Conditional and Marginal Mutual Information in Gaussian and Hyperbolic Decay Time Series

2016 ◽  
Vol 37 (6) ◽  
pp. 851-861
Author(s):  
Gordon Chavez
Keyword(s):  
Time Series ◽  
Mutual Information ◽  
Decay Time

Multiscale permutation mutual information quantify the information interaction for traffic time series

2020 ◽  
Vol 102 (3) ◽  
pp. 1909-1923
Author(s):  
Yi Yin ◽  
Xi Wang ◽  
Qiang Li ◽  
Pengjian Shang ◽  
He Gao ◽  
...  
Keyword(s):  
Time Series ◽  
Mutual Information ◽  
Traffic Time Series ◽  
Information Interaction

scholarly journals A model of Fe speciation and biogeochemistry at the Tropical Eastern North Atlantic Time-Series Observatory site

2009 ◽  
Vol 6 (10) ◽  
pp. 2041-2061 ◽  
Author(s):  
Y. Ye ◽  
C. Völker ◽  
D. A. Wolf-Gladrow
Keyword(s):  
Time Series ◽  
Vertical Distribution ◽  
North Atlantic ◽  
Decay Time ◽  
Particle Aggregation ◽  
Dust Particles ◽  
Dimensional Model ◽  
One Dimensional ◽  
Fe Speciation ◽  
Eastern North Atlantic

Abstract. A one-dimensional model of Fe speciation and biogeochemistry, coupled with the General Ocean Turbulence Model (GOTM) and a NPZD-type ecosystem model, is applied for the Tropical Eastern North Atlantic Time-Series Observatory (TENATSO) site. Among diverse processes affecting Fe speciation, this study is focusing on investigating the role of dust particles in removing dissolved iron (DFe) by a more complex description of particle aggregation and sinking, and explaining the abundance of organic Fe-binding ligands by modelling their origin and fate. The vertical distribution of different particle classes in the model shows high sensitivity to changing aggregation rates. Using the aggregation rates from the sensitivity study in this work, modelled particle fluxes are close to observations, with dust particles dominating near the surface and aggregates deeper in the water column. POC export at 1000 m is a little higher than regional sediment trap measurements, suggesting further improvement of modelling particle aggregation, sinking or remineralisation. Modelled strong ligands have a high abundance near the surface and decline rapidly below the deep chlorophyll maximum, showing qualitative similarity to observations. Without production of strong ligands, phytoplankton concentration falls to 0 within the first 2 years in the model integration, caused by strong Fe-limitation. A nudging of total weak ligands towards a constant value is required for reproducing the observed nutrient-like profiles, assuming a decay time of 7 years for weak ligands. This indicates that weak ligands have a longer decay time and therefore cannot be modelled adequately in a one-dimensional model. The modelled DFe profile is strongly influenced by particle concentration and vertical distribution, because the most important removal of DFe in deeper waters is colloid formation and aggregation. Redissolution of particulate iron is required to reproduce an observed DFe profile at TENATSO site. Assuming colloidal iron is mainly composed of inorganic colloids, the modelled colloidal to soluble iron ratio is lower that observations, indicating the importance of organic colloids.

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