DOCSLIB.ORG

Cosmic Noise Absorption Signature of Particle Precipitation During Interplanetary Coronal Mass Ejection Sheaths and Ejecta

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Cosmic Noise Absorption Signature of Particle Precipitation During Interplanetary Coronal Mass Ejection Sheaths and Ejecta Ann. Geophys., 38, 557–574, 2020 https://doi.org/10.5194/angeo-38-557-2020 © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License. Cosmic noise absorption signature of particle precipitation during interplanetary coronal mass ejection sheaths and ejecta Emilia Kilpua1, Liisa Juusola2, Maxime Grandin1, Antti Kero3, Stepan Dubyagin2, Noora Partamies4,5, Adnane Osmane1, Harriet George1, Milla Kalliokoski1, Tero Raita3, Timo Asikainen6, and Minna Palmroth1,2 1Department of Physics, University of Helsinki, Helsinki, Finland 2Finnish Meteorological Institute, Helsinki, Finland 3Sodankylä Geophysical Observatory, University of Oulu, Sodankylä, Finland 4Department of Arctic Geophysics, The University Centre in Svalbard, Longyearbyen, Norway 5Birkeland Centre for Space Science, Bergen, Norway 6ReSoLVE Center of Excellence, Space Climate Research Unit, University of Oulu, Oulu, Finland Correspondence: Emilia Kilpua (emilia.kilpua@helsinki.fi) Received: 30 October 2019 – Discussion started: 3 December 2019 Revised: 20 February 2020 – Accepted: 26 February 2020 – Published: 21 April 2020 Abstract. We study here energetic-electron (E > 30 keV) tance of substorms and magnetospheric ultra-low-frequency precipitation using cosmic noise absorption (CNA) during (ULF) waves for enhanced CNA. the sheath and ejecta structures of 61 interplanetary coro- nal mass ejections (ICMEs) observed in the near-Earth so- lar wind between 1997 and 2012. The data come from the Finnish riometer (relative ionospheric opacity meter) chain 1 Introduction from stations extending from auroral (IVA, 65.2◦ N geomag- netic latitude; MLAT) to subauroral (JYV, 59.0◦ N MLAT) Precipitation of high-energy (E > 30 keV) electrons from the latitudes. We find that sheaths and ejecta lead frequently to inner magnetosphere into the Earth’s ionosphere is an inter- enhanced CNA (> 0:5 dB) both at auroral and subauroral esting fundamental plasma process that can have significant latitudes, although the CNA magnitudes stay relatively low consequences on the atmospheric chemistry (e.g. Rodger (medians around 1 dB). Due to their longer duration, ejecta et al., 2010; Andersson et al., 2014; Seppälä et al., 2015; typically lead to more sustained enhanced CNA periods (on Turunen et al., 2016; Newnham et al., 2018). This process average 6–7 h), but the sheaths and ejecta were found to be is also of particular interest for climate models (e.g. Matthes equally effective in inducing enhanced CNA when relative- et al., 2017), but its details and external factors governing occurrence frequency and CNA magnitude were considered. the efficiency of precipitation are currently not well under- Only at the lowest-MLAT station, JYV, ejecta were more stood. The electron precipitation can result from substorm effective in causing enhanced CNA. Some clear trends of injections from the nightside plasma sheet or from the scat- magnetic local time (MLT) and differences between the tering of trapped electrons in the Van Allen radiation belts ejecta and sheaths were found. The occurrence frequency surrounding the Earth. Precipitation can occur also during and magnitude of CNA activity was lowest close to mid- pulsating aurora and exhibit modulation at similar timescales night, while it peaked for the sheaths in the morning and as the auroral emission (e.g. Grandin et al., 2017b). afternoon/evening sectors and for the ejecta in the morning Substorm injections occur predominantly from before and noon sectors. These differences may reflect differences midnight to midnight (e.g. Aminaei et al., 2006; Beharrell in typical MLT distributions of wave modes that precipitate et al., 2015) from geostationary orbit to about 9 Earth radii substorm-injected and trapped radiation belt electrons during (e.g. Spanswick et al., 2010), and electrons then drift around the sheaths and ejecta. Our study also emphasizes the impor- the Earth along the morning side (e.g. Kavanagh et al., 2007; Grandin et al., 2017a). The precipitation signatures also vary Published by Copernicus Publications on behalf of the European Geosciences Union. 