A distributional regression network acts as a backward operator to produce uncertainty-quantified, multivariate Gaussian retrievals of cloud properties from six solar channels for data assimilation.
Tippett, Jeffrey L
8 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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LES of a real campus finds shielding controls individual building drag, quantified by upstream fetch ratio L_s/H_s and relative height ratio H_s/H.
EnSF-LR combines nonlinear score-based analysis on observed components with EnKF-style linear regression on unobserved components via ensemble covariance, achieving lower full-state RMSE than EnSF and EnKF in nonlinear-observation tests on Lorenz-63 and Lorenz-96.
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.
Identifies morphology-dependent urban heterogeneity scales (~256 m original layout, ~64 m infilled) where resolved and sub-grid flow variability become comparable and tests drag and stress parameterizations for scale-aware urban canopy models.
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 variational hierarchy unifies Bayesian filtering, variational data assimilation, KL-regularized control, and Kalman methods by proving that posteriors minimize a likelihood-plus-KL objective with evidence as the global infimum.
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.
citing papers explorer
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Using Distributional Regression Networks to Retrieve Cloud Properties from Solar Satellite Channels for Data Assimilation
A distributional regression network acts as a backward operator to produce uncertainty-quantified, multivariate Gaussian retrievals of cloud properties from six solar channels for data assimilation.
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Building drag and shielding in a realistic urban environment
LES of a real campus finds shielding controls individual building drag, quantified by upstream fetch ratio L_s/H_s and relative height ratio H_s/H.
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A Two-Step Ensemble Score Filter for Data Assimilation in Partially Observed Systems
EnSF-LR combines nonlinear score-based analysis on observed components with EnKF-style linear regression on unobserved components via ensemble covariance, achieving lower full-state RMSE than EnSF and EnKF in nonlinear-observation tests on Lorenz-63 and Lorenz-96.
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Amortized Probabilistic Retrieval of Atmospheric CO2 from OCO-2 Spectra Using Deep Learning with Laplace Approximations and Normalizing Flows
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.
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Multi-scale flow analysis for scale-aware urban-canopy models
Identifies morphology-dependent urban heterogeneity scales (~256 m original layout, ~64 m infilled) where resolved and sub-grid flow variability become comparable and tests drag and stress parameterizations for scale-aware urban canopy models.
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Scalable generative modeling of non-Gaussian spatio-temporal fields via autoregressive Gaussian processes
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.
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Reinforcement Learning, Optimal Control, and Bayesian Filtering in Data Assimilation
A variational hierarchy unifies Bayesian filtering, variational data assimilation, KL-regularized control, and Kalman methods by proving that posteriors minimize a likelihood-plus-KL objective with evidence as the global infimum.
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Dynamics of East Atlantic seed vortex populations in global km-scale models
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.