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This article summarizes research and risk reduction that will inform acquisition decisions regarding NOAA’s future national operational weather radar network. A key alternative being evaluated is polarimetric phased-array radar (PAR). Research indicates PAR can plausibly achieve fast, adaptive volumetric scanning, with associated benefits for severe-weather warning performance. We assess these benefits using storm observations and analyses, observing system simulation experiments, and real radar-data assimilation studies. Changes in the number and/or locations of radars in the future network could improve coverage at low altitude. Analysis of benefits that might be so realized indicates the possibility for additional improvement in severe-weather and flash-flood warning performance, with associated reduction in casualties. Simulations are used to evaluate techniques for rapid volumetric scanning and assess data quality characteristics of PAR. Finally, we describe progress in developing methods to compensate for polarimetric variable estimate biases introduced by electronic beam-steering. A research-to-operations (R2O) strategy for the PAR alternative for the WSR-88D replacement network is presented.

Over the last two decades, the Central Weather Bureau of Taiwan and the U.S. National Severe Storms Laboratory have been involved in a research and development collaboration to improve the monitoring and prediction of river flooding, flash floods, debris flows, and severe storms for Taiwan. The collaboration resulted in the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) system. The QPESUMS system integrates observations from multiple mixed-band weather radars, rain gauges, and numerical weather prediction model fields to produce high-resolution (1 km) and rapid-update (10 min) rainfall and severe storm monitoring and prediction products. The rainfall products are widely used by government agencies and emergency managers in Taiwan for flood and mudslide warnings as well as for water resource management. The 3D reflectivity mosaic and QPE products are also used in high-resolution radar data assimilation and for the verification of numerical weather prediction model forecasts. The system facilitated collaborations with academic communities for research and development of radar applications, including quantitative precipitation estimation and nowcasting. This paper provides an overview of the operational QPE capabilities in the Taiwan QPESUMS system.

The method of neural networks (aka deep learning) has opened up many new opportunities to utilize remotely sensed images in meteorology. Common applications include image classification, e.g., to determine whether an image contains a tropical cyclone, and image-to-image translation, e.g., to emulate radar imagery for satellites that only have passive channels. However, there are yet many open questions regarding the use of neural networks for working with meteorological images, such as best practices for evaluation, tuning, and interpretation. This article highlights several strategies and practical considerations for neural network development that have not yet received much attention in the meteorological community, such as the concept of receptive fields, underutilized meteorological performance measures, and methods for neural network interpretation, such as synthetic experiments and layer-wise relevance propagation. We also consider the process of neural network interpretation as a whole, recognizing it as an iterative meteorologist-driven discovery process that builds on experimental design and hypothesis generation and testing. Finally, while most work on neural network interpretation in meteorology has so far focused on networks for image classification tasks, we expand the focus to also include networks for image-to-image translation.

Tor Bergeron was a key member of the Bergen School of Meteorology that developed some of the most influential contributions to synoptic analysis in the twentieth century: airmass analysis, polar-front theory, and the Norwegian cyclone model. However, the eventual success of these so-called Bergen methods of synoptic analysis was not guaranteed. Concerns and criticisms of the methods—in part from the lack of referencing to prior studies, overly simplified conceptual models, and lack of real data in papers by J. Bjerknes and Solberg—were inhibiting worldwide adoption. Bergeron’s research output in the 1920s was aimed at addressing these concerns. His doctoral thesis, written in German, was published as a journal article in Geofysiske Publikasjoner in 1928. Here, an accessible and annotated English translation is provided along with a succinct overview of this seminal study. Major interlaced themes of Bergeron’s study were the first comprehensive description of the Bergen methods: a vigorous defense of cyclogenesis as primarily a lower-tropospheric process as opposed to an upper-tropospheric–lower-stratospheric one; a nuanced explanation of the assertion that meteorology constituted a distinct and special scientific discipline; and, very understandably, a thorough account of Bergeron’s own contributions to the Bergen School. His contributions included identifying how deformation results in frontogenesis and frontolysis, classifying the influence of aerosols on visibility, and explaining the role of the ambient conditions in the onset of drizzle as opposed to rain showers—a distinction that led the formulation of the Wegener–Bergeron–Findeisen process.