558 E. Kilpua et al.: Cosmic noise absorption signature of particle precipitation depending on the substorm phase. The growth phase is char- and dawn sectors (e.g. Malaspina et al., 2016) with the acterized by a relatively weak but intensifying precipita- strongest amplitude recorded at dusk (Kim and Shprits, tion region which extends in longitude (in particular west- 2019). They are believed to result from nonlinear growth of ward) and moves to lower latitudes (e.g. Hargreaves et al., chorus waves when they transfer into the plasmasphere (e.g. 1975; Ranta et al., 1981). Short duration spikes are iden- Bortnik et al., 2008; Hartley et al., 2018). The EMIC waves tified during the onset associated with rapid particle accel- in turn occur predominantly in the duskside of the magneto- eration into the ionosphere (e.g. Hargreaves et al., 2001), sphere close to the plasmapause, as they are generated by while in the growth phase precipitation proceeds to lower anisotropic ring current proton distributions (Zhang et al., latitudes (e.g. Pytte et al., 1976). For both the freshly in- 2016; Saikin et al., 2016). jected substorm electrons and the electrons trapped in the Several statistical studies have highlighted differences in Van Allen belts, the precipitation is considered to be primar- geomagnetic and radiation belt responses and in precipita- ily facilitated through pitch-angle scattering following inter- tion signatures during different large-scale solar-wind drivers action with very-low-frequency (VLF) waves – in particu- (e.g. Huttunen et al., 2002; Borovsky and Denton, 2006; lar, due to cyclotron resonance with the lower-band chorus Potapov, 2013; Kilpua et al., 2015; Asikainen and Ruopsa, whistler waves. The precipitation via whistler waves is con- 2016; Kilpua et al., 2017b; Shen et al., 2017; Grandin et al., sidered most efficient for a few tens of kiloelectronvolts of 2017a; Benacquista et al., 2018), namely interplanetary coro- energy electrons, while they mostly accelerate higher-energy nal mass ejections (ICMEs; e.g. Kilpua et al., 2017a) and (> 500 keV) electrons (e.g. Bortnik and Thorne, 2007; Lam slow–fast stream interaction regions (SIRs; e.g. Richardson, et al., 2010). A high-amplitude chorus can, however, cause 2018). ICMEs are generally known to drive the strongest ge- microburst precipitation of megaelectronvolt trapped elec- omagnetic storms, while SIRs and the following fast streams trons (e.g. Thorne et al., 2005; Osmane et al., 2016; Douma cause more moderate, but sustained, geomagnetic activity et al., 2019). Ultra-low-frequency (ULF) Pc5 waves also (e.g. Borovsky and Denton, 2006; Grandin et al., 2019). At play an important role. The Coroniti and Kennel(1970) the- the geostationary orbit (L =∼ 6:6), SIR-/fast-stream-driven ory predicts that ULF waves may enhance energetic-electron storms more efficiently enhance megaelectronvolt radiation precipitation by periodically increasing the growth rate of belt electrons than ICME-driven storms that cause deeper whistler waves. Observational evidence supporting this the- and longer depletions. Shen et al.(2017), however, showed ory was provided by Motoba et al.(2013) during a conjunc- that at the heart of the outer belt (L =∼ 4–5) and low L shells tion between the Cluster satellites and a ground-based riome- ICMEs enhance more effectively both megaelectronvolt and ter (relative-ionospheric-opacity-meter) station. lower-energy electron fluxes. A comparison of energetic- Another wave mode that is invoked to precipitate electrons particle precipitation during ICMEs and SIRs shows that the through gyroresonance are electromagnetic-ion-cyclotron former tend to produce a higher riometer precipitation sig- (EMIC) waves. They have been shown to be able to pre- nal during the first hours of the geomagnetic storm (Long- cipitate both megaelectronvolt (e.g. Usanova et al., 2014) den et al., 2008), whereas the latter may lead to energetic- and sub-megaelectronvolt electrons (e.g. Blum et al., 2019; electron precipitation for up to 4 d following the storm on- Hendry et al., 2019), although the efficiency of sub- set (Grandin et al., 2017a). A case study of an ICME event megaelectronvolt electron precipitation is still debated. Both by Longden et al.(2007) found that most of the precipitation chorus and EMIC waves occur outside the high-density plas- observed during the related geomagnetic storm was produced masphere, whose outer boundary, the plasmapause, varies by substorm-injected particles on the nightside. On the day- significantly in location with geomagnetic activity and also side, precipitation events occurred in good correlation with with the magnetic local time (MLT) (O’Brien and Moldwin, solar-wind dynamic-pressure pulses. Kavanagh et al.