QPCA-EnDCF is a deterministic ensemble data assimilation method that replaces stochastic observation perturbations with a spectrally regularized rank-κ update on whitened residuals, yielding better spread-skill and rank-histogram reliability than stochastic EnKF on Lorenz-96 in undersampled regimes.
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A deep learning method amortizes probabilistic XCO2 retrieval from OCO-2 spectra via Laplace approximations and normalizing flows, trained on simulations with model errors to achieve faster inference and better-calibrated uncertainties than operational solvers.
Latent diffusion model parameterization allows MCMC and SMC to outperform latent-space ESMDA in data mismatch and uncertainty reduction for 3D subsurface DA, while model-space ESMDA produces unrealistic posteriors.
An autoregressive Gaussian process transport-map construction factors spatio-temporal joint densities into conditional distributions with data-dependent sparsity to enable scalable generative modeling of non-Gaussian fields.
A derivative-free ensemble Kalman-Bucy smoother is developed for continuous-time data assimilation that supports Bayesian causal inference and iterative model structure identification with small ensemble sizes under partial observations.
The explicit-convection km-scale simulation produces fewer and weaker Atlantic hurricanes than parameterized coarser runs because seed vortices fail to amplify after crossing the West African coast due to weaker top-heavy mass flux profiles and underestimated MCS stratiform components.
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A Data-Consistent Approach to Ensemble Filtering
QPCA-EnDCF is a deterministic ensemble data assimilation method that replaces stochastic observation perturbations with a spectrally regularized rank-κ update on whitened residuals, yielding better spread-skill and rank-histogram reliability than stochastic EnKF on Lorenz-96 in undersampled regimes.