A long wave transverse electric‐transverse magnetic noise prediction model

Abstract

This paper describes a computerized physical model that predicts both horizontally and vertically polarized noise in the ELF to LF band (10 Hz to 60 kHz). Since naturally occurring radio noise in this band is produced by lightning and propagates to the receiver via the Earth‐ionosphere waveguide, the model starts with average lightning flash density data from which it calculates radiated power for horizontal and vertical noise. Adjustments are made to the radiated power to account for seasonal and latitudinal differences in the lightning processes. The noise power is then integrated over fairly large geographic areas to formulate horizontal and vertical equivalent noise transmitters. The power radiated from each of these transmitters is propagated to the receiver location using standard anisotropic long wave propagation algorithms and well‐known models of the Earth‐ionosphere waveguide. From the received power the model predicts RMS noise, standard deviation, voltage deviation VD, and the amplitude probability distribution of the noise for both polarizations. Since the model is based on theory, it can also predict these parameters under disturbed ionospheric conditions. The model's generally good agreement with RMS noise data is demonstrated.

VLF/ELF sferic evidence for in‐cloud discharge activity producing sprites

“…One of the keys for understanding the source of the sferic cluster is its characteristic in the frequency domain. Two types of lightning strokes can be identified from the VLF sferic waveform, i.e., a return stroke and a bipolar discharge [ Warber and Field , 1995]. The return stroke is one of the CG discharge processes associated with strong electromagnetic radiation whose spectral peak in intensity is around several kHz in the power spectrum.…”

Seasonal variations of globally measured ELF/VLF radio noise

“…The radiometers were primarily developed to obtain new information in support of defense communications and radio navigation systems, and to this end the data have been used to develop long-range ELF/VLF noise prediction models [Warber and Field, 1995]. However, the data have also found use in geophysical and environmental analyses, such as to study polar region events (auroral hiss and polar chorus) and the effects of solar particle events [Fraser- Smith and Turtle, 1993], to define the natural background noise levels at power line frequencies for comparison with those levels created by man-made power generation and distribution systems [Fraser-Smith and Bowen, 1992], and to relate long-term Copyright 1996 by the American Geophysical Union.…”

A Clustering Poisson model for characterizing the interarrival times of sferics

“…In addition, 50 percent of those flashes with five or more strokes have a duration of more than 400 msec. Data histograms in Uman [1969] and Warber and Field [1995] showing typical flash per stroke distributions indicate that the number of flashes per stroke can be reasonably modeled as a geometric random variable; thus the number of impulses per cluster in the Clustering Poisson model is defined to be a geometric random variable as well.…”

“…A number of statistical‐physical noise models (i.e., based on the underlying physical process that creates the noise) have been developed over the past four decades; each can be roughly categorized into one of two types: (1) simple enough to provide concise, closed form answers but only somewhat representative of the true physical situation, and (2) representative of the true physical situation but very complicated to use. The second type often uses specific storm distribution and propagation information and calculates the noise characteristics at a given point numerically [e.g., Warber and Field , 1995], whereas the first type usually assumes independence in space and time of the source distribution (a condition known not to be true in practice). The new model partially bridges the gap between the two types by replacing independence in time with a Clustering Poisson assumption.…”