The Geostationary Lightning Mapper (GLM) on the Geostationary Operational Environmental Satellite‐R series of weather satellites provides point geolocations of lightning flashes that are further comprised of a hierarchy of geolocated groups and events. This study describes an open‐source method for reconstruction of imagery from those point detections that retains the quantitative physical measurements made by GLM, restores the spatial footprint of the events, and connects that spatial footprint to the groups and flashes. Meteorological signals are demonstrated to be more apparent in the gridded imagery than in the point detections, leading to their adoption by the United States National Weather Service as the first GLM product available in their real‐time displays. Analysis of a mesoscale convective system over Argentina confirms that there is a class of propagating lightning observed by GLM (distinct from that in storm cores) that can be visualized and quantified using our imagery‐based approach.

For the first time, we demonstrate modulation domain image filters that achieve perceptually motivated image processing goals by directly manipulating the FM functions in a multi-component AM-FM image model. The action of previous modulation domain filters has been limited to modification of the AM functions based on the values of the AM and FM functions. This is because reconstruction of the modified phase from the filtered frequency modulation vectors was an unsolved problem. Here, we present two new algorithms capable of reconstructing the phase from the processed frequencies, one based on a least squares solution of the discrete Poisson equation with Neumann boundary condition and one based on cubic tensor product spline integration. New modulation domain FM filters are designed to modify both the orientations and magnitudes of the visually important emergent image frequency vectors. In our most dramatic example, we demonstrate an FM filter that autonomously changes the stripes on the pants in the well known Barbara image from vertical to horizontal.Index Terms-AM-FM image models, AM-FM image filters, modulation domain signal processing, multicomponent models

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In an effort to monitor and alleviate roadway traffic conditions, the Oklahoma Department of Transportation (ODOT) has deployed a statewide Intelligent Transportation Systems (ITS) architecture consisting of a large number of devices, including cameras, dynamic message signs, and speed sensors along Oklahoma highways. These devices are connected throughout a private ITS fiber-optic network to controlling stations located at stakeholder agencies statewide, forming a virtual Traffic Management Center (TMC). This decentralized approach allows individual consoles on the virtual TMC to display and control reachable devices even if portions of the network become disconnected. Enabling this fault-tolerant design is a novel peer-based communications protocol. The communication system is dynamically configured and automatically resolves communications regardless of network configuration. This paper introduces this robust peer-based approach and describes its implementation within the Oklahoma virtual TMC. Results of this implementation of the system are also presented.

While storm identification and tracking algorithms are used both operationally and in research, there exists no single standard technique to objectively determine performance of such algorithms. Thus, a comparative skill score is developed herein which consists of four parameters, three of which constitute the quantification of storm attributes – size consistency, linearity of tracks, and mean track duration – and the fourth which correlates performance to an optimal post-event reanalysis. The skill score is a cumulative sum of each of the parameters normalized from zero to one amongst the compared algorithms, such that a maximum skill score of four can be obtained. The skill score is intended to favor algorithms which are efficient at severe storm detection, i.e., high-scoring algorithms should detect storms that have higher current or future severe threat and minimize detection of weaker, short-lived storms with low severe potential. The skill score is shown to be capable of successfully ranking a large number of algorithms, both between varying settings within the same base algorithm and between distinct base algorithms. Through a comparison with manually-created user datasets, high-scoring algorithms are verified to match well with hand analyses, demonstrating appropriate calibration of skill score parameters.

We introduce a new phase unwrapping algorithm that makes it possible to obtain high fidelity image reconstructions from computed AM-FM models without the need for storing multiple boundary conditions such as phase samples from the original image in order to reconstruct the phase from the estimated FM field. This is important to the development of general modulation domain filters because the phase initial conditions are unknown after a filtering operation that modifies the FM functions, making reconstruction of the filtered image hard. In the new approach, frequency information from an initial least squares estimate of the unwrapped phase is used to guide selection of refined phase values that are congruent with the principal phase of the image. The selection process applies a queue-based region growing strategy to compute the final unwrapped phase solution with sparse branch cuts that tend to be placed only in areas with low visual impact. This final solution for the unwrapped phase leads to new solutions for the frequency modulations of the image that are in good agreement with visual perception and provide high quality reconstruction of the original image.

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