High-sensitivity weather radars easily detect nonmeteorological phenomena characterized by weak radar returns. Fireworks are the example presented here. To understand radar observations, an experiment was conducted in which the National Severe Storms Laboratory (NSSL)’s research (3-cm wavelength) dual-polarization radar and a video camera were located at 1 km from fireworks in Norman, Oklahoma. The fireworks from the 4 July 2017 celebration were recorded by both instruments. The experiment is described. Few bursts recorded by the camera are analyzed to obtain the height of the explosion, its maximum diameter, number of stars, and the duration of the visible image. Radar volume scans are examined to characterize the height of the observation, the maximum reflectivity, and its distribution with height. The fireworks location is close to the Terminal Doppler Weather Radar (TDWR) that operates in single polarization at a 5-cm wavelength and monitors hazardous weather over the Oklahoma City airport. A third radar with data from the event is the Weather Surveillance Radar-1988 Doppler (WSR-88D) located in Norman. It has a wavelength of 10 cm and supports technical developments at the Radar Operation Center. Reflectivity factors measured by the three radars are compared to infer the size of dominant scatterers. The polarimetric characteristics of fireworks returns are analyzed. Although these differ from those of precipitation, they are indistinguishable from insect returns. Radar observation of larger fireworks in Fort Worth, Texas, with a WSR-88D is included and compared with the observations of the smaller fireworks in Norman. We expect the detectability of explosions would be similar as of fireworks. Pinpointing locations would be useful to first responders, or air quality forecasters. A benefit of fireworks recognition in weather radar data is that it can prevent contamination of precipitation accumulations.

The existence of mesoscale lightning discharges on the order of 100 km in length has been known since the radar-based findings of Ligda in the mid-1950s. However, it took the discovery of sprites in 1989 to direct significant attention to horizontally extensive “megaflashes” within mesoscale convective systems (MCSs). More recently, 3D Lightning Mapping Arrays (LMAs) have documented sprite-initiating lightning discharges traversing several hundred kilometers. One such event in a 2007 Oklahoma MCS having an LMA-derived length of 321 km, has been certified by the WMO as the longest officially documented lightning flash. The new Geostationary Lightning Mapper (GLM) sensor on GOES-16/17 now provides an additional tool suited to investigating mesoscale lightning. On 22 October 2017, a quasi-linear convective system moved through the central United States. At 0513 UTC, the GLM indicated a lightning discharge originated in northern Texas, propagated north-northeast across Oklahoma, fortuitously traversed the Oklahoma LMA (OKLMA), and finally terminated in southeastern Kansas. This event is explored using the OKLMA, the National Lightning Detection Network (NLDN), and the GLM. The NLDN reported 17 positive cloud-to-ground flashes (+CGs), 23 negative CGs (−CGs), and 37 intracloud flashes (ICs) associated with this massive discharge, including two +CGs capable of inducing sprites, with others triggering upward lightning from tall towers. Combining all available data confirms the megaflash, which illuminated 67,845 km2, was at least 500 km long, greatly exceeding the current official record flash length. Yet even these values are being superseded as GLM data are further explored, revealing that such vast discharges may not be all that uncommon.