(2012) 2003). Inside the plasmasphere, precipitation occurs primar- performed an extensive epoch analysis of electron precipi- ily due to plasmaspheric hiss (Li et al., 2015). Hiss waves tation during high-speed streams using riometer data. The can scatter electrons over a wide range of energies, but the study showed that precipitation enhances at the arrival of the scattering times increase significantly with increasing elec- SIR, peaks about half a day after its arrival and typically tron energy and decreasing hiss wave power (e.g. Selesnick continues for several days during the following high-speed et al., 2003; Summers et al., 2008; Kavanagh et al., 2018). stream, in agreement
Load more

Recommended publications
  • Spectrum Management Technical Handbook
    NTIA TN 89-2 SPECTRUM MANAGEMENT TECHNICAL HANDBOOK TECHNICAL NOTE SERIES NTIA TECHNICAL NOTE 89-2 SPECTRUM MANAGEMENT TECHNICAL HANDBOOK l U.S. DEPARTMENT OF COMMERCE Robert A. Mosbacher, Secretary Alfred C. Sikes, Assistant Secretary for Communications and Information JULY 1989 ACKNOWLEDGEMENT NT IA acknowledges the efforts of Mark Schellhammer in the completion of Phase I of this handbook (Sections 1-3). Other portions will be added as resources are available. ABSTRACT This handbook is a compilation of the procedures, definitions, relationships and conversions essential to electromagnetic compatibility analysis. It is intended as a reference for NTIA engineers. KEY WORDS CONVERSION FACTORS COUPLING EQUATIONS DEFINITIONS ·TECHNICAL RELATIONSHIPS iii TABLE OF CONTENTS Subsection Page SECTION 1 TECHNICAL TERMS:DEFINITIONS AND NOTATION TERMS AND DEFINITIONS . .. .. 1-1 GLOSSARY OF STANDARD NOTATION . .. 1-13 SECTION 2 TECHNICAL RELATIONSHIPS AND CONVERSION FACTORS INTRODUCTION . .. .. .. 2-1 TRANSMISSION LOSS FOR RADIO LINKS . �. .. .. 2-1 Propagation Loss . .. .. .. 2-1 Transmission Loss ........................................ 2-2 Free-Space Basic Transmission Loss .......................... 2-2 Basic Transmission Loss .................................... 2-2 Loss Relative to Free-Space .................................. 2-2 System Loss . .. 2-3 ; Total Loss . .. .. 2-3 ANTENNA CHARACTERISTICS . .. 2-3 Antenna Directivity ........................................ 2-3 Antenna Gain and Efficiency .................................. 2-3 I
    [Show full text]
  • A Novel Ship Target Detection Algorithm Based on Error Self-Adjustment Extreme Learning Machine and Cascade Classifier
    Cognitive Computation (2019) 11:110–124 https://doi.org/10.1007/s12559-018-9606-5 A Novel Ship Target Detection Algorithm Based on Error Self-adjustment Extreme Learning Machine and Cascade Classifier Wandong Zhang1,2 · Qingzhong Li1 · Q. M. Jonathan Wu2 · Yimin Yang3 · Ming Li1 Received: 30 June 2018 / Accepted: 10 October 2018 / Published online: 24 October 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract High-frequency surface wave radar (HFSWR) has a vital civilian and military significance for ship target detection and tracking because of its wide visual field and large sea area coverage. However, most of the existing ship target detection methods of HFSWR face two main difficulties: (1) the received radar signals are strongly polluted by clutter and noises, and (2) it is difficult to detect ship targets in real-time due to high computational complexity. This paper presents a ship target detection algorithm to overcome the problems above by using a two-stage cascade classification structure. Firstly, to quickly obtain the target candidate regions, a simple gray-scale feature and a linear classifier were applied. Then, a new error self-adjustment extreme learning machine (ES-ELM) with Haar-like input features was adopted to further identify the target precisely in each candidate region. The proposed ES-ELM includes two parts: initialization part and updating part. In the former stage, the L1 regularizer process is adopted to find the sparse solution of output weights, to prune the useless neural nodes and to obtain the optimal number of hidden neurons. Also, to ensure an excellent generalization performance by the network, in the latter stage, the parameters of hidden layer are updated through several iterations using L2 regularizer process with pulled back error matrix.
