scholarly journals 6-Hour to 1-Year Variance of Five Global Precipitation Sets

2007 ◽  
Vol 11 (11) ◽  
pp. 1-29 ◽  
Author(s):  
Alex C. Ruane ◽  
John O. Roads
Keyword(s):  
High Frequency ◽  
Spectral Characteristics ◽  
Power Spectra ◽  
Low Frequency ◽  
Tropical Rainfall Measuring Mission ◽  
Seasonal Migration ◽  
Low Frequency Variability ◽  
Precipitation Estimation ◽  
The Indian Ocean ◽  
Global Precipitation

Abstract Three-hourly time series of precipitation from three high-resolution precipitation products [Tropical Rainfall Measuring Mission (TRMM) algorithm 3B-42, the Climate Prediction Center’s morphing method (CMORPH), and the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)] and two reanalyses are examined for their frequency characteristics using broad and narrow variance categories. After isolating the diurnally forced peaks (at 24, 12, 8, and 6 h), the power spectra are divided into comprehensive broad bands comprising the annual (∼80 days–1 yr), intraseasonal (20 to ∼80 days), slow (6–20 days) and fast (36 h–6 days) synoptic, and high-frequency (6–36 h) periods. Global maps accounting for 100% of precipitation’s variance are analyzed to identify unique regional behaviors. Annual variability is strongest over regions affected by the seasonal migration of the intertropical convergence zone, as well as over monsoonal regions. The intraseasonal band displays off-equatorial evidence of the Madden–Julian oscillation (MJO), particularly in the Indian Ocean, but the MJO’s rainfall is partially manifested in the slow synoptic band and at higher frequencies. The fast synoptic band is particularly strong over the oceans, while high-frequency variability is enhanced over land by more extreme surface gradients. Diurnal variance is strongest at low latitudes and is pronounced over regions with well-known diurnal circulations, including mountains and coastlines. Interproduct and intermodel differences also indicate biases of the precipitation product algorithms and convective parameterizations, including a strong bias toward low-frequency variability in the relaxed Arakawa–Schubert parameterization employed by one of the reanalyses, as well as increased white-spectral characteristics over land in the precipitation products.

Nonrandom variability in respiratory cycle parameters of humans during stage 2 sleep

1990 ◽  
Vol 69 (2) ◽  
pp. 630-639 ◽  
Author(s):  
M. Modarreszadeh ◽  
E. N. Bruce ◽  
B. Gothe
Keyword(s):  
High Frequency ◽  
Minute Ventilation ◽  
Power Spectra ◽  
Low Frequency ◽  
Neural Mechanism ◽  
Normal Subjects ◽  
Feedback Loops ◽  
Low Frequency Oscillations ◽  
Stage 2 Sleep ◽  
Correlated Components

We analyzed breath-to-breath inspiratory time (TI), expiratory time (TE), inspiratory volume (VI), and minute ventilation (Vm) from 11 normal subjects during stage 2 sleep. The analysis consisted of 1) fitting first- and second-order autoregressive models (AR1 and AR2) and 2) obtaining the power spectra of the data by fast-Fourier transform. For the AR2 model, the only coefficients that were statistically different from zero were the average alpha 1 (a1) for TI, VI, and Vm (a1 = 0.19, 0.29, and 0.15, respectively). However, the power spectra of all parameters often exhibited peaks at low frequency (less than 0.2 cycles/breath) and/or at high frequency (greater than 0.2 cycles/breath), indicative of periodic oscillations. After accounting for the corrupting effects of added oscillations on the a1 estimates, we conclude that 1) breath-to-breath fluctuations of VI, and to a lesser extent TI and Vm, exhibit a first-order autoregressive structure such that fluctuations of each breath are positively correlated with those of immediately preceding breaths and 2) the correlated components of variability in TE are mostly due to discrete high- and/or low-frequency oscillations with no underlying autoregressive structure. We propose that the autoregressive structure of VI, TI, and Vm during spontaneous breathing in stage 2 sleep may reflect either a central neural mechanism or the effects of noise in respiratory chemical feedback loops; the presence of low-frequency oscillations, seen more often in Vm, suggests possible instability in the chemical feedback loops. Mechanisms of high-frequency periodicities, seen more often in TE, are unknown.