A new, advanced radar has been developed at Colorado State University (CSU). The Sea-Going Polarimetric (SEA-POL) radar is a C-band, polarimetric Doppler radar specifically designed to deploy on research ships. SEA-POL is the first such weather radar developed in the United States. Ship-based weather radars have a long history, dating back to GATE in 1974. The GATE radars measured only reflectivity. After GATE, ship radars also provided Doppler measurements. SEA-POL represents the next advancement by adding dual-polarization technology, the ability to transmit and receive both horizontal and vertical polarizations. This configuration provides information about hydrometeor size, shape, and phase. As a result, superior rain-rate estimates are afforded by the dual-polarization technology, along with hydrometeor identification and overall improved data quality. SEA-POL made its first deployment as part of the Salinity Processes in the Upper Ocean Regional Study, second field phase (SPURS-2) fall 2017 cruise to the eastern tropical Pacific, sailing on the R/V Roger Revelle. SPURS-2 was a field project to investigate the fate of freshwater deposited on the ocean’s surface. Oceanographers are keenly interested in how fast these freshwater patches mix out by wind and upper-ocean turbulence, as the less dense rainfall sitting atop the salty ocean inhibits mixing through increased stability. To this end, during SPURS-2, SEA-POL produced rain maps identifying the location of freshwater lenses on the ocean’s surface thereby providing context for measurements of SST and salinity. Examples of SEA-POL polarization measurements are also discussed to assess microphysical processes within oceanic convection. Future ocean-based field campaigns will now benefit from SEA-POL’s advanced dual-polarization technology.

Radar is an important tool for investigating and forecasting processes in Earth’s atmosphere. More specifically, multiple frequencies of radar with sensitivities to cloud and precipitation regimes are important for process-level weather and climate studies. An online educational applet (http://lecuyer.aos.wisc.edu/radarlabviewer) has been developed to provide a learning experience about this topic with an intended audience of undergraduate and graduate university–level students. Cloud-resolving model data coupled with a radar instrument simulator provide a three-dimensional view of a midlatitude winter cyclone that is explorable through the applet. This synthetic laboratory environment provides direct comparisons between modeled hydrometeors and simulated instrument observations allowing students to visualize the concepts of attenuation, non-Rayleigh scattering, insensitivity of instruments, and viewing perspective. Variations in the minimum detectable signal of instruments also expose students to the real-world tradeoffs between instrument features and costs. Additional development and formal educational assessment of the applet is planned in the future.

This article presents the prospects of measurement systems for wind hazards and turbulence at airports, which have been explored in the Ultrafast Wind Sensors (UFO) project. At France’s Toulouse–Blagnac Airport, in situ, profiling, and scanning sensors have been used to collect measurements, from which wind vectors and turbulence intensities are estimated. A scanning 1.5-µm coherent Doppler lidar and a solid state X-band Doppler radar have been developed with improved update rates, spatial resolution, and coverage. In addition, Mode-S data downlinks have been collected for data analysis. Wind vector and turbulence intensity retrieval techniques are applied to demonstrate the capabilities of these measurement systems. An optimal combination of remote measurement systems is defined for all weather monitoring at airports. In this combination, lidar and radar systems are complementary for clear-air and rainy conditions, which are formulated in terms of visibility and rain rate. The added value of the measurement systems for high-resolution numerical weather prediction models is estimated by an observing system experiment, and a positive impact on the local wind forecast is demonstrated.

The National Center for Atmospheric Research (NCAR) operates a state-of-the-art S-band dual-polarization Doppler radar (S-Pol) for the National Science Foundation (NSF). This radar has some similar and some distinguishing characteristics to the National Weather Service (NWS) operational Weather Surveillance Radar-1988 Doppler Polarimetric (WSR-88DP). One key difference is that the WSR-88DP is used for operational purposes where rapid 360° volumetric scanning is required to monitor rapid changes in storm characteristics for nowcasting and issuing severe storm warnings. Since S-Pol is used to support the NSF research community, it usually scans at much slower rates than operational radars. This results in higher resolution and higher data quality suitable for many research studies. An important difference between S-Pol and the WSR-88DP is S-Pol’s ability to use customized scan strategies including scanning on vertical surfaces ([range–height indicators (RHIs)], which are presently not done by WSR-88DPs. RHIs provide high-resolution microphysical structures of convective storms, which are central to many research studies. Another important difference is that the WSR-88DP simultaneously transmits horizontal (H) and vertical (V) polarized pulses. In contrast, S-Pol typically transmits alternating H and V pulses, which results in not only higher data quality for research but also allows for the cross-polar signal to be measured. The cross-polar signal provides estimates of the linear depolarization ratio (LDR) and the co- to cross-correlation coefficient that give additional microphysical information. This paper presents plots and interpretations of high-quality, high-resolution polarimetric data that demonstrate the value of S-Pol’s polarimetric measurements for atmospheric research.