    [Show full text]
  • Transmitter T (X) Channel E (X) Receiver R
    Analog Communication ( 10EC53 ) Introduction The function of the communication system is to make available at the destination a signal originating at a distant point. This signal is called the desired signal. Unfortunately, during the passage of the signal through the channel and front-end of the receiver, this desired signal gets corrupted by a number of undesired signals. This is referred to as Noise. Noise is any unknown or unwanted signal. Noise is the static you hear in the speaker when you tune any AM or FM receiver to any position between stations. It is also the snow or confetti that is visible on a TV screen. The received signal is modeled as r (t) = s (t) + n (t) Where s (t) is the transmitted/desired signal n (t) is the additive noise/unwanted signal The signal n (t) gets added at the channel. It disturbs the transmission and processing of signal in Communication System. Over which one cannot have a control. In general term it is an unwanted signal that affects a wanted signal. It is a random signal that cannot be represented with a simple equation. But some time can be deterministic components (power supply hum, certain oscillations).Deterministic Components can be eliminated by proper shielding and introduction of notch filters. If their were no noise perfect communication would be possible with minimum transmitted power. At the receiver only amplification of the signal power to the desired level is required. If R (x)=T (x) then e (x)=0 Transmitter Receiver T (x) R (x) If R (x) ≠ T (x) then e (x) ≠ 0.The received signal is corrupted.
    [Show full text]
  • Demodulation, Bit-Error Probability and Diversity Arrangements
    RADIO SYSTEMS – ETIN15 Lecture no: 6 Demodulation, bit-error probability and diversity arrangements Anders J Johansson, Department of Electrical and Information Technology [email protected] 5 April 2017 1 Contents • Receiver noise calculations [Covered briefly in Chapter 3 of textbook!] • Optimal receiver and bit error probability – Principle of maximum-likelihood receiver – Error probabilities in non-fading channels – Error probabilities in fading channels • Diversity arrangements – The diversity principle – Types of diversity – Spatial (antenna) diversity performance 5 April 2017 2 RECEIVER NOISE 5 April 2017 3 Receiver noise Noise sources The noise situation in a receiver depends on several noise sources Noise picked up Wanted by the antenna signal Output signal Analog Detector with requirement circuits on quality Thermal noise 5 April 2017 4 Receiver noise Equivalent noise source To simplify the situation, we replace all noise sources with a single equivalent noise source. Wanted How do we determine Noise free signal N from the other sources? N Output signal C Analog Detector with requirement circuits on quality Noise free Same “input quality”, signal-to-noise ratio, C/N in the whole chain. 5 April 2017 5 Receiver noise Examples • Thermal noise is caused by random movements of electrons in circuits. It is assumed to be Gaussian and the power is proportional to the temperature of the material, in Kelvin. • Atmospheric noise is caused by electrical activity in the atmosphere, e.g. lightning. This noise is impulsive in its nature and below 20 MHz it is a dominating. • Cosmic noise is caused by radiation from space and the sun is a major contributor.