Spectral analysis of the EMG and diaphragmatic muscle fatigue during periodic breathing in infants

1985 ◽  
Vol 58 (3) ◽  
pp. 830-833 ◽  
Author(s):  
S. T. Nugent ◽  
J. P. Finley
Keyword(s):  
Spectral Analysis ◽  
Muscle Fatigue ◽  
Preterm Infants ◽  
High Frequency ◽  
Power Spectra ◽  
Low Frequency ◽  
Periodic Breathing ◽  
Full Term ◽  
Diaphragmatic Muscle ◽  
Normal Breathing

Periodic breathing occurs commonly in full-term and preterm infants. The mechanisms which switch breathing on and off within a cycle of periodic breathing are not certain. Since immature infants may experience diaphragmatic muscle fatigue, one potential switching mechanism is fatigue. Power spectra of the electromyogram, uncontaminated by the electrocardiograph artifact, were studied for evidence of diaphragmatic muscle fatigue during spontaneous periodic breathing in infants. A fall in the high-frequency (103–600 Hz) power and an increase in the low-frequency (23–47 Hz) power during periodic as compared with normal breathing would indicate fatigue. This effect was not observed in any of the infants studied. Hence, there is no evidence that periodic breathing is the result of diaphragmatic muscle fatigue. This finding suggests that the effect of drugs such as theophylline in eliminating periodic breathing may be unrelated to the fact that they also reduce fatigue.

Hyperinsulinemia produces cardiac vagal withdrawal and nonuniform sympathetic activation in normal subjects

1999 ◽  
Vol 276 (1) ◽  
pp. R178-R183 ◽  
Author(s):  
Philippe Van De Borne ◽  
Martin Hausberg ◽  
Robert P. Hoffman ◽  
Allyn L. Mark ◽  
Erling A. Anderson
Keyword(s):  
High Frequency ◽  
Sympathetic Activity ◽  
Sympathetic Nerve Activity ◽  
Muscle Sympathetic Nerve Activity ◽  
Low Frequency ◽  
Power Spectral Analysis ◽  
Normal Subjects ◽  
Power Spectral ◽  
Low Frequency Variability ◽  
High Frequency Variability

The exact mechanisms for the decrease in R-R interval (RRI) during acute physiological hyperinsulinemia with euglycemia are unknown. Power spectral analysis of RRI and microneurographic recordings of muscle sympathetic nerve activity (MSNA) in 16 normal subjects provided markers of autonomic control during 90-min hyperinsulinemic/euglycemic clamps. By infusing propranolol and insulin ( n = 6 subjects), we also explored the contribution of heightened cardiac sympathetic activity to the insulin-induced decrease in RRI. Slight decreases in RRI ( P < 0.001) induced by sevenfold increases in plasma insulin could not be suppressed by propranolol. Insulin increased MSNA by more than twofold ( P < 0.001), decreased the high-frequency variability of RRI ( P< 0.01), but did not affect the absolute low-frequency variability of RRI. These results suggest that reductions in cardiac vagal tone and modulation contribute at least in part to the reduction in RRI during hyperinsulinemia. Moreover, more than twofold increases in MSNA occurring concurrently with a slight and not purely sympathetically mediated tachycardia suggest regionally nonuniform increases in sympathetic activity during hyperinsulinemia in humans.

