Concurrent high resolution bio-optical and physical time series observations in the Sargasso Sea during the spring of 1987

1991 ◽  
Vol 96 (C5) ◽  
pp. 8643 ◽  
Author(s):  
T. Dickey ◽  
J. Marra ◽  
T. Granata ◽  
C. Langdon ◽  
M. Hamilton ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Sargasso Sea ◽  
Physical Time

scholarly journals Autonomous, high-resolution observations of particle flux in the oligotrophic ocean

2013 ◽  
Vol 10 (8) ◽  
pp. 5517-5531 ◽  
Author(s):  
M. L. Estapa ◽  
K. Buesseler ◽  
E. Boss ◽  
G. Gerbi
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Particle Flux ◽  
Sediment Traps ◽  
Sargasso Sea ◽  
Time Resolved ◽  
Time Series Study ◽  
Different Depths ◽  
Particle Export ◽  
Series Study

Abstract. Observational gaps limit our understanding of particle flux attenuation through the upper mesopelagic because available measurements (sediment traps and radiochemical tracers) have limited temporal resolution, are labor-intensive, and require ship support. Here, we conceptually evaluate an autonomous, optical proxy-based method for high-resolution observations of particle flux. We present four continuous records of particle flux collected with autonomous profiling floats in the western Sargasso Sea and the subtropical North Pacific, as well as one shorter record of depth-resolved particle flux near the Bermuda Atlantic Time-series Study (BATS) and Oceanic Flux Program (OFP) sites. These observations illustrate strong variability in particle flux over very short (~1-day) timescales, but at longer timescales they reflect patterns of variability previously recorded during sediment trap time series. While particle flux attenuation at BATS/OFP agreed with the canonical power-law model when observations were averaged over a month, flux attenuation was highly variable on timescales of 1–3 days. Particle fluxes at different depths were decoupled from one another and from particle concentrations and chlorophyll fluorescence in the immediately overlying surface water, consistent with horizontal advection of settling particles. We finally present an approach for calibrating this optical proxy in units of carbon flux, discuss in detail the related, inherent physical and optical assumptions, and look forward toward the requirements for the quantitative application of this method in highly time-resolved studies of particle export and flux attenuation.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

scholarly journals Interpretation of the Chemical and Physical Time-Series Retrieved from Sentik Glacier, Ladakh Himalaya, India

1984 ◽  
Vol 30 (104) ◽  
pp. 66-76 ◽  
Author(s):  
Paul A. Mayewski ◽  
W. Berry Lyons ◽  
N. Ahmad ◽  
Gordon Smith ◽  
M. Pourchet
Keyword(s):  
Time Series ◽  
Chemical Species ◽  
Data Sets ◽  
Reactive Iron ◽  
Physical Time ◽  
Ladakh Himalaya ◽  
Data Density ◽  
Mass Circulation ◽  
The Himalaya ◽  
Analysis Of Time Series

AbstractSpectral analysis of time series of a c. 17 ± 0.3 year core, calibrated for total ß activity recovered from Sentik Glacier (4908m) Ladakh, Himalaya, yields several recognizable periodicities including subannual, annual, and multi-annual. The time-series, include both chemical data (chloride, sodium, reactive iron, reactive silicate, reactive phosphate, ammonium, δD, δ(18O) and pH) and physical data (density, debris and ice-band locations, and microparticles in size grades 0.50 to 12.70 μm). Source areas for chemical species investigated and general air-mass circulation defined from chemical and physical time-series are discussed to demonstrate the potential of such studies in the development of paleometeorological data sets from remote high-alpine glacierized sites such as the Himalaya.

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