4.6 Article

Convolutional Neural Network-Based Adaptive Localization for an Ensemble Kalman Filter

Related references

Note: Only part of the references are listed.
Article Meteorology & Atmospheric Sciences

Convolutional Neural Network-Based Adaptive Localization for an Ensemble Kalman Filter

Zhongrui Wang et al.

Summary: Two convolutional neural network-based localization methods are proposed in this article, which can better capture the structures of the Kalman gain and generate improved analyses and forecasts in cycling assimilations.

JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS (2023)

Article Computer Science, Interdisciplinary Applications

Machine learning techniques to construct patched analog ensembles for data assimilation

L. Minah Yang et al.

Summary: By incorporating generative models and patching schemes, the scalability of the constructed analog ensemble optimal interpolation method for data assimilation has been enhanced, resulting in improved assimilation performance on complex dynamical models.

JOURNAL OF COMPUTATIONAL PHYSICS (2021)

Article Meteorology & Atmospheric Sciences

Sampling Error Correction Evaluated Using a Convective-Scale 1000-Member Ensemble

Tobias Necker et al.

MONTHLY WEATHER REVIEW (2020)

Article Geosciences, Multidisciplinary

Adaptive Localization for Tropical Cyclones With Satellite Radiances in an Ensemble Kalman Filter

Chen Wang et al.

FRONTIERS IN EARTH SCIENCE (2020)

Article Meteorology & Atmospheric Sciences

Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter

Lili Lei et al.

JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS (2020)

Review Meteorology & Atmospheric Sciences

Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation

P. L. Houtekamer et al.

MONTHLY WEATHER REVIEW (2016)

Article Meteorology & Atmospheric Sciences

Localizing the impact of satellite radiance observations using a global group ensemble filter

Lili Lei et al.

JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS (2016)

Article Meteorology & Atmospheric Sciences

Linear Filtering of Sample Covariances for Ensemble-Based Data Assimilation. Part II: Application to a Convective-Scale NWP Model

Benjamin Menetrier et al.

MONTHLY WEATHER REVIEW (2015)

Article Meteorology & Atmospheric Sciences

NCAR's Experimental Real-Time Convection-Allowing Ensemble Prediction System

Craig S. Schwartz et al.

WEATHER AND FORECASTING (2015)

Article Meteorology & Atmospheric Sciences

Towards a theory of optimal localisation

Jonathan Flowerdew

TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY (2015)

Article Meteorology & Atmospheric Sciences

Higher Resolution in an Operational Ensemble Kalman Filter

P. L. Houtekamer et al.

MONTHLY WEATHER REVIEW (2014)

Article Meteorology & Atmospheric Sciences

Impacts of Frequent Assimilation of Surface Pressure Observations on Atmospheric Analyses

Lili Lei et al.

MONTHLY WEATHER REVIEW (2014)

Article Meteorology & Atmospheric Sciences

Impact of Surface Parameter Uncertainties within the Canadian Regional Ensemble Prediction System

Christophe Lavaysse et al.

MONTHLY WEATHER REVIEW (2013)

Article Meteorology & Atmospheric Sciences

Empirical Localization of Observation Impact in Ensemble Kalman Filters

Jeffrey Anderson et al.

MONTHLY WEATHER REVIEW (2013)

Article Meteorology & Atmospheric Sciences

Localization and Sampling Error Correction in Ensemble Kalman Filter Data Assimilation

Jeffrey L. Anderson

MONTHLY WEATHER REVIEW (2012)

Article Meteorology & Atmospheric Sciences

Balance and Ensemble Kalman Filter Localization Techniques

Steven J. Greybush et al.

MONTHLY WEATHER REVIEW (2011)

Article Meteorology & Atmospheric Sciences

Vertical Covariance Localization for Satellite Radiances in Ensemble Kalman Filters

William F. Campbell et al.

MONTHLY WEATHER REVIEW (2010)

Article Meteorology & Atmospheric Sciences

Ensemble data assimilation with the CNMCA regional forecasting system

Massimo Bonavita et al.

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY (2010)

Article Meteorology & Atmospheric Sciences

Model Error Representation in an Operational Ensemble Kalman Filter

P. L. Houtekamer et al.

MONTHLY WEATHER REVIEW (2009)

Article Meteorology & Atmospheric Sciences

Ensemble covariances adaptively localized with ECO-RAP. Part 1: tests on simple error models

Craig H. Bishop et al.

TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY (2009)

Article Meteorology & Atmospheric Sciences

Ensemble covariances adaptively localized with ECO-RAP. Part 2: a strategy for the atmosphere

Craig H. Bishop et al.

TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY (2009)

Article Meteorology & Atmospheric Sciences

Ensemble data assimilation with the NCEP Global Forecast System

Jeffrey S. Whitaker et al.

MONTHLY WEATHER REVIEW (2008)

Article Meteorology & Atmospheric Sciences

Flow-adaptive moderation of spurious ensemble correlations and its use in ensemble-based data assimilation

Craig H. Bishop et al.

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY (2007)

Article Computer Science, Artificial Intelligence

Image denoising by sparse 3-D transform-domain collaborative filtering

Kostadin Dabov et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2007)

Article Mathematics, Applied

Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter

Jeffrey L. Anderson

PHYSICA D-NONLINEAR PHENOMENA (2007)

Article Statistics & Probability

Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants

Reinhard Furrer et al.

JOURNAL OF MULTIVARIATE ANALYSIS (2007)

Article Meteorology & Atmospheric Sciences

Ensemble Kalman filtering

P. L. Houtekamer et al.

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY (2005)

Article Meteorology & Atmospheric Sciences

Designing chaotic models

EN Lorenz

JOURNAL OF THE ATMOSPHERIC SCIENCES (2005)

Article Meteorology & Atmospheric Sciences

Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations

PL Houtekamer et al.

MONTHLY WEATHER REVIEW (2005)