Heart rate variability and dissociation in panic disorder

2011 ◽  
Vol 26 (S2) ◽  
pp. 147-147
Author(s):  
T. Diveky ◽  
D. Kamaradova ◽  
A. Grambal ◽  
K. Latalova ◽  
J. Prasko ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Panic Disorder ◽  
Frequency Band ◽  
High Frequency ◽  
Power Spectra ◽  
Low Frequency ◽  
Very Low Frequency ◽  
Significant Difference ◽  
Before And After

The aim of our study is to measure very low frequency band (VLF), low frequency band (LF) and high frequency band (HF) components of R-R interval during orthostatic experiment in panic disorder patients before and after treatment.MethodsWe assessed heart rate variability in 19 patients with panic disorder before and after 6-weeks treatment with antidepressants combined with CBT and 18 healthy controls. They were regularly assessed on the CGI, BAI and BDI. Heart rate variability was assessed during 5 min standing, 5 min supine and 5 min standing positions before and after the treatment. Power spectra were computed using a fast Fourier transformation for very low frequency - VLF (0.0033 - 0.04 Hz), low-frequency - LF (0.04-0.15 Hz) and high frequency - HF (0.15-0.40 Hz) powers.Results19 panic disorder patients entered a 6-week open-label treatment study with combination of SSRI and cognitive behavioral therapy. A combination of CBT and pharmacotherapy proved to be the effective treatment of patients. They significantly improved in all rating scales. There were highly statistical significant differences between panic patients and control group in all components of power spectral analysis in 2nd and in two component of 3rd (LF and HF in standing) positions. There was also statistically significant difference between these two groups in LF/HF ratio in supine position (2nd). During therapy there was tendency to increasing values in all three positions in components of HRV power spectra, but there was only statistically significant increasing in HF1 component.Supported by project IGA MZ ČR NS 10301-3/2009

scholarly journals Stochastic Subgrid-Scale Ocean Mixing: Impacts on Low-Frequency Variability

2017 ◽  
Vol 30 (13) ◽  
pp. 4997-5019 ◽  
Author(s):  
Stephan Juricke ◽  
Tim N. Palmer ◽  
Laure Zanna
Keyword(s):  
Southern Ocean ◽  
Interannual Variability ◽  
Time Scales ◽  
High Frequency ◽  
Low Frequency ◽  
Global Ocean ◽  
Small Scale ◽  
Subgrid Scale ◽  
Low Frequency Variability ◽  
Ocean Models

In global ocean models, the representation of small-scale, high-frequency processes considerably influences the large-scale oceanic circulation and its low-frequency variability. This study investigates the impact of stochastic perturbation schemes based on three different subgrid-scale parameterizations in multidecadal ocean-only simulations with the ocean model NEMO at 1° resolution. The three parameterizations are an enhanced vertical diffusion scheme for unstable stratification, the Gent–McWilliams (GM) scheme, and a turbulent kinetic energy mixing scheme, all commonly used in state-of-the-art ocean models. The focus here is on changes in interannual variability caused by the comparatively high-frequency stochastic perturbations with subseasonal decorrelation time scales. These perturbations lead to significant improvements in the representation of low-frequency variability in the ocean, with the stochastic GM scheme showing the strongest impact. Interannual variability of the Southern Ocean eddy and Eulerian streamfunctions is increased by an order of magnitude and by 20%, respectively. Interannual sea surface height variability is increased by about 20%–25% as well, especially in the Southern Ocean and in the Kuroshio region, consistent with a strong underestimation of interannual variability in the model when compared to reanalysis and altimetry observations. These results suggest that enhancing subgrid-scale variability in ocean models can improve model variability and potentially its response to forcing on much longer time scales, while also providing an estimate of model uncertainty.

scholarly journals Acoustical study on spectral characteristics of teachers’ English vocalization

2021 ◽  
Vol 257 ◽  
pp. 02027
Author(s):  
Yao Xiao ◽  
Yue-Zhe Zhao
Keyword(s):  
Frequency Band ◽  
High Frequency ◽  
Sound Pressure ◽  
Spectral Characteristics ◽  
Low Frequency ◽  
Sound Intensity ◽  
English Teaching ◽  
Female Teachers ◽  
Acoustical Study ◽  
Second Peak

