scholarly journals Observations of phase and intensity fluctuations for low-frequency, long-range transmissions in the Philippine Sea and comparisons to path-integral theory

2019 ◽  
Vol 146 (1) ◽  
pp. 567-585 ◽  
Author(s):  
John A. Colosi ◽  
Bruce D. Cornuelle ◽  
Matthew A. Dzieciuch ◽  
Peter F. Worcester ◽  
Tarun K. Chandrayadula
2019 ◽  
Vol 146 (4) ◽  
pp. 2986-2986
Author(s):  
John A. Colosi ◽  
Bruce Cornuelle ◽  
Matthew Dzieciuch ◽  
Peter F. Worcester ◽  
Tarun K. Chandrayadula

2020 ◽  
Vol 147 (2) ◽  
pp. 877-897
Author(s):  
Tarun K. Chandrayadula ◽  
Sivaselvi Periyasamy ◽  
John A. Colosi ◽  
Peter F. Worcester ◽  
Matthew A. Dzieciuch ◽  
...  

2012 ◽  
Vol 131 (4) ◽  
pp. 3354-3354
Author(s):  
Andrew W. White ◽  
Rex K. Andrew ◽  
James A. Mercer ◽  
Peter F. Worcester ◽  
Matthew A. Dzieciuch

2012 ◽  
Vol 131 (4) ◽  
pp. 3353-3353
Author(s):  
Tarun K. Chandrayadula ◽  
John A. Colosi ◽  
Peter F. Worcester ◽  
Matthew Dzieciuch

2019 ◽  
Vol 146 (4) ◽  
pp. 2986-2986
Author(s):  
Tarun K. Chandrayadula ◽  
John A. Colosi ◽  
Peter F. Worcester ◽  
Matthew Dzieciuch ◽  
James Mercer ◽  
...  

2012 ◽  
Vol 25 (6) ◽  
pp. 1814-1826 ◽  
Author(s):  
Dimitrios Giannakis ◽  
Andrew J. Majda

Abstract An information-theoretic framework is developed to assess the predictive skill and model error in imperfect climate models for long-range forecasting. Here, of key importance is a climate equilibrium consistency test for detecting false predictive skill, as well as an analogous criterion describing model error during relaxation to equilibrium. Climate equilibrium consistency enforces the requirement that long-range forecasting models should reproduce the climatology of prediction observables with high fidelity. If a model meets both climate consistency and the analogous criterion describing model error during relaxation to equilibrium, then relative entropy can be used as an unbiased superensemble measure of the model’s skill in long-range coarse-grained forecasts. As an application, the authors investigate the error in modeling regime transitions in a 1.5-layer ocean model as a Markov process and identify models that are strongly persistent but their predictive skill is false. The general techniques developed here are also useful for estimating predictive skill with model error for Markov models of low-frequency atmospheric regimes.


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