The High-Resolution Ensemble Forecast system (HREF) calibrated thunder guidance is a suite of probabilistic forecast products designed to predict the likelihood of at least one cloud-to-ground (CG) lightning flash within 20 km (12 miles) of a point during a given 1-, 4-, and 24-h time interval. This guidance takes advantage of a combination of storm attribute and environmental fields produced by the convection-allowing HREF to objectively improve upon lightning forecasts generated by the non-convection-allowing Short-Range Ensemble Forecast system (SREF). This study provides an overview of how the HREF calibrated thunder guidance was developed and calibrated to be statistically reliable against observed CG lightning flashes recorded by the National Lightning Detection Network (NLDN). Performance metrics for the 1-, 4-, and 24-h guidance are provided and compared to the respective SREF calibrated probabilistic lightning forecasts. The HREF calibrated thunder guidance has been implemented operationally within the National Weather Service and is now available to the public. Significance StatementThe NOAA Storm Prediction Center has created a suite of new calibrated probabilistic thunderstorm guidance products from a convection-allowing model ensemble, the HREF. The new guidance is a notable improvement over the long-running SREF calibrated thunder guidance and is now operational across the National Weather Service.

MetPy is an open-source, Python-based package for meteorology, providing domain-specific functionality built extensively on top of the robust scientific Python software stack, which includes libraries like NumPy, SciPy, Matplotlib, and xarray. The goal of the project is to bring the weather analysis capabilities of GEMPAK (and similar software tools) into a modern computing paradigm. MetPy strives to employ best practices in its development, including software tests, continuous integration, and automated publishing of web-based documentation. As such, MetPy represents a sustainable, long-term project that fills a need for the meteorological community. MetPy's development is substantially driven by its user community, both through feedback on a variety of open, public forums like Stack Overflow, and through code contributions facilitated by the GitHub collaborative software development platform. MetPy has recently seen the release of version 1.0, with robust functionality for analyzing and visualizing meteorological datasets. While previous versions of MetPy have already seen extensive use, the 1.0 release represents a significant milestone in terms of completeness and a commitment to long-term support for the programming interfaces. This article provides an overview of MetPy's suite of capabilities, including its use of labeled arrays and physical unit information as its core data model, unit-aware calculations, cross sections, skew T and GEMPAK-like plotting, station model plots, and support for parsing a variety of meteorological data formats. The general road map for future planned development for MetPy is also discussed.

This study presents and examines a modern climatology of U.S. severe convective storm frequency using a kernel density estimate to showcase various aspects of climatological risk. Results are presented in the context of specified event probability thresholds that correspond to definitions used at the NOAA/NWS's Storm Prediction Center following a practically perfect hindcast approach. Spatial climatologies presented herein are closely related to previous research. Spatiotemporal changes were examined by splitting the study period (1979-2018) into two 20-yr epochs and calculating deltas. Portions of the southern Great Plains and High Plains have seen a decrease in counts of tornado event threshold probability, whereas increases have been documented in the middle Mississippi River valley region. Large hail, and especially damaging convective wind gusts, have shown increases between the two periods over a majority of the CONUS. To temporally showcase local climatologies, event threshold days are shown for 12 select U.S. cities. Finally, data created and used in this study are available as an open-source repository for future research applications.

The 5th generation (5G) of mobile and wireless communications is expected to have a large impact on society and industry that will go far beyond the information and communications technology (ICT) field. It is expected that 5G will play a key role for the automotive sector and transportation in general, for instance allowing for advanced forms of collaborative driving and the protection of vulnerable road users, or increased efficiency in railroad transportation. This chapter elaborates in more detail on the timing of the book with respect to the 5G developments in 3rd generation partnership project (3GPP) and global initiatives. It stresses the exact scope of the 5G system design. The chapter finally explains the approach pursued in writing this book, and introduces the structure and also presents an outline of this book.

Two extreme wind-driven wildfire events impacted California in late 2017, leading to 46 fatalities and thousands of structures lost. This study characterizes the meteorological and climatological factors that drove and enabled these wildfire events and quantifies their rarity over the observational record. Both events featured key fire-weather metrics that were unprecedented in the observational record that followed a sequence of climatic conditions that enhanced fine fuel abundance and fuel availability. The North Bay fires of October 2017 occurred coincident with strong downslope winds, with a majority of burned area occurring within the first 12 hours of ignition. By contrast, the southern California fires of December 2017 occurred during the longest Santa Ana wind event on record, resulting in the largest wildfire in California's modern history. Both fire events occurred following an exceptionally wet winter that was preceded by a severe four-year drought. Fuels were further preconditioned by the warmest summer and autumn on record in northern and southern California, respectively. Finally, delayed onset of autumn precipitation allowed for critically low dead fuel moistures leading up to the wind events. Fire weather conditions were well forecast several days prior to the fire. However, the rarity of fire-weather conditions that occurred near populated regions, along with other societal factors such as limited evacuation protocols and limited wildfire preparedness in communities outside of the traditional wildland urban interface were key contributors to the widespread wildfire impacts.

