scholarly journals Application of time-series regularity metrics to ion flux data from a population of pollen tubes

2021 ◽  
Vol 14 (1) ◽  
pp. 51-54
Author(s):  
Mariusz A. Pietruszka
Keyword(s):  
Time Series ◽  
Pollen Tubes ◽  
Ion Flux ◽  
Flux Data

From Satellite ion flux data to SEU rate estimation

2004 ◽  
Vol 51 (5) ◽  
pp. 2927-2935 ◽  
Author(s):  
S.E. Hoyos ◽  
H.D.R. Evans ◽  
E. Daly
Keyword(s):  
Ion Flux ◽  
Rate Estimation ◽  
Flux Data

scholarly journals Scales of seafloor sediment resuspension in the northern Gulf of Mexico

Elem Sci Anth ◽  
2018 ◽  
Vol 6 ◽  
Author(s):  
Arne-R. Diercks ◽  
Clayton Dike ◽  
Vernon L. Asper ◽  
Steven F. DiMarco ◽  
Jeffrey P. Chanton ◽  
...  
Keyword(s):  
Time Series ◽  
Particle Size ◽  
Gulf Of Mexico ◽  
Sediment Resuspension ◽  
Small Scale ◽  
Nepheloid Layer ◽  
Northern Gulf Of Mexico ◽  
Flux Data ◽  
Seafloor Sediment ◽  
Benthic Nepheloid Layer

Seafloor sediment resuspension events of different scales and magnitudes and the resulting deep (>1,000 m) benthic nepheloid layers were investigated in the northern Gulf of Mexico during Fall 2012 to Summer 2013. Time-series data of size-specific in-situ settling speeds of marine snow in the benthic nepheloid layer (moored flux cameras), particle size distributions (profiling camera), currents (various current meters) and stacked time-series flux data (sediment traps) were combined to recognize resuspension events ranging from small-scale local, to small-scale far-field to hurricane-scale. One small-scale local resuspension event caused by inertial currents was identified based on local high current speeds (>10 cm s–1) and trap data. Low POC content combined with high lithogenic silica flux at 30 m above bottom (mab) compared to the flux at 120 mab, suggested local resuspension reaching 30 mab, but not 120 mab. Another similar event was detected by the changes in particle size distribution and settling speeds of particles in the benthic nepheloid layer. Flux data indicated two other small-scale events, which occurred at some distance, rather than locally. Inertia-driven resuspension of material in shallower areas surrounding the traps presumably transported this material downslope leaving a resuspension signal at 120 mab, but not at 30 mab. The passage of hurricane Isaac left a larger scale resuspension event that lasted a few days and was recorded in both traps. Although hurricanes cause large-scale events readily observable in sediment trap samples, resuspension events small in temporal and spatial scale are not easily recognizable in trapped material as they tend to provide less material and become part of the background signal in the long-term averaged trap samples. We suggest that these small-scale resuspension events, mostly unnoticed in conventional time-series sampling, play an important role in the redistribution and ultimate fate of sediment distribution on the seafloor.

scholarly journals Testing the applicability of neural networks as a gap-filling method using CH<sub>4</sub> flux data from high latitude wetlands

2013 ◽  
Vol 10 (12) ◽  
pp. 8185-8200 ◽  
Author(s):  
S. Dengel ◽  
D. Zona ◽  
T. Sachs ◽  
M. Aurela ◽  
M. Jammet ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Pearson Correlation ◽  
Time Of Day ◽  
Gap Filling ◽  
Ch4 Flux ◽  
Advantages And Disadvantages ◽  
Flux Data ◽  
Free Data ◽  
Filling Method

Abstract. Since the advancement in CH4 gas analyser technology and its applicability to eddy covariance flux measurements, monitoring of CH4 emissions is becoming more widespread. In order to accurately determine the greenhouse gas balance, high quality gap-free data is required. Currently there is still no consensus on CH4 gap-filling methods, and methods applied are still study-dependent and often carried out on low resolution, daily data. In the current study, we applied artificial neural networks to six distinctively different CH4 time series from high latitudes, explain the method and test its functionality. We discuss the applicability of neural networks in CH4 flux studies, the advantages and disadvantages of this method, and what information we were able to extract from such models. Three different approaches were tested by including drivers such as air and soil temperature, barometric air pressure, solar radiation, wind direction (indicator of source location) and in addition the lagged effect of water table depth and precipitation. In keeping with the principle of parsimony, we included up to five of these variables traditionally measured at CH4 flux measurement sites. Fuzzy sets were included representing the seasonal change and time of day. High Pearson correlation coefficients (r) of up to 0.97 achieved in the final analysis are indicative for the high performance of neural networks and their applicability as a gap-filling method for CH4 flux data time series. This novel approach which we show to be appropriate for CH4 fluxes is a step towards standardising CH4 gap-filling protocols.