The process of pyroconvection occurs when fire-released heat, moisture, and/or aerosols induce or augment convection in the atmosphere. Prediction of pyroconvection presents a set of complex problems for meteorologists and wildfire managers. In particular, the turbulent characteristics of a pyroconvective plume exert bidirectional feedback on fire behavior, often with resulting severe impacts on life and property. Here, we present the motivation, field strategy, and initial results from the Bushfire Convective Plume Experiment, which through the use of mobile radar aims to quantify the kinematics of pyroconvection and its role in fire behavior. The case studies presented include world-first observations from two wildfires and one prescribed burn using the University of Queensland’s portable, dual-polarized X-band Doppler radar (UQ-XPOL). The initial analyses of reflectivity, Doppler winds, polarimetric variables, and spectrum width data provide insights into these relatively unexplored datasets within the context of pyroconvection. Weather radar data are supported by mesonet observations, time-lapse photography, airborne multispectral imaging, and spot-fire mapping. The ability to combine ground-validated fire intensity and progression at an hourly scale with quantitative data documenting the evolution of the convective plume kinematics at the scale of hundreds of meters represents a new capability for advancing our understanding of wildfires. The results demonstrate the suitability of portable, dual-polarized X-band Doppler radar to investigate pyroconvection and associated plume dynamics.

Continental mosaics of radar data have now been generated for more than 20 years. They offer information on precipitation climatology that is simply not available or archived elsewhere: How often does it rain at any particular location? At what time? And with what intensity distribution? What are the geographical and temporal patterns of precipitation occurrence, formation, and decay? What is the climatology of severe weather? Answers to these questions have value on their own and invariably trigger more questions about the processes causing these patterns but also suggest some answers. They also have considerable pedagogical value in illustrating in the classroom the impacts of different processes—such as sea–land breezes, topography, and seasons—on precipitation. In this work, U.S. mosaics of radar data from 1996 to 2015 are used to demonstrate the possibilities offered by such a dataset. Three topics are discussed: (i) climatologies and daily cycles of precipitation and convection, and what they can teach us about precipitation mechanisms; (ii) the spatial and temporal distribution of the appearance and occurrence of convection, and what it reveals about the importance of surface terrain properties for these events; and (iii) the power and challenges of looking for a small signal in such a large dataset using the influence of weekly activity cycles and cities on precipitation as an illustration.

Weather radars provide us with colorful images of storms, their development, and their movement, but from time to time the radars fail and we are left without data. To minimize these disruptions, owners of weather radars carry out preventive maintenance. The European radar project Operational Programme for the Exchange of Weather Radar Information (OPERA) conducted a survey among technicians from 21 countries on their experiences of maintenance. Regular maintenance frequency varies widely from as frequent as weekly to as infrequent as 6 months. Results show that the primary causes of missing data are not the failure of radar components and software or lack of maintenance but rather issues with the electricity supplies or telecommunications. Where issues are with the radars themselves, they are most commonly with the transmitter or the antenna controllers. Faults can be repaired quickly, but, if certain parts are required or the site is very remote, a radar can be out of service for weeks or even months. Failures of electricity or communications may also lead to lengthy periods of unavailability. As an example there is a story from Norway where wintertime thunderstorms severely damaged a radar at a very remote location. Annual operative costs of a radar are typically on the order of 5%–10% of the radar purchase price. During the lifetime of a system (typically 10–20 years) the operator can hence pay as much for the running costs as for the hardware purchase. It is extremely important to take infrastructure, maintenance, and monitoring into account when purchasing a new radar.

