Daily reservoir water level reconstruction remains challenging when satellite observations are sparse and temporally irregular. This study proposes a framework integrating multisource remote sensing, gated recurrent unit (GRU) priors, and ensemble Kalman filtering/smoothing (EnKF/EnKS), using the Fengtan Reservoir as a...
This study assimilates a newly developed Deep Learning‐enhanced AMSR‐E/2 soil moisture data set that provides a seamless daily record from 2003 to 2023 by reducing retrieval artifacts while preserving spatiotemporal consistency, and demonstrates that observation pre‐processing is essential for effective soil moisture d...
An integrated hybrid ensemble Kalman smoother (IHEnKS) is proposed to optimally utilize proxy data from the past to the future for paleoclimate data assimilation (PDA). As an extension of the integrated hybrid ensemble Kalman filter, IHEnKS assimilates future proxies through cross‐time error covariances, which are es...
Latent-LWETKF, a structured latent-space implementation of the localized weighted ensemble transform Kalman filter, is developed, supporting tractable nonlinear ensemble analysis with 40 members while retaining physical observation geometry and complete-field decoding context.
The ensemble smoother with multiple data assimilation (ESMDA) is widely used for reservoir history matching because of its parallel computational efficiency and ease of implementation. Production data in high-dimensional reservoir applications are often correlated across time, wells, and response types. When a diagon...
Accurate sub-daily soil moisture (SM) retrievals from satellite observations remain a major challenge due to sparse temporal sampling and retrieval uncertainties. This study introduces a localized convolutional neural network (CNN-l) framework designed to enhance SM estimates from Advanced SCATterometer (ASCAT) obser...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
