Advanced open-source tools for detecting and analyzing peaks in cloud radar Doppler spectra

2021 ◽  
Author(s):  
Teresa Vogl ◽  
Martin Radenz ◽  
Heike Kalesse-Los
Keyword(s):  
Supercooled Liquid ◽  
Detection Algorithm ◽  
Supervised Machine Learning ◽  
Doppler Spectrum ◽  
The Past ◽  
Cloud Radar ◽  
Depolarisation Ratio ◽  
Instrument Type ◽  
Peak Detection Algorithm ◽  
Binary Tree Structure

<p>Cloud radar Doppler spectra contain vertically highly resolved valuable information about the hydrometeors present in the cloud. A mixture of different hydrometeor types can lead to several peaks in the Doppler spectrum due to their different fall speeds, giving a hint about the size/ density/ number of the respective particles. Tools to separate and interpret peaks in cloud radar Doppler spectra have been developed in the past, but their application is often limited to certain radar settings, or the code not freely available to other users.</p> <p>We here present the effort of joining two methods, which have been developed and published (Radenz et al., 2019; Kalesse et al., 2019) with the aim to make them insensitive to instrument type and settings, and available on GitHub, and applicable to all cloud radars which are part of the ACTRIS CloudNet network.</p> <p>A supervised machine learning peak detection algorithm (PEAKO, Kalesse et al., 2019) is used to derive the optimal parameters to detect peaks in cloud radar Doppler spectra for each set of instrument settings. In the next step, these parameters are used by peakTree (Radenz et al., 2019), which is a tool for converting multi-peaked (cloud) radar Doppler spectra into a binary tree structure. PeakTree yields the (polarimetric) radar moments of each detected peak and can thus be used to classify the hydrometeor types. This allows us to analyze Doppler spectra of different cloud radars with respect to, e.g. the occurrence of supercooled liquid water or ice needles/columns with high linear depolarisation ratio (LDR).</p>

scholarly journals An automated cloud detection method based on the green channel of total-sky visible images

2015 ◽  
Vol 8 (11) ◽  
pp. 4671-4679 ◽  
Author(s):  
J. Yang ◽  
Q. Min ◽  
W. Lu ◽  
W. Yao ◽  
Y. Ma ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Mie Scattering ◽  
Brightness Distribution ◽  
Adaptive Threshold ◽  
Cloud Detection ◽  
Blue Band ◽  
Green Channel ◽  
The Past ◽  
Visible Images ◽  
Cloud Mask

Abstract. Obtaining an accurate cloud-cover state is a challenging task. In the past, traditional two-dimensional red-to-blue band methods have been widely used for cloud detection in total-sky images. By analyzing the imaging principle of cameras, the green channel has been selected to replace the 2-D red-to-blue band for detecting cloud pixels from partly cloudy total-sky images in this study. The brightness distribution in a total-sky image is usually nonuniform, because of forward scattering and Mie scattering of aerosols, which results in increased detection errors in the circumsolar and near-horizon regions. This paper proposes an automatic cloud detection algorithm, "green channel background subtraction adaptive threshold" (GBSAT), which incorporates channel selection, background simulation, computation of solar mask and cloud mask, subtraction, an adaptive threshold, and binarization. Five experimental cases show that the GBSAT algorithm produces more accurate retrieval results for all these test total-sky images.

scholarly journals On the intermittency of orographic gravity wave hotspots and its importance for middle atmosphere dynamics

2020 ◽  
Author(s):  
Ales Kuchar ◽  
Petr Sacha ◽  
Roland Eichinger ◽  
Christoph Jacobi ◽  
Petr Pisoft ◽  
...  
Keyword(s):  
Climate Model ◽  
Middle Atmosphere ◽  
Model Simulation ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Dynamics Simulation ◽  
Dynamical Structure ◽  
Wave Forcing ◽  
Peak Detection Algorithm ◽  
Opposing Effects

Abstract. When orographic gravity waves (OGWs) break, they dissipate their momentum and energy and thereby influence the thermal and dynamical structure of the atmosphere. This OGW forcing mainly takes place in the middle atmosphere. It is zonally asymmetric and strongly intermittent. So-called OGW hotspot regions have been shown to exert a large impact on the total wave forcing, in particular in the lower stratosphere (LS). Motivated by this we investigate the asymmetrical distribution of the three-dimensional OGW drag (OGWD) for selected hotspot regions in the specified dynamics simulation of the chemistry-climate model CMAM (Canadian Middle Atmosphere Model) for the period 1979–2010. As an evaluation, we first compare zonal mean OGW fluxes and GW drag (GWD) of the model simulation with observations and reanalyses in the northern hemisphere. We find an overestimation of GW momentum fluxes and GWD in the model's LS, presumably attributable to the GW parameterizations which are tuned to correctly represent the dynamics of the southern hemisphere. In the following, we define three hotspot regions which are of particular interest for OGW studies, namely the Himalayas, the Rocky Mountains and East Asia. The GW drags in these hotspot regions emerge as strongly intermittent, a result that can also quantitatively be corroborated with observational studies. Moreover, a peak-detection algorithm is applied to capture the intermittent and zonally asymmetric character of OGWs breaking in the LS and to assess composites for the three hotspot regions. This shows that LS peak OGW events can have opposing effects on the upper stratosphere and mesosphere depending on the hotspot region. Our analysis constitutes a new method for studying the intermittency of OGWs, thereby facilitating a new possibility to assess the effect of particular OGW hotspot regions on middle atmospheric dynamics.