Adlen Ksentini is an Associate Professor at the University of Rennes 1, France. He is a member of the INRIA Rennes team Dionysos. He received a M.Sc. in telecommunication and multimedia networking from the University of Versailles. He obtained his Ph.D. degree in computer science from the University of Cergy-Pontoise in 2005, with a dissertation on QoS provisioning in IEEE 802.11-based networks. His other interests include: future Internet networks, mobile networks, QoS, QoE, performance evaluation and multimedia transmission. Dr. Ksentini is involved in several national and European projects on QoS and QoE support in Future wireless and mobile Networks. Dr. Ksentini is a coauthor of over 70 technical journal and international conference papers. He received Best Paper Award from IEEE IC4S 2014, IEEE ICC 2012 and ACM MSWiM 2005. Currently, Dr. Ksentini is the IEEE ComSoc EMEA Director, He was guest editor for IEEE Wireless Communication Magazine and IEEE Communication Magazine Series on Standards. In addition, Dr. Ksentini has been in the technical program committee of major IEEE ComSoc conferences, ICC/Globecom, ICME, WCNC, PIMRC. Dr. Ksentini is a Senior IEEE member.

The focus of this study is the performance of high-density truck platooning achieved with different wireless technologies for vehicle-to-vehicle (V2V) communications. Platooning brings advantages such as lower fuel consumption and better traffic efficiency, which are maximized when the inter-vehicle spacing can be steadily maintained at a feasible minimum. This can be achieved with Cooperative Adaptive Cruise Control, an automated cruise controller that relies on the complex interplay among V2V communications, on-board sensing, and actuation. This work provides a clear mapping between the performance of the V2V communications, which is measured in terms of latency and reliability, and of the platoon, which is measured in terms of achievable inter-truck spacing. Two families of radio technologies are compared: IEEE 802.11p and 3GPP Cellular-V2X (C-V2X). The C-V2X technology considered in this work is based on the Release 14 of the LTE standard, which includes two modes for V2V communications: Mode 3 (base-station-scheduled) and Mode 4 (autonomously-scheduled). Results show that C-V2X in both modes allows for shorter inter-truck distances than IEEE 802.11p due to more reliable communications performance under increasing congestion on the wireless channel caused by surrounding vehicles.

The continuing growth in traffic demands impose the need for the use of frequency bands in the centimeter-wave (cmWave) and millimeter-wave (mmWave) spectrum to increase the throughput. However, this leads to challenges in the system design because of the need for higher cell densification as well as cost-effective deployment. To overcome these challenges the concept of wireless self-backhauling can be used to provide high-capacity network at reduced cost. In this paper, we investigate the performance of self-backhauling with flexible reuse of the resources for access and backhaul in a real-life street canyon scenario with dynamic blockages. The simulation results show that highly flexible access/backhaul and uplink/downlink scheduling with centralized coordination achieves significant throughput gain in median and 5 th percentile of uplink (UL) and downlink (DL). Simulation based results also show that with the limitation of TDD flexibility, self-backhauling mainly provides uplink 5 th percentile throughput gain.

Previous work with observations from the NEXRAD (WSR-88D) network in the United States has shown that the probability of damage from a tornado, as represented by EF-scale ratings, increases as low-level rotational velocity increases. This work expands on previous studies by including reported tornadoes from 2014 to 2015, as well as a robust sample of nontornadic severe thunderstorms [>= 1-in.-(2.54 cm) diameter hail, thunderstorm wind gusts >= 50 kt (25 m s(-1)), or reported wind damage] with low-level cyclonic rotation. The addition of the nontornadic sample allows the computation of tornado damage rating probabilities across a spectrum of organized severe thunderstorms represented by right-moving supercells and quasi-linear convective systems. Dual-polarization variables are used to ensure proper use of velocity data in the identification of tornadic and nontornadic cases. Tornado damage rating probabilities increase as low-level rotational velocity V-rot increases and circulation diameter decreases. The influence of height above radar level (or range from radar) is less obvious, with a muted tendency for tornado damage rating probabilities to increase as rotation (of the same V-rot magnitude) is observed closer to the ground. Consistent with previous work on gate-to-gate shear signatures such as the tornadic vortex signature, easily identifiable rotation poses a greater tornado risk compared to more nebulous areas of cyclonic azimuthal shear. Additionally, tornado probability distributions vary substantially (for similar sample sizes) when comparing the southeast United States, which has a high density of damage indicators, to the Great Plains, where damage indicators are more sparse.