scholarly journals A robust data cleaning procedure for eddy covariance flux measurements

2019 ◽  
Author(s):  
Domenico Vitale ◽  
Gerardo Fratini ◽  
Massimo Bilancia ◽  
Giacomo Nicolini ◽  
Simone Sabbatini ◽  
...  
Keyword(s):  
Time Series ◽  
Data Processing ◽  
Eddy Covariance ◽  
Outlier Detection ◽  
Data Cleaning ◽  
False Negative ◽  
Error Rates ◽  
Quality Data ◽  
Cleaning Procedure ◽  
Flux Data

Abstract. Integration of long-term eddy covariance (EC) flux datasets over regional and global scales requires high degree of comparability of flux data measured at different stations, which entails not only similar-performing instrumentation and their appropriate deployment, but also standardized and reproducible data processing and quality control (QC) procedures. This work focuses on the latter topic and, in particular, on the development of a robust data cleaning procedure. The proposed strategy includes a set of tests aimed at detecting the presence of specific sources of systematic error in the data, as well as an outlier detection procedure aimed at identifying aberrant flux values. Results from tests and outlier detection are integrated in such a way as to leave a large degree of flexibility in the choice of tests and of test threshold values without losing in efficacy and, at the same time, to avoid the use of subjective criteria in the decision rule that specifies whether to retain or reject flux data of dubious quality. Tests development was rooted on advanced time series analysis techniques that consider the stochastic properties of both raw, high-frequency EC data and of flux time series, such as complex dynamics, high persistence and possible presence of stochastic trends. The performance of each proposed test is evaluated by means of Monte Carlo simulations on synthetic datasets, whereas their impact on observed times series was evaluated on a selection of EC datasets distributed by the ICOS research infrastructure. Simulation results evidenced that the proposed tests have a better performance compared to alternative existing QC routines, showing lower false positive and false negative error rates. The application of the proposed tests on real datasets led to an effective cleaning of EC flux data retaining the maximum number of good quality data. Although there is still room for improvement, in particular with the development of new QC tests, we think that the proposed data cleaning procedure can serve as a basis towards a unified QC strategy for EC datasets which i) includes only completely data-driven routines and is therefore suitable for automatic and centralized data processing pipelines, ii) guarantees results reproducibility and iii) is flexible and scalable to accommodate new and additional tests that makes the approach also suitable for other greenhouse gases.

scholarly journals SPATIOTEMPORAL VISUALIZATION OF TIME-SERIES SATELLITE-DERIVED CO2 FLUX DATA USING VOLUME RENDERING AND GPU-BASED INTERPOLATION ON A CLOUD-DRIVEN DIGITAL EARTH

2017 ◽  
Vol IV-4/W2 ◽  
pp. 77-85 ◽  
Author(s):  
S. Wu ◽  
Y. Yan ◽  
Z. Du ◽  
F. Zhang ◽  
R. Liu
Keyword(s):  
Time Series ◽  
Carbon Cycle ◽  
Volume Rendering ◽  
Satellite Data ◽  
Particle System ◽  
Realistic Simulation ◽  
Digital Earth ◽  
Flux Data ◽  
Location Aware ◽  
Ocean Carbon

The ocean carbon cycle has a significant influence on global climate, and is commonly evaluated using time-series satellite-derived CO<sub>2</sub> flux data. Location-aware and globe-based visualization is an important technique for analyzing and presenting the evolution of climate change. To achieve realistic simulation of the spatiotemporal dynamics of ocean carbon, a cloud-driven digital earth platform is developed to support the interactive analysis and display of multi-geospatial data, and an original visualization method based on our digital earth is proposed to demonstrate the spatiotemporal variations of carbon sinks and sources using time-series satellite data. Specifically, a volume rendering technique using half-angle slicing and particle system is implemented to dynamically display the released or absorbed CO<sub>2</sub> gas. To enable location-aware visualization within the virtual globe, we present a 3D particlemapping algorithm to render particle-slicing textures onto geospace. In addition, a GPU-based interpolation framework using CUDA during real-time rendering is designed to obtain smooth effects in both spatial and temporal dimensions. To demonstrate the capabilities of the proposed method, a series of satellite data is applied to simulate the air-sea carbon cycle in the China Sea. The results show that the suggested strategies provide realistic simulation effects and acceptable interactive performance on the digital earth.

