S. Penny

发表

Automated Route Generation for Avoiding Deterministic Weather in Transition Airspace

Joseph S. B. Mitchell, J. Krozel, J. Prete, 2007 .

Temperature and Salinity Variability in the SODA3, ECCO4r3, and ORAS5 Ocean Reanalyses, 1993–2015

E. Kalnay, J. Carton, S. Penny, 2019, Journal of Climate.

Causality Analysis: Identifying the Leading Element in a Coupled Dynamical System

Amir E. BozorgMagham, E. Kalnay, S. Penny, 2015, PloS one.

AGGREGATE STATISTICS OF THE NATIONAL AIRSPACE SYSTEM

J. Krozel, S. Penny, Bob Hoffman, 2003 .

The Hybrid Local Ensemble Transform Kalman Filter

Stephen G. Penny, S. Penny, 2014 .

Hybrid Gain Data Assimilation Using Variational Corrections in the Subspace Orthogonal to the Ensemble

S. Penny, Shu-Chih Yang, Chih-Chien Chang, 2020, Monthly Weather Review.

A local particle filter for high-dimensional geophysical systems

Stephen G. Penny, Takemasa Miyoshi, T. Miyoshi, 2015 .

Improving NCEP’s global-scale wave ensemble averages using neural networks

S. Penny, V. Krasnopolsky, R. Campos, 2020 .

Improved ocean analysis for the Indian Ocean

D. Behringer, S. Penny, M. Ravichandran, 2018, Journal of Operational Oceanography.

The local ensemble transform Kalman filter and the running-in-place algorithm applied to a global ocean general circulation model

E. Kalnay, K. Ide, J. Carton, 2013 .

Lagrangian Data Assimilation of Surface Drifters in a Double-Gyre Ocean Model Using the Local Ensemble Transform Kalman Filter

S. Penny, Luyu Sun, 2019, Monthly Weather Review.

Ocean Reanalyses: Recent Advances and Unsolved Challenges

C. Domingues, M. Balmaseda, A. Barth, 2019, Front. Mar. Sci..

Developing 4D-Var for strongly coupled data assimilation using a coupled atmosphere-ocean quasi-geostrophic model

M. Goodliff, S. Penny, 2022, Monthly Weather Review.

Assessments of Surface Winds and Waves from the NCEP Ensemble Forecast System

S. Penny, V. Krasnopolsky, R. Campos, 2018, Weather and Forecasting.

A Comparison of Hybrid‐Gain Versus Hybrid‐Covariance Data Assimilation for Global NWP

J. Whitaker, S. Penny, A. Shlyaeva, 2022, Journal of Advances in Modeling Earth Systems.

Identifying Efficient Ensemble Perturbations for Initializing Subseasonal‐To‐Seasonal Prediction

S. Vannitsem, S. Penny, J. Demaeyer, 2022, Journal of Advances in Modeling Earth Systems.

Correcting Systematic and State‐Dependent Errors in the NOAA FV3‐GFS Using Neural Networks

J. Whitaker, R. Pincus, S. Penny, 2022, Journal of Advances in Modeling Earth Systems.

A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics

Stephen G. Penny, Henry D. I. Abarbanel, Jason A. Platt, 2022, Neural Networks.

Robust forecasting using predictive generalized synchronization in reservoir computing.

Adrian S. Wong, Jason A. Platt, H. Abarbanel, 2021, Chaos.

Forecasting Using Reservoir Computing: The Role of Generalized Synchronization pdf

Stephen G. Penny, Henry D. I. Abarbanel, Adrian S. Wong, 2021, ArXiv.

Challenges and Prospects in Ocean Circulation Models

Richard J. Greatbatch, Todd D. Ringler, Stephen G. Penny, 2019, Front. Mar. Sci..

CLASSIFICATION OF DAYS IN THE NATIONAL AIRSPACE SYSTEM USING CLUSTER ANALYSIS

Jimmy Krozel, Anindya Roy, Steve Penny, 2005 .

A CLUSTER ANALYSIS TO CLASSIFY DAYS IN THE NATIONAL AIRSPACE SYSTEM

Jimmy Krozel, Karlin Roth, Bob Hoffman, 2003 .

Correction: Causality Analysis: Identifying the Leading Element in a Coupled Dynamical System

E. Kalnay, S. Penny, S. Motesharrei, 2015, PloS one.

Ocean Observations to Improve Our Understanding, Modeling, and Forecasting of Subseasonal-to-Seasonal Variability

T. Hamill, Arun Kumar, B. Cornuelle, 2019, Front. Mar. Sci..

Integrating Recurrent Neural Networks With Data Assimilation for Scalable Data‐Driven State Estimation pdf

Hsin-Yi Lin, Stephen G. Penny, Jason A. Platt, 2021, Journal of Advances in Modeling Earth Systems.

Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence

S. Penny, J. Platt, T. A. Smith, 2023, ArXiv.

Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction

Stephen G. Penny, Yosuke Fujii, Ibrahim Hoteit, 2019, Front. Mar. Sci..

Outlook for Exploiting Artificial Intelligence in the Earth and Environmental Sciences

S. E. Haupt, K. Casey, A. McGovern, 2020, Bulletin of the American Meteorological Society.

Constraining Chaos: Enforcing dynamical invariants in the training of recurrent neural networks

Jason A. Platt, H. Abarbanel, S. Penny, 2023, ArXiv.

Integrating Recurrent Neural Networks With Data Assimilation for Scalable Data‐Driven State Estimation

H. Abarbanel, M. Goodliff, S. Penny, 2021, Journal of Advances in Modeling Earth Systems.

Mathematical foundations of hybrid data assimilation from a synchronization perspective.

Stephen G Penny, S. Penny, 2017, Chaos.

A Hybrid Global Ocean Data Assimilation System at NCEP

E. Kalnay, D. Behringer, J. Carton, 2014 .

Strongly Coupled Data Assimilation in Multiscale Media: Experiments Using a Quasi‐Geostrophic Coupled Model

T. Yoshida, S. G. Penny, E. Bach, 2019, Journal of Advances in Modeling Earth Systems.

Best Practice Strategies for Process Studies Designed to Improve Climate Modeling

Janet Sprintall, Victoria J. Coles, Kevin A. Reed, 2020, Bulletin of the American Meteorological Society.

Impacts of the Lagrangian Data Assimilation of Surface Drifters on Estimating Ocean Circulation During the Gulf of Mexico Grand Lagrangian Deployment

M. Harrison, S. Penny, Luyu Sun, 2022, Monthly Weather Review.

Nonlinear Wave Ensemble Averaging in the Gulf of Mexico Using Neural Networks

Stephen G. Penny, Jose-Henrique G. M. Alves, Ricardo Martins Campos, 2019, Journal of Atmospheric and Oceanic Technology.

An Investigation of Ocean Model Uncertainties Through Ensemble Forecast Experiments in the Southwest Atlantic Ocean

C. Tanajura, S. Penny, L. Pezzi, 2019, Journal of Geophysical Research: Oceans.

Coupled Data Assimilation for Integrated Earth System Analysis and Prediction: Goals, Challenges, and Recommendations

Stephen G. Penny, Thomas M. Hamill, T. Hamill, 2017 .

Identifying Efficient Ensemble Perturbations for Initializing Subseasonal‐To‐Seasonal Prediction

S. Vannitsem, S. Penny, J. Demaeyer, 2021, Journal of Advances in Modeling Earth Systems.

Ensemble-based data assimilation of significant wave height from Sofar Spotters and satellite altimeters with a global operational wave model

S. Penny, I. Houghton, P. Smit, 2023, Ocean Modelling.

Assimilating atmospheric observations into the ocean using strongly coupled ensemble data assimilation

Stephen G. Penny, Takemasa Miyoshi, Eugenia Kalnay, 2016 .

Robust Traffic Flow Management: Coevolutionary Approach

Babak Khorrami, Herbert Hackney, Rafal Kicinger, 2009 .

Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence

Jason A. Platt, S. Penny, J. Platt, 2023, Journal of Advances in Modeling Earth Systems.

'Next Generation' Reservoir Computing: an Empirical Data-Driven Expression of Dynamical Equations in Time-Stepping Form pdf

Stephen G. Penny, Jason A. Platt, Tse-Chun Chen, 2022, ArXiv.

Impact of an upgraded model in the NCEP Global Ocean Data Assimilation System: The tropical Indian Ocean

D. Behringer, S. Penny, M. Ravichandran, 2016 .

Comparison of Algorithms for Synthesizing Weather Avoidance Routes in Transition Airspace

Joseph S. B. Mitchell, Jimmy Krozel, Joseph Prete, 2004 .

Ground Delay Programs to Address Weather within En Route Flow Constrained Areas

Jimmy Krozel, Steve Penny, Ray Jakobovits, 2005 .

Subseasonal Forecast Skill Improvement From Strongly Coupled Data Assimilation With a Linear Inverse Model

M. Newman, C. Snyder, S. Penny, 2022, Geophysical Research Letters.

Coupled Data Assimilation with a Linear Inverse

M. Newman, C. Snyder, S. Penny, 2022 .

Workshop on the Initialization of High-Resolution Earth System Models

Craig H. Bishop, Steve Penny, Santha Akella, 2018 .