While there is already a common understanding of the services, which 5th generation (5G) mobile communications systems should support, and the key technology components needed to achieve this, there is still the need for further clarification and consensus among key players on the overall 5G radio access network (RAN) architecture and its detailed functional design. The 5G public private partnership (5G PPP) project METIS‐II has the objective to foster exactly this consensus building before and in the early days of the standardisation work for 5G. This paper lists the 5G RAN design requirements as identified in the project and summarises the latest considerations of METIS‐II on the air interface landscape in 5G, the envisioned logical RAN architecture and related aspects, as well as key functional design considerations in 5G, which have found wide endorsement within the project. Copyright © 2016 John Wiley & Sons, Ltd.

While there is clarity on the wide range of applications that are to be supported by 5G cellular communications, and standardization of 5G has now started in 3GPP, there is no conclusion yet on the detailed design of the overall 5G RAN. This article provides a comprehensive overview of the 5G RAN design guidelines, key design considerations, and functional innovations as identified and developed by key players in the field. 1 It depicts the air interface landscape that is envisioned for 5G, and elaborates on how this will likely be harmonized and integrated into an overall 5G RAN, in the form of concrete control and user plane design considerations and architectural enablers for network slicing, supporting independent business-driven logical networks on a common infrastructure. The article also explains key functional design considerations for the 5G RAN, highlighting the difference to legacy systems such as LTE-A and the implications of the overall RAN design.

The Storm Prediction Center (SPC) is developing both a tornadic and severe nontornadic sample of supercell storms from 2014. This latest work is an extension of earlier research which led to the development of conditional probabilities of tornado damage rating from near-storm environment and radar-based storm-scale characteristics from a 5-year sample of tornadoes (4,770) reported in the contiguous United States (CONUS) during 2009–2013. The probabilities are derived from filtering tornado EF-scale segment data, large hail (i.e., ≥1 inch diameter), and severe wind gust (i.e., ≥50 kt) data by the maximum event type (e.g., tornado, hail, wind) per hour on a 40-km horizontal grid. Near-storm environment data, consisting primarily of supercell-related convective parameters from hourly objective mesoscale analysis calculated at the SPC, accompanied each grid-hour event. Filtered large-hail/wind events (~ 11000) associated with effective shear ≥20-kt and tornado events (800) were subsequently examined with level-II radar data. Convective mode was then assigned manually to each tornado large-hail/wind event if 0.5° velocity data from the nearest WSR-88D exhibited rotation. Peak 0.5° rotational velocity was recorded for each tornado event along the tornado path and within 10 minutes/miles for large-hail/wind reports. Preliminary results of tornado probabilities based on 2014 severe supercell 40-km grid-hour data are presented. Implications of these findings for diagnosing tornado potential in near realtime and possibly applying this research to aid National Weather Service (NWS) Impact-Based Warnings are discussed —tentatively scheduled for operational adoption NWS-wide by early 2016.

Written by leading experts in 5G research, this book is a comprehensive overview of the current state of 5G. Covering everything from the most likely use cases, spectrum aspects, and a wide range of technology options to potential 5G system architectures, it is an indispensable reference for academics and professionals involved in wireless and mobile communications. Global research efforts are summarised, and key component technologies including D2D, mm-wave communications, massive MIMO, coordinated multi-point, wireless network coding, interference management and spectrum issues are described and explained. The significance of 5G for the automotive, building, energy, and manufacturing economic sectors is addressed, as is the relationship between IoT, machine type communications, and cyber-physical systems. This essential resource equips you with a solid insight into the nature, impact and opportunities of 5G.

One of the key building blocks of the newly emerging 5th generation (5G) wireless communication system are ultra dense small cells. In this paper, the system performance of such a deployment is studied for scenarios where there are individual deadlines to serve data packets. A proactive delay-minimizing scheduling method (PDMS) is proposed to minimize the number of dropped packets due to missed deadlines and further reduce the average delay of the served packets. It is assumed that the radio scheduler does not only have information about the packet deadlines, but also has some information on how channel properties will evolve in the future. In practice, such information could be obtained through a prediction of user trajectories or prediction of fading signals, but this is beyond the scope of this paper. The presented results show that the proposed scheduler performs well in high load regimes. It substantially reduces the number of dropped packets and the average delay as compared to a non-deadline-aware one. The good overall performance recommends the proposed scheme as a potential component for 5G systems where there will likely be a wide diversity of applications with more stringent quality of service and latency requirements.