With the increase of English teaching courses, the specific vocal characteristics of teachers in English teaching are studied and used as the basic data for the design of teachers’ spatial acoustics. In order to study this problem, three different English voice materials were read in a anechoic room under three sound intensity levels: large, medium and small, respectively, so as to analyse the equivalent continuous sound pressure level (SPL) and frequency characteristic curves of male and female teachers at 0.3m. In the low frequency band, the SPL increases as the frequency increases. In the mid-frequency band, the SPL reaches the first peak, then decreases briefly as the frequency increases, then increases again to the second peak, and then decreases again briefly as the frequency increases. Then increase again to the third peak. In the high frequency band, the SPL decreases as the frequency increases.

scholarly journals Spectral characteristics of satellite links at ka band in the southern tropical region of India

2017 ◽  
Vol 7 (1.1) ◽  
pp. 631
Author(s):  
Sravya Velaga ◽  
Jagadeesh Korrapati ◽  
Chaithanya Krishna B ◽  
Vandana Matti ◽  
K Ch. Sri Kavya ◽  
...  
Keyword(s):  
High Frequency ◽  
Spectral Characteristics ◽  
Low Frequency ◽  
Radiowave Propagation ◽  
Tropical Region ◽  
Multimedia Services ◽  
System Availability ◽  
Ka Band ◽  
Frequency Bands ◽  
Satellite Links

The objective of this study was to perform a spectral analysis of the Ka band propagation channel. Now a day, because of the expanding interest of the end users for multimedia services which require vast bandwidth, and because of the immersion of low frequency bands, for example, L, S, C bands, and Ku band in close future, satellite media transmission systems are moving to higher frequency bands. In such high frequency bands, the presence of the atmosphere strongly affects radiowave propagation and attenuation effects become so severe that system availability would not be sufficient if systems were designed in a conventional way with a static propagation margin. 

scholarly journals Spectral Characteristics of the Response of the Meridional Overturning Circulation to Deep-Water Formation

2005 ◽  
Vol 35 (10) ◽  
pp. 1813-1825 ◽  
Author(s):  
Julie Deshayes ◽  
Claude Frankignoul
Keyword(s):  
High Frequency ◽  
Spectral Characteristics ◽  
Low Frequency ◽  
Meridional Overturning Circulation ◽  
Thermocline Depth ◽  
Deep Water Formation ◽  
Meridional Transport ◽  
Overturning Circulation ◽  
Water Formation ◽  
Meridional Overturning

Abstract The response of the upper limb of the meridional overturning circulation to the variability of deep-water formation is investigated analytically with a linear, reduced-gravity model in basins of simple geometry. The spectral characteristics of the model response are first derived by prescribing white-noise fluctuations in the meridional transport at the northern boundary. Although low-frequency basin modes are solutions to the eigenproblem, they are too dissipative to be significantly excited by the boundary forcing, and the thermocline depth response has a red spectrum with no prevailing time scale other than that of a high-frequency equatorial mode, only flattening at the millennial time scale because of vertical diffusivity. The meridional transport is asymmetric about the equator because the northern part of the basin is directly influenced by the boundary forcing while the southern part is mostly set in motion by long Rossby waves. This results in the equator acting as a low-pass filter for the Southern Hemisphere, which clarifies the so-called buffering effect of the equator. In a basin connected by a southern circumpolar channel, the thermocline depth and the transport spectra are redder than in the forced basin and, when a somewhat more realistic stochastic forcing derived from general circulation model simulations is considered, the variability is strongly reduced at high frequency. The linear model qualitatively explains several features of the low-frequency variability of the meridional overturning circulation in climate models, such as its red spectrum and its larger intensity in the North Atlantic Ocean.

scholarly journals Quantifying the Snowfall Detection Performance of the GPM Microwave Imager Channels over Land

