at short intervals.
Proof that the Kalman gain minimizes the generalized variance
The optimal gain matrix of the Kalman filter is often derived by minimizing the trace of the posterior covariance matrix.
parasweep: A template-based utility for generating, dispatching, and post-processing of parameter sweeps
We introduce parasweep, a free and open-source utility for facilitating parallel parameter sweeps with computational models.
Distributed, Parallel, and Cluster Computing
