T. D. Hamlin

Katrina and Rita were lit up with lightning

Hurricanes generally produce very little lightning activity compared to other noncyclonic storms, and lightning is especially sparse in the eye wall and inner regions within tens of kilometers surrounding the eye [Molinari et al., 1994, 1999]. (The eye wall is the wall of clouds that encircles the eye of the hurricane.) Lightning can sometimes be detected in the outer, spiral rainbands, but the lightning occurrence rate varies significantly from hurricane to hurricane as well as within an individual hurricanes lifetime. Hurricanes Katrina and Rita hit the U.S. Gulf coasts of Louisiana, Mississippi, and Texas, and their distinctions were not just limited to their tremendous intensity and damage caused. They also differed from typical hurricanes in their lightning production rate.

A terrestrial gamma ray flash observed from an aircraft

On 21 August 2009, the Airborne Detector for Energetic Lightning Emissions (ADELE), an array of six gamma-ray detectors, detected a brief burst of gamma rays while flying aboard a Gulfstream V jet near two active thunderstorm cells. The duration and spectral characteristics of the event are consistent with the terrestrial gamma ray flashes (TGFs) seen by instruments in low Earth orbit. A long-duration, complex +IC flash was taking place in the nearer cell at the same time, at a distance of ~10 km from the plane. The sferics that are probably associated with this flash extended over 54 ms and included several ULF pulses corresponding to charge moment changes of up to 30 C km, this value being in the lower half of the range of sferics associated with TGFs seen from space. Monte Carlo simulations of gamma ray propagation in the Earths atmosphere show that a TGF of normal intensity would, at this distance, have produced a gamma ray signal in ADELE of approximately the size and spectrum that was actually observed. We conclude that this was the first detection of a TGF from an aircraft. We show that because of the distance, ADELEs directional and spectral capabilities could not strongly constrain the source altitude of the TGF but that such constraints would be possible for TGFs detected at closer range.

Space- and Ground-Based Studies of Lightning Signatures

This article provides a brief survey of the space- and ground-based stud- ies of lightning performed by investigators at Los Alamos National Laboratory (LANL). The primary goal of these studies was to further understand unique light- ning signatures known as Narrow Bipolar Events (NBEs). First, an overview is presented of the Fast On-orbit Recording of Transient Events (FORTE) satellite and of the ground-based Los Alamos Sferic Array (LASA). This is followed by a summary of the phenomenology, physics, and meteorological context of NBEs and NBE-related discharges. This article also discusses additional radio frequency and optical observations of lightning made by the FORTE satellite and concludes with an outlook on LANLs growing interest in the use of lightning observations in the study of severe weather and hurricane intensification.

Signal classification of satellite-based recordings of radiofrequency (RF) transients using data-adaptive dictionaries

Ongoing research at Los Alamos National Laboratory (LANL) studies the Earths radiofrequency (RF) background utilizing satellite-based RF observations of terrestrial lightning. The Fast On-orbit Recording of Transient Events (FORTE) satellite provided a rich satellite lightning database, that has been previously used for some event classification. We now develop and implement new event classification capability on the FORTE database using state-of-the-art adaptive signal processing combined with compressive sensing and machine learning techniques. The focus of our work is improved feature extraction using sparse representations in data-adaptive dictionaries. We explore two dictionary approaches: dictionaries learned directly from data, and analytical, over-complete dictionaries. Discriminative dictionaries learned directly from data do not rely on analytical constraints or knowledge about the signal characteristics, and provide sparse representations that can perform well when used with a statistical classifier. Pursuit-type decompositions over analytical, over-complete dictionaries yield sparse representations by design and can work well for signals in the same function class as the dictionary atoms. We present preliminary results of our work and discuss performance and future development.

Adaptive sparse signal processing of on-orbit lightning data using learned dictionaries

For the past two decades, there has been an ongoing research effort at Los Alamos National Laboratory to learn more about the Earth’s radiofrequency (RF) background utilizing satellite-based RF observations of terrestrial lightning. The Fast On-orbit Recording of Transient Events (FORTE) satellite provided a rich RF lighting database, comprising of five years of data recorded from its two RF payloads. While some classification work has been done previously on the FORTE RF database, application of modern pattern recognition techniques may advance lightning research in the scientific community and potentially improve on-orbit processing and event discrimination capabilities for future satellite payloads. We now develop and implement new event classification capability on the FORTE database using state-of-the-art adaptive signal processing combined with compressive sensing and machine learning techniques. The focus of our work is improved feature extraction using sparse representations in learned dictionaries. Conventional localized data representations for RF transients using analytical dictionaries, such as a short-time Fourier basis or wavelets, can be suitable for analyzing some types of signals, but not others. Instead, we learn RF dictionaries directly from data, without relying on analytical constraints or additional knowledge about the signal characteristics, using several established machine learning algorithms. Sparse classification features are extracted via matching pursuit search over the learned dictionaries, and used in conjunction with a statistical classifier to distinguish between lightning types. We present preliminary results of our work and discuss classification scenarios and future development.