2017 ◽  
Vol 18 (3) ◽  
pp. 729-751 ◽  
Author(s):  
Yalei You ◽  
Nai-Yu Wang ◽  
Ralph Ferraro ◽  
Scott Rudlosky
Keyword(s):  
Water Vapor ◽  
High Frequency ◽  
Model Simulation ◽  
Low Frequency ◽  
Detection Performance ◽  
Probability Of Detection ◽  
Frequency Channel ◽  
Microwave Imager ◽  
Type 3 ◽  
Global Precipitation

Abstract This study uses Global Precipitation Measurement (GPM) Microwave Imager (GMI) and Ka-precipitation radar observations to quantify the snowfall detection performance for different channel (frequency) combinations. Results showed that the low-frequency-channel set contains limited snow detection information with a 0.34 probability of detection (POD). Much better performance is evident using the high-frequency channels (i.e., POD = 0.74). In addition, if only one high-frequency channel is allowed to be added to the low-frequency-channel set, adding the 183 ± 3 GHz channel presents the largest POD improvement (from 0.34 to 0.50). However, this does not imply that the water vapor is the key information for snowfall detection. Only using the high-frequency water vapor channels showed poor snowfall detection with POD at 0.13. Further analysis of all 8191 possible GMI channel combinations showed that the 166-GHz channels are indispensable for any channel combination with POD greater than 0.70. This suggests that the scattering signature, not the water vapor effect, is essential for snowfall detection. Data analysis and model simulation support this explanation. Finally, the GPM constellation radiometers are grouped into six categories based on the channel availability and their snowfall detection capability is estimated, using channels available on GMI. It is found that type-4 radiometer (all channels) has the best snowfall detection performance with a POD of 0.77. The POD values are only slightly smaller for the type-3 radiometer (high-frequency channels) and type-5 radiometer (all channels except 183 channels).

Selective loss of high-frequency oscillations in phrenic and hypoglossal activity in the decerebrate rat during gasping

2006 ◽  
Vol 291 (5) ◽  
pp. R1414-R1429 ◽  
Author(s):  
Vitaliy Marchenko ◽  
Robert F. Rogers
Keyword(s):  
Power Distribution ◽  
High Frequency ◽  
Spectral Characteristics ◽  
Low Frequency ◽  
High Amplitude ◽  
Medial Branch ◽  
Adult Rats ◽  
Time Frequency ◽  
High Frequency Oscillations ◽  
Nerve Recordings

Respiratory motor outputs contain medium-(MFO) and high-frequency oscillations (HFO) that are much faster than the fundamental breathing rhythm. However, the associated changes in power spectral characteristics of the major respiratory outputs in unanesthetized animals during the transition from normal eupneic breathing to hypoxic gasping have not been well characterized. Experiments were performed on nine unanesthetized, chemo- and barodenervated, decerebrate adult rats, in which asphyxia elicited hyperpnea, followed by apnea and gasping. A gated fast Fourier transform (FFT) analysis and a novel time-frequency representation (TFR) analysis were developed and applied to whole phrenic and to medial branch hypoglossal nerve recordings. Our results revealed one MFO and one HFO peak in the phrenic output during eupnea, where HFO was prominent in the first two-thirds of the burst and MFO was prominent in the latter two-thirds of the burst. The hypoglossal activity contained broadband power distribution with several distinct peaks. During gasping, two high-amplitude MFO peaks were present in phrenic activity, and this state was characterized by a conspicuous loss in HFO power. Hypoglossal activity showed a significant reduction in power and a shift in its distribution toward lower frequencies during gasping. TFR analysis of phrenic activity revealed the increasing importance of an initial low-frequency “start-up” burst that grew in relative intensity as hypoxic conditions persisted. Significant changes in MFO and HFO rhythm generation during the transition from eupnea to gasping presumably reflect a reconfiguration of the respiratory network and/or alterations in signal processing by the circuitry associated with the two motor pools.

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