    [Show full text]
  • All You Need to Know About SINAD Measurements Using the 2023
    applicationapplication notenote All you need to know about SINAD and its measurement using 2023 signal generators The 2023A, 2023B and 2025 can be supplied with an optional SINAD measuring capability. This article explains what SINAD measurements are, when they are used and how the SINAD option on 2023A, 2023B and 2025 performs this important task. www.ifrsys.com SINAD and its measurements using the 2023 What is SINAD? C-MESSAGE filter used in North America SINAD is a parameter which provides a quantitative Psophometric filter specified in ITU-T Recommendation measurement of the quality of an audio signal from a O.41, more commonly known from its original description as a communication device. For the purpose of this article the CCITT filter (also often referred to as a P53 filter) device is a radio receiver. The definition of SINAD is very simple A third type of filter is also sometimes used which is - its the ratio of the total signal power level (wanted Signal + unweighted (i.e. flat) over a broader bandwidth. Noise + Distortion or SND) to unwanted signal power (Noise + The telephony filter responses are tabulated in Figure 2. The Distortion or ND). It follows that the higher the figure the better differences in frequency response result in different SINAD the quality of the audio signal. The ratio is expressed as a values for the same signal. The C-MES signal uses a reference logarithmic value (in dB) from the formulae 10Log (SND/ND). frequency of 1 kHz while the CCITT filter uses a reference of Remember that this a power ratio, not a voltage ratio, so a 800 Hz, which results in the filter having "gain" at 1 kHz.
    [Show full text]
  • Signal to Noise Ratio (SNR Or S/N) SNR Is the Ratio of the Signal Power to Noise Power
    JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA KAKINADA – 533 001 , ANDHRA PRADESH GATE Coaching Classes as per the Direction of Ministry of Education GOVERNMENT OF ANDHRA PRADESH Analog Communication 26-05-2020 to 06-07-2020 Prof. Ch. Srinivasa Rao Dept. of ECE, JNTUK-UCE Vizianagaram Analog Communication-Day 7, 01-06-2020 Presentation Outline Noise: • Noise and its types • Thermal Noise • Parameters of Noise • Noise in Baseband Communication systems • Gaussian process and NB Noise • Problems 02-06-2020 Prof.Ch.Srinivasa Rao, JNTUK UCEV 2 Learning Outcomes • At the end of this Session, Student will be able to: • LO 1 : Fundamental aspects of Noise • LO 2 : Different Noise types and its Paraneters • LO 3 : Gaussian Process and ND Noise 02-06-2020 Prof.Ch.Srinivasa Rao, JNTUK UCEV 3 Noise Definitions of Noise: • Noise is unwanted signal that affects wanted signal. • Noise can broadly be defined as any unknown signal that affects the recovery of the desired signal. • Noise is an unwanted signal, which interferes with the original message signal and corrupts the parameters of the message signal. This alteration in the communication process, leads to the message getting altered. It most likely enters at the channel or the receiver. Effect of noise • Degrades system performance (Analog and digital) • Receiver cannot distinguish signal from noise • Efficiency of communication system reduces Most common examples of noise are: • Hiss sound in radio receivers • Buzz sound amidst of telephone conversations • Flicker in television receivers, Noise Sources External Internal Noise Noise Atmospheric Industrial Extraterrestrial Solar Noise Cosmic Noise External Noise : • It is due to Man- made and natural resources • Sources over which we have no control such as thunders, snow fall, lightning etc.
    [Show full text]
  • AN10062 Phase Noise Measurement Guide for Oscillators
    Phase Noise Measurement Guide for Oscillators Contents 1 Introduction ............................................................................................................................................. 1 2 What is phase noise ................................................................................................................................. 2 3 Methods of phase noise measurement ................................................................................................... 3 4 Connecting the signal to a phase noise analyzer ..................................................................................... 4 4.1 Signal level and thermal noise ......................................................................................................... 4 4.2 Active amplifiers and probes ........................................................................................................... 4 4.3 Oscillator output signal types .......................................................................................................... 5 4.3.1 Single ended LVCMOS ........................................................................................................... 5 4.3.2 Single ended Clipped Sine ..................................................................................................... 5 4.3.3 Differential outputs ............................................................................................................... 6 5 Setting up a phase noise analyzer ...........................................................................................................