Role of vasopressin in sympathetic response to paraventricular nucleus stimulation in anesthetized rats

1994 ◽  
Vol 266 (1) ◽  
pp. R228-R236 ◽  
Author(s):  
S. C. Malpas ◽  
J. H. Coote
Keyword(s):  
Paraventricular Nucleus ◽  
Neuronal Cell ◽  
Detection Algorithm ◽  
Homocysteic Acid ◽  
Renal Sympathetic Nerve Activity ◽  
Anesthetized Rats ◽  
Neuronal Cell Bodies ◽  
Peak Detection Algorithm ◽  
Glass Micropipette

Vasopressin may play an extrahypothalamic role in the central control of the cardiovascular system, specifically acting as a spinal neurotransmitter in the pathway where the paraventricular nucleus (PVN) alters sympathetic outflow. In this study, the effect of stimulating neuronal cell bodies in the PVN on renal sympathetic nerve activity (RSNA) and the possible involvement of vasopressin in the pathway was investigated in anesthetized rats. The PVN was stimulated by microinjection with 0.2 M D,L-homocysteic acid via a glass micropipette, and the hemodynamic and sympathetic responses were recorded. A computerized sympathetic peak-detection algorithm was applied to recordings of sympathetic discharges to retrieve information about the characteristics of RSNA during PVN stimulation. The algorithm scanned the series of RSNA voltages for significant increases followed by significant decreases in a small cluster of voltage values. Once each synchronized RSNA peak had been detected, its corresponding amplitude and peak-to-peak interval were calculated. PVN stimulation consistently increased the amplitude of RSNA (mean 30 +/- 5.6% over control), arterial pressure, and the peak-to-peak interval of discharges. A V1 vasopressin antagonist intrathecally administered as a 500-pmol dose was subsequently able to completely block the hemodynamic response (blood pressure increase of 14 +/- 5%) and a 35 +/- 6% increase in RSNA in response to PVN stimulation and intrathecal vasopressin. Thus spinal vasopressin is likely to be a neurotransmitter involved in the cardiovascular regulation involving the PVN.

Fiber Bragg Grating Spectrum Peak-Detection Algorithm Based on Difference of Gaussian

2021 ◽  
Vol 58 (7) ◽  
pp. 0706002
Author(s):  
蔺彦章 Lin Yanzhang ◽  
刘毅 Liu Yi ◽  
潘玉恒 Pan Yuheng ◽  
李国燕 Li Guoyan
Keyword(s):  
Fiber Bragg Grating ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Bragg Grating ◽  
Peak Detection Algorithm ◽  
Difference Of Gaussian

Research of Peak-Detection Algorithm Based on Absolute Wavelength Calibration of Excimer Laser

2018 ◽  
Vol 45 (7) ◽  
pp. 0701003
Author(s):  
袁靖超 Yuan Jingchao ◽  
赵江山 Zhao Jiangshan ◽  
李慧 Li Hui ◽  
刘广义 Liu Guangyi
Keyword(s):  
Excimer Laser ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Wavelength Calibration ◽  
Peak Detection Algorithm

scholarly journals Low Resource Complexity R-peak Detection Based on Triangle Template Matching and Moving Average Filter

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3997 ◽  
Author(s):  
Tam Nguyen ◽  
Xiaoli Qin ◽  
Anh Dinh ◽  
Francis Bui
Keyword(s):  
Template Matching ◽  
Moving Average ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Moving Average Filter ◽  
Ecg Signals ◽  
Low Resource ◽  
Peak Detection Algorithm ◽  
Robustness To Noise ◽  
Average Filter

A novel R-peak detection algorithm suitable for wearable electrocardiogram (ECG) devices is proposed with four objectives: robustness to noise, low latency processing, low resource complexity, and automatic tuning of parameters. The approach is a two-pronged algorithm comprising (1) triangle template matching to accentuate the slope information of the R-peaks and (2) a single moving average filter to define a dynamic threshold for peak detection. The proposed algorithm was validated on eight ECG public databases. The obtained results not only presented good accuracy, but also low resource complexity, all of which show great potential for detection R-peaks in ECG signals collected from wearable devices.

scholarly journals Fully Automated R-peak Detection Algorithm (FLORA) for fetal magnetoencephalographic data

2019 ◽  
Vol 173 ◽  
pp. 35-41 ◽  
Author(s):  
Katrin Sippel ◽  
Julia Moser ◽  
Franziska Schleger ◽  
Hubert Preissl ◽  
Wolfgang Rosenstiel ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Peak Detection ◽  
Peak Detection Algorithm
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