Embry-Riddle Aeronautical University Convective-Boundary Research Engaging Educational Student Experiences (ERAU C-BREESE) was an 18-day National Science Foundation (NSF)-funded educational Doppler on Wheels (DOW) deployment through the Center for Severe Weather Research in May 2015. ERAU C-BREESE had three primary areas of focus: meteorological field observations and research, undergraduate experiential learning, and local community outreach. ERAU undergraduate meteorology students had the unique opportunity to forecast for, collect, and analyze field measurements of sea-breeze processes and convection. The scientific objectives of ERAU C-BREESE were to forecast, observe, and analyze central Florida sea-breeze processes and thunderstorms by combining a DOW with more traditional tools. Specific scientific investigations were spurred by nine intensive observation periods (IOPs) throughout central Florida. Specific details are provided for IOP9, the most successful IOP, from both forecast and observational perspectives. Summaries of local community outreach, student education and responsibilities, and a discussion of the benefits of experiential learning are also provided.

The emergence of 3D scanning technologies has provided a new opportunity to explore the shape characteristics of hailstones in great detail. The ability to effectively map the shape of hailstones will improve assessments of hailstone aerodynamic properties, how their density relates to their strength, and how radar energy is scattered. Ultimately, 3D scanning of hailstones will contribute toward research in hail detection, forecasting, and damage mitigation of severe hail, which accounts for well over $1 billion in annual insured losses. The use of a handheld 3D laser scanner in a field setting was explored during field campaigns in 2015 and 2016. Hailstones were collected following thunderstorm passages and were measured, weighed, and scanned. The system was successful in capturing 3D models of more than 40 hailstones. A full scan takes approximately 3 minutes to complete, and data can be captured at a resolution of 0.008 cm. It is believed this is the first time such a system has been used to produce 3D digital hailstone models. Analysis of the model data has shown that hailstones depart from spherical shapes as they increase in diameter, and that bulk density and strength show little correlation. While the dataset presented here is small, the use of 3D scanners in the field is a practical method to obtain detailed datasets on hailstone characteristics. In addition, these data could be used to 3D-print hailstones to explore their aerodynamics, to produce cavity molds for ice impact tests, and for modeling radar scattering properties of natural hailstone shapes.

A World Meteorological Organization weather and climate extremes committee has judged that the world’s longest reported distance for a single lightning flash occurred with a horizontal distance of 321 km (199.5 mi) over Oklahoma in 2007, while the world’s longest reported duration for a single lightning flash is an event that lasted continuously for 7.74 seconds over southern France in 2012. In addition, the committee has unanimously recommended amendment of the AMS Glossary of Meteorology definition of lightning discharge as a “series of electrical processes taking place within 1 second” by removing the phrase “within one second” and replacing with “continuously.” Validation of these new world extremes (a) demonstrates the recent and on-going dramatic augmentations and improvements to regional lightning detection and measurement networks, (b) provides reinforcement regarding the dangers of lightning, and (c) provides new information for lightning engineering concerns. Fil: Lang, Timothy J.. NASA Marshall Space Flight Center; Estados Unidos Fil: Pédeboy, Stéphane. Météorage; Francia Fil: Rison, Willlliam. Institute of Mining and Technology, Department of Electrical Engineering; México Fil: Cerveny, Randallll S.. Arizona State Universit, School of Geographical Sciences and Urban Planning; Estados Unidos Fil: Montanyà, Joan. Universidad Politécnica de Catalunya; España Fil: Chauzy, Serge. University of Toulouse/CNRS, Laboratoire d’Aérologie; Francia Fil: MacGorman, Donald R.. NOAA/National Severe Storms Laboratory; Estados Unidos Fil: Holle, Ronald L.. Vaisala, Inc.; Estados Unidos Fil: Avila, Eldo Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina Fil: Zhang, Yijun. Chinese Academy of Meteorological Sciences, Laboratory of Lightning Physics and Protection Engineering; China Fil: Carbin, Gregory. NOAA/Storm Prediction Center; Estados Unidos Fil: Mansell, Edward R.. NOAA/National Severe Storms Laboratory; Estados Unidos Fil: Kuleshov, Yuriy. Royal Melbourne Institute of Technology University, Bureau of Meteorology and School of Mathematical and Geospatial Sciences; Australia Fil: Petersrson, Thomas C.. World Meteorological Organization, Commission for Climatology; Estados Unidos Fil: Brunet, Manola. University of East Anglia, School of Environmental Sciences,Climatic Research Unit; Reino Unido. Universitat Rovira I Virgili; España Fil: Driouech, Fatima. Direction de la Météorologie Nationale, Climate Studies Service; Marruecos Fil: Krahenbuhl, Daniel S.. Arizona State University, School of Geographical Sciences and Urban Planning; Estados Unidos