    [Show full text]
  • CALIFORNIA STATE UNIVERSITY, NORTHRIDGE SPACE COMMUNICATION THEORY and PRACTICE a Graduate Project Submitted in Partial Satisfac
    CALIFORNIA STATE UNIVERSITY, NORTHRIDGE SPACE COMMUNICATION THEORY AND PRACTICE A graduate project submitted in partial satisfaction of the requirements for the degree of the Masterr of Science 1n Engineering by Kristine Patricia Maine January, 1983 The project of Kristine Patricia Maine is approved: CDr. Ste~en A. Gad~ski) (Dr. ~y H. Pettit, Chair) California State University, Northridge ii ACKNOWLEDGEMENTS A deep sense of gratitude is expressed to my academic advisor, Dr. Ray Pettit, for his invaluable counsel and advisement during the course of this study. In addition, I express thanks to The Aerospace Corporation for the use of their facilities and personnel for the completion of this document. iii TABLE OF CONTENTS Title Page Acknowledgement iii Table of Figures v List of Tables vii List of Symbols viii Abstract xi I. Introduction 1 II. Space Communication System Components 2 III. Theory of Link Analysis 8 IV. Applications of Link Analysis 21 v. Summary 41 Appendix A--Signal Losses 45 Appendix B--Space Shuttle Design Model 54 Appendix C--Communication Satellite Systems 62 Footnotes 67 Bi biliography 70 iv TABLE OF FIGURES Figure Number Title 1 Simplified Transponder Block Diagram 3 Showing a Single Channel Transponder 2 TWT Characteristics 4 3 Typical Antenna Beamwidth Pattern 7 4 Satellite Communication System 9 5 Uplink Model 10 6 Downlink Model 13 7 Satellite Communication Transmitter­ 16 to-Receiver with Typical Loss and Noise Sources 8 Zenith Attenuation Values Expected to 18 be Exceeded 2% of the Year at Mid­ latitudes 9 Probability
    [Show full text]
  • Topic 5: Noise in Images
    NOISE IN IMAGES Session: 2007-2008 -1 Topic 5: Noise in Images 5.1 Introduction One of the most important consideration in digital processing of images is noise, in fact it is usually the factor that determines the success or failure of any of the enhancement or recon- struction scheme, most of which fail in the present of significant noise. In all processing systems we must consider how much of the detected signal can be regarded as true and how much is associated with random background events resulting from either the detection or transmission process. These random events are classified under the general topic of noise. This noise can result from a vast variety of sources, including the discrete nature of radiation, variation in detector sensitivity, photo-graphic grain effects, data transmission errors, properties of imaging systems such as air turbulence or water droplets and image quantsiation errors. In each case the properties of the noise are different, as are the image processing opera- tions that can be applied to reduce their effects. 5.2 Fixed Pattern Noise As image sensor consists of many detectors, the most obvious example being a CCD array which is a two-dimensional array of detectors, one per pixel of the detected image. If indi- vidual detector do not have identical response, then this fixed pattern detector response will be combined with the detected image. If this fixed pattern is purely additive, then the detected image is just, f (i; j) = s(i; j) + b(i; j) where s(i; j) is the true image and b(i; j) the fixed pattern noise.