Time series analysis demonstrates the absence of pulsatile hyphal growth

Microbiology ◽  
2003 ◽  
Vol 149 (11) ◽  
pp. 3111-3119 ◽  
Author(s):  
Karina Sampson ◽  
Roger R. Lew ◽  
I. Brent Heath
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Growth Rates ◽  
Tip Growth ◽  
Hyphal Growth ◽  
Pollen Tubes ◽  
Saprolegnia Ferax ◽  
Hyphal Tip Growth ◽  
Rigorous Statistical Analysis ◽  
Hyphal Tip

Hyphal tip growth has been previously reported as pulsatile, defined as regularly alternating fast and slow rates of extension. The growth of pollen tubes, and hyphae of Neurospora crassa and Saprolegnia ferax were analysed using high spatial and temporal resolution. By using long (100–500 s) records of growth rate, sampled every second, it was possible to apply rigorous statistical analysis of the time series. As previously demonstrated, pollen tubes can show pulsatile growth, detectable with this system. In contrast, hyphal growth rates do not show any evidence of pulsatile growth; instead, growth rates appear to fluctuate randomly. It is concluded that pulsatile growth is not a common feature of hyphal tip growth.

scholarly journals Testing the applicability of neural networks as a gap-filling method using CH<sub>4</sub> flux data from high latitude wetlands

2013 ◽  
Vol 10 (5) ◽  
pp. 7727-7759 ◽  
Author(s):  
S. Dengel ◽  
D. Zona ◽  
T. Sachs ◽  
M. Aurela ◽  
M. Jammet ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Pearson Correlation ◽  
Time Of Day ◽  
Gap Filling ◽  
Ch4 Flux ◽  
Advantages And Disadvantages ◽  
Flux Data ◽  
Free Data ◽  
Filling Method

Abstract. Since the advancement in CH4 gas analyser technology and its applicability to eddy covariance flux measurements, monitoring of CH4 emissions is becoming more widespread. In order to accurately determine the greenhouse gas balance, high quality gap-free data is required. Currently there is still no consensus on CH4 gap-filling methods, and methods applied are still study-dependent and often carried out on low resolution daily data. In the current study, we applied artificial neural networks to six distinctively different CH4 time series from high latitudes in order to recover missing data points, explained the method and tested its functionality. We discuss the applicability of neural networks in CH4 flux studies, the advantages and disadvantages of this method, and what information we were able to extract from such models. In keeping with the principle of parsimony, we included only five standard meteorological variables traditionally measured at CH4 flux measurement sites. These included drivers such as air and soil temperature, barometric air pressure, solar radiation, and in addition wind direction (indicator of source location). Four fuzzy sets were included representing the time of day. High Pearson correlation coefficients (r) of 0.76–0.93 achieved in the final analysis are indicative for the high performance of neural networks and their applicability as a gap-filling method for CH4 flux data time series. This novel approach that we showed to be appropriate for CH4 fluxes is a step towards standardising CH4 gap-filling protocols.

The LDE-Type Flare Occurrence (1969-1993)

1994 ◽  
Vol 144 ◽  
pp. 279-282
Author(s):  
A. Antalová
Keyword(s):  
Time Series ◽  
Fourier Analysis ◽  
Sunspot Cycle ◽  
List Type ◽  
Type Number ◽  
Solar Cycles ◽  
Type Iv ◽  
Long Duration ◽  
Cycle Maximum ◽  
Flare Index

AbstractThe occurrence of LDE-type flares in the last three cycles has been investigated. The Fourier analysis spectrum was calculated for the time series of the LDE-type flare occurrence during the 20-th, the 21-st and the rising part of the 22-nd cycle. LDE-type flares (Long Duration Events in SXR) are associated with the interplanetary protons (SEP and STIP as well), energized coronal archs and radio type IV emission. Generally, in all the cycles considered, LDE-type flares mainly originated during a 6-year interval of the respective cycle (2 years before and 4 years after the sunspot cycle maximum). The following significant periodicities were found:• in the 20-th cycle: 1.4, 2.1, 2.9, 4.0, 10.7 and 54.2 of month,• in the 21-st cycle: 1.2, 1.6, 2.8, 4.9, 7.8 and 44.5 of month,• in the 22-nd cycle, till March 1992: 1.4, 1.8, 2.4, 7.2, 8.7, 11.8 and 29.1 of month,• in all interval (1969-1992):a)the longer periodicities: 232.1, 121.1 (the dominant at 10.1 of year), 80.7, 61.9 and 25.6 of month,b)the shorter periodicities: 4.7, 5.0, 6.8, 7.9, 9.1, 15.8 and 20.4 of month.Fourier analysis of the LDE-type flare index (FI) yields significant peaks at 2.3 - 2.9 months and 4.2 - 4.9 months. These short periodicities correspond remarkably in the all three last solar cycles. The larger periodicities are different in respective cycles.

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