Weather radar networks are indispensable tools for forecasting and disaster prevention in industrialized countries. However, they are far less common in the countries of South America, which frequently suffer from an underdeveloped network of meteorological stations. To address this problem in southern Ecuador, this article presents a novel radar network using cost-effective, single-polarization, X-band technology: the RadarNet-Sur. The RadarNet-Sur network is based on three scanning X-band weather radar units that cover approximately 87,000 km2 of southern Ecuador. Several instruments, including five optical disdrometers and two vertically aligned K-band Doppler radar profilers, are used to properly (inter) calibrate the radars. Radar signal processing is a major issue in the high mountains of Ecuador because cost-effective radar technologies typically lack Doppler capabilities. Thus, special procedures were developed for clutter detection and beam blockage correction by integrating ground-based and satelliteborne measurements. To demonstrate practical applications, a map of areas frequently affected by intense rainfall is presented, based on a time series of one radar that has been in operation since 2002. Such information is of vital importance to, for example, infrastructure management because rain-driven landslides are a major issue for road maintenance and safety throughout Ecuador. The presented case study of exceptionally strong rain events during the recent El Niño in March 2015 highlights the system’s practicality in weather forecasting related to disaster management. For the first time, RadarNet-Sur warrants a spatial-explicit observation of El Niño-related heavy precipitation in a transect from the coast to the highlands in a spatial resolution of 500 m.

Mobile radar platforms designed for observation of severe local storms have consistently pushed the boundaries of spatial and temporal resolution in order to allow for detailed analysis of storm structure and evolution. Digital beamforming, or radar imaging, is a technique that is similar in nature to a photograwphic camera, where data samples from different spaces at the same range are collected simultaneously. This allows for rapid volumetric update rates compared to radars that scan with a single narrow beam. The Atmospheric Imaging Radar (AIR) is a mobile X-band (3.14-cm wavelength) imaging weather radar that transmits a vertical, 20° fan beam and uses a 36-element receive array to form instantaneous range–height indicators (RHIs) with a native beamwidth of 1° × 1°. Rotation in azimuth allows for 20° × 90° volumetric updates in under 6 s, while advanced pulse compression techniques achieve 37.5-m range resolution. The AIR has been operational since 2012 and has collected data on tornadoes and supercells at ranges as close as 6 km, resulting in high spatial and temporal resolution observations of severe local storms. The use of atmospheric imaging is exploited to detail rapidly evolving phenomena that are difficult to observe with traditional scanning weather radars.