    [Show full text]
  • Dr. Dharmendra Kumar Assistant Professor Department of Electronics and Communication Engineering MMM University of Technology, Gorakhpur–273010
    Dr. Dharmendra Kumar Assistant Professor Department of Electronics and Communication Engineering MMM University of Technology, Gorakhpur–273010. Content of Unit-3 Noise: Source of Noise, Frequency domain, Representation of noise, Linear Filtering of noise, Noise in Amplitude modulation system, Noise in SSB-SC,DSB and DSB-C, Noise Ratio, Noise Comparison of FM and AM, Pre- emphasis and De-emphasis, Figure of Merit. Claude E. Shannon conceptualized the communication theory model in the late 1940s. It remains central to communication study today. Noise is random signal that exists in communication systems Channel is the main source of noise in communication systems Transmitter or Receiver may also induce noise in the system There are mainly 2-types of noise sources Internal noise source (are mainly internal to the communication system) External noise source Noise is an inconvenient feature which affects the system performance. Following are the effects of noise Degrade system performance for both analog and digital systems Noise limits the operating range of the systems Noise indirectly places a limit on the weakest signal that can be amplified by an amplifier. The oscillator in the mixer circuit may limit its frequency because of noise. A system’s operation depends on the operation of its circuits. Noise limits the smallest signal that a receiver is capable of processing The receiver can not understand the sender the receiver can not function as it should be. Noise affects the sensitivity of receivers: Sensitivity is the minimum amount of input signal necessary to obtain the specified quality output. Noise affects the sensitivity of a receiver system, which eventually affects the output.
    [Show full text]
  • Soundproofing 101: a Presentation to the MCAC Environment and Health Subcommittee March 18, 2019 Content
    Soundproofing 101: A presentation to the MCAC Environment and Health Subcommittee March 18, 2019 Content • Overview of How Logan Works • Noise Abatement Program • Soundproofing Regulatory Context • Soundproofing Process • History of the Program and Current Status • Questions/Discussion 2 The FAA uses Logan runways in combinations to safely and efficiently meet demand. Depending on which sets of runways the FAA chooses different communities are overflown Northeast Flow Southwest Flow R 4R\L and 9 R22R\L and 27 Northwest Flow Southeast Flow R33L\32 and 27 R33L\32 and 27 3 Because of Logan’s urban location, Massport has developed a comprehensive noise abatement program for Logan Airport • Noise abatement departure • 24/7 noise complaint line 617-561- procedures 3333 • Late night runway preference • State of the art Noise Monitoring opposite direction operations System • Decibel restriction on R4L • Near live flight tracking on website departures and 22R arrivals • http://www.massport.com/environment/environmental_rep orting/Noise%20Abatement/overview.aspx • Unidirectional/wind restriction use R14/32 • Soundproofing Program for Homes and School • Engine run-up restrictions • Limited time • Specific locations • Towing requirements for certain aircraft repositioning • Encourage use of single engine taxiing and reverse thrust 4 Noise Contours for environmental analysis and soundproofing must be created by using the FAA’s Airport Environmental Data Tool (AEDT) Model • The AEDT is an FAA Model • Critical inputs include number of flights, aircraft
    [Show full text]
  • Sources of Phase Noise and Jitter in Oscillators by Ramon Cerda, Crystek Crystals Corporation
    PAGE • MARCH 2006 FEATURE ARTICLE WWW.MPDIGEST.COM Sources of Phase Noise and Jitter in Oscillators by Ramon Cerda, Crystek Crystals Corporation he output signal of an oscillator, no matter how good it is, will contain Tall kinds of unwanted noises and signals. Some of these unwanted signals are spurious output frequencies, harmon- ics and sub-harmonics, to name a few. The noise part can have a random and/or deterministic noise in both the amplitude and phase of the signal. Here we will look into the major sources of some of these un- wanted signals/noises. Oscillator noise performance is char- acterized as jitter in the time domain and as phase noise in the frequency domain. Which one is preferred, time or frequency domain, may depend on the application. In radio frequency (RF) communications, phase noise is preferred while in digital systems, jitter is favored. Hence, an RF engineer would prefer to address phase noise while a digital engineer wants jitter specified. Note that phase noise and jitter are two linked quantities associated with a noisy oscillator and, in general, as the phase noise increases in the oscillator, so does the jitter. The best way to illustrate this is to examine an ideal signal and corrupt it until the signal starts resembling the real output of an oscillator. The Perfect or Ideal Signal Figure 1 Figure 2 An ideal signal can be described math- ematically as follows: The new time and frequency domain representation is shown in Figure 2 while a vector representation of Equation 3 is illustrated in Figure 3 (a and b.) Equation 1 It turns out that oscillators are usually satu- rated in amplitude level and therefore we can Where: neglect the AM noise in Equation 3.
    [Show full text]