There are numerous challenges with the forecasting and detection of flash floods, one of the deadliest weather phenomena in the United States. Statistical metrics of flash flood warnings over recent years depict a generally stagnant warning performance, while regional flash flood guidance utilized in warning operations was shown to have low skill scores. The Hydrometeorological Testbed—Hydrology (HMT-Hydro) experiment was created to allow operational forecasters to assess emerging products and techniques designed to improve the prediction and warning of flash flooding. Scientific goals of the HMT-Hydro experiment included the evaluation of gridded products from the Multi-Radar Multi-Sensor (MRMS) and Flooded Locations and Simulated Hydrographs (FLASH) product suites, including the experimental Coupled Routing and Excess Storage (CREST) model, the application of user-defined probabilistic forecasts in experimental flash flood watches and warnings, and the utility of the Hazard Services software interface with flash flood recommenders in real-time experimental warning operations. The HMT-Hydro experiment ran in collaboration with the Flash Flood and Intense Rainfall (FFaIR) experiment at the Weather Prediction Center to simulate the real-time workflow between a national center and a local forecast office, as well as to facilitate discussions on the challenges of short-term flash flood forecasting. Results from the HMT-Hydro experiment highlighted the utility of MRMS and FLASH products in identifying the spatial coverage and magnitude of flash flooding, while evaluating the perception and reliability of probabilistic forecasts in flash flood watches and warnings. NSSL scientists and NWS forecasters evaluate new tools and techniques through real-time test bed operations for the improvement of flash flood detection and warning operations.

The newly developed High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Cloud Radar (HCR) is an airborne, W-band, dual-polarization, Doppler research radar that fits within an underwing pod on the National Center for Atmospheric Research Gulfstream-V HIAPER aircraft. On 2 February 2015, the HCR was flown on its maiden research voyage over a cyclone along the Northeast coast of the United States. Six straight flight legs were flown over 6 h between the northern tip of Delaware Bay and Bangor, Maine, crossing the rain–snow line, and passing directly over Boston, Massachusetts, which received over 16 in. of snow during the event. The HCR, which recorded reflectivity, radial velocity, spectral width, and linear depolarization ratio with a 0.7° beam, was pointed at nadir from a flight altitude of 12,800 m (42,000 ft). The along-track resolution ranged between 20 and 200 m, depending on range, at aircraft speeds varying between 200 and 275 m s−1. The range resolution was 19.2 m. Remarkably detailed finescale structures were found throughout the storm system, including cloud-top generating cells, upright elevated convection, layers of turbulence, vertical velocity perturbations across the melting level, gravity waves, boundary layer circulations, and other complex features. Vertical velocities in these features ranged from 1 to 5 m s−1, and many features were on scales of 5 km or less. The purpose of this paper is to introduce the HCR and highlight the remarkable finescale structures revealed within this Northeast U.S. cyclone by the HCR.

The Multi-Radar Multi-Sensor (MRMS) system, which was developed at the National Severe Storms Laboratory and the University of Oklahoma, was made operational in 2014 at the National Centers for Environmental Prediction. The MRMS system consists of the Warning Decision Support System–Integrated Information suite of severe weather and aviation products, and the quantitative precipitation estimation products created by the National Mosaic and Multi-sensor Quantitative Precipitation Estimation system. Products created by the MRMS system are at a spatial resolution of approximately 1 km, with 33 vertical levels, updating every 2 min over the conterminous United States and southern Canada. This paper describes initial operating capabilities for the severe weather and aviation products that include a three-dimensional mosaic of reflectivity; guidance for hail, tornado, and lightning hazards; and nowcasts of storm location, height, and intensity.

Sudden local severe weather is a threat, and we explore what the highest-end supercomputing and sensing technologies can do to address this challenge. Here we show that using the Japanese flagship “K” supercomputer, we can synergistically integrate “big simulations” of 100 parallel simulations of a convective weather system at 100-m grid spacing and “big data” from the next-generation phased array weather radar that produces a high-resolution 3-dimensional rain distribution every 30 s—two orders of magnitude more data than the currently used parabolic-antenna radar. This “big data assimilation” system refreshes 30-min forecasts every 30 s, 120 times more rapidly than the typical hourly updated systems operated at the world’s weather prediction centers. A real high-impact weather case study shows encouraging results of the 30-s-update big data assimilation system.