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Two Geostationary Lightning Mappers (GLMs) now observe spatial and temporal lightning distributions over a vast region. The GOES-16 GLM covers most land areas in the Western Hemisphere, and detects ~4 times as much lightning as the GOES-17 GLM. Although the continents dominate the lightning distributions year-round, each season exhibits widespread lightning over parts of the Atlantic Ocean and within three broad regions over the Pacific. These oceanic regions demonstrate the key role convective organization plays in producing larger, longer-lasting, and more energetic flashes observed by both GLMs over the oceans. Texture within the flash densities reveals a close relationship with the underlying topography, underscored by the complex diurnal cycles observed along coastlines and in mountainous regions. GLM information beyond flash frequency provides additional insights into storm mode and evolution. For example, over the Sierra Madre Occidental, time series reveal initially small, short-duration GLM flashes growing larger and longer as storms grow upscale. These mesoscale convective systems often transition offshore, contributing to an average flash area maximum over the eastern Pacific. Data quality improves during the study period with tuning of the ground system software. GLM artifacts due to solar intrusion and sun glint greatly diminish following the blooming filter installation, and the second-level threshold filter reduces false events along particular subarray boundaries (i.e., bar artifacts). Analysis of the overlap region reveals a pronounced north–south line near 103°W, with the GOES-16 (GOES-17) GLM detecting more flashes to the east (west) of this line.

The organization and structure of thunderstorms determines the extent and severity of their hazards to the general public and their consequences for the Earth system. Distinguishing vigorous convective regions that produce heavy rain and hail from adjacent regions of stratiform clouds or overhanging anvil clouds that produce light to no rainfall is valuable in operations and physical research. Cloud-type algorithms that partition convection from stratiform regions have been developed for space-based radar, passive microwave, and now Geostationary Operational Environmental Satellites (GOES) Advanced Baseline Imager (ABI) multispectral products. However, there are limitations for each of these products including temporal availability, spatial coverage, and the degree to which they based on cloud microphysics. We have developed a cloud-type algorithm for GOES Geostationary Lightning Mapper (GLM) observations that identifies convective/nonconvective regions in thunderstorms based on signatures of interactions with nonconvective charge structures in the lightning flash data. The GLM sensor permits a rapid (20 s) update cycle over the combinedGOES-16–GOES-17domain across all hours of the day. Storm regions that do not produce lightning will not be classified by our algorithm, however. The GLM cloud-type product is intended to provide situational awareness of electrified nonconvective clouds and to complement other cloud-type retrievals by providing a contemporary assessment tied to lightning physics. We propose that a future combined ABI–GLM cloud-type algorithm would be a valuable product that could draw from the strengths of each instrument and approach.

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This work addresses the long-term relative occurrence of cloud-to-ground (CG) and intracloud (IC; no attachment to ground) flashes for the contiguous United States (CONUS). It expands upon an earlier analysis by Boccippio et al. who employed 4-yr datasets provided by the U.S. National Lightning Detection Network (NLDN) and the Optical Transient Detector (OTD). Today, the duration of the NLDN historical dataset has more than tripled, and OTD data can be supplemented with data from the Lightning Imaging Sensor (LIS). This work is timely, given the launch of GOES-16, which includes the world’s first geostationary lightning mapper that will observe total lightning (IC and CG) over the Americas and adjacent ocean regions. Findings support earlier results indicating factor-of-10 variations in the IC:CG ratio throughout CONUS, with climatological IC fraction varying between 0.3 and greater than 0.9. The largest values are seen in the Pacific Northwest, central California, and where Colorado borders Kansas and Nebraska. An uncertainty analysis indicates that the large values in the northwest and central California are likely not due to measurement uncertainty. The high IC:CG ratio (>4) throughout much of Texas reported by Boccippio et al. is not supported by this longer-term climatology. There is no clear evidence of differences in IC fraction between land and coastal ocean. Lightning characteristics in six selected large regions show a consistent positive relationship between IC fraction and the percent of positive CG flashes, irrespective of lightning incidence (flash density), dominant season, or diurnal maximum period.

This study evaluates the performance of the operational and reprocessed Global Lightning Dataset 360 (GLD360) data relative to the Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS) during 2012–14. The analysis compares ground- and space-based lightning observations to better characterize the pre- and postupgrade GLD360. The reprocessed, postupgrade data increase the fraction of LIS flashes detected by the GLD360 [i.e., relative detection efficiency (DE)]. The relative DE improves during each year in every region, and year-over-year improvement appears in both datasets. The reprocessed relative DE exceeds 40% throughout large portions of the study domain with relative maxima over the western Atlantic, eastern Pacific, and the Gulf of Mexico. The upgrade results in shorter distances between matched LIS and GLD360 locations, indicating improved location accuracy. On average, the matched LIS flashes last longer (18.6 ms) and are larger (379.3 km2) than the unmatched LIS flashes (6.1 ms, 251.0 km2). For each LIS characteristic examined, the greater the value, the more likely the GLD360 detects the flash. Of the matched LIS flashes, 44.3% have multiple GLD360 strokes, and the mean LIS characteristics increase with increasing stroke count. LIS flashes with four-plus related GLD360 strokes are longest (61.1 ms) and largest (492.7 km2). Of the multistroke flashes, 57.3% contain subsequent strokes that are stronger than the initial stroke. The vast majority of multistroke flashes with a first stroke estimated with a peak current of

This study uses Global Precipitation Measurement (GPM) Microwave Imager (GMI) and Ka-precipitation radar observations to quantify the snowfall detection performance for different channel (frequency) combinations. Results showed that the low-frequency-channel set contains limited snow detection information with a 0.34 probability of detection (POD). Much better performance is evident using the high-frequency channels (i.e., POD = 0.74). In addition, if only one high-frequency channel is allowed to be added to the low-frequency-channel set, adding the 183 ± 3 GHz channel presents the largest POD improvement (from 0.34 to 0.50). However, this does not imply that the water vapor is the key information for snowfall detection. Only using the high-frequency water vapor channels showed poor snowfall detection with POD at 0.13. Further analysis of all 8191 possible GMI channel combinations showed that the 166-GHz channels are indispensable for any channel combination with POD greater than 0.70. This suggests that the scattering signature, not the water vapor effect, is essential for snowfall detection. Data analysis and model simulation support this explanation. Finally, the GPM constellation radiometers are grouped into six categories based on the channel availability and their snowfall detection capability is estimated, using channels available on GMI. It is found that type-4 radiometer (all channels) has the best snowfall detection performance with a POD of 0.77. The POD values are only slightly smaller for the type-3 radiometer (high-frequency channels) and type-5 radiometer (all channels except 183 channels).

Optical lightning observations from space reveal a wide range of flash structure. Lightning imagers such as the Geostationary Lightning Mapper and Lightning Imaging Sensor measure flash appearance by recording transient changes in cloud top illumination. The spatial and temporal optical energy distributions reported by these instruments depend on the physical structure of the flash and the distribution of hydrometeors within the thundercloud that scatter and absorb the optical emissions. This study explores how flash appearance changes according to the scale and organization of the parent thunderstorms with a focus on mesoscale convective systems. Clouds near the storm edge are frequently illuminated by large optical flashes that remain stationary between groups. These flashes appear large because their emissions can reflect off the exposed surfaces of nearby clouds to reach the satellite. Large stationary flashes also occur in small isolated thunderstorms. Optical flashes that propagate horizontally, meanwhile, are most frequently observed in electrified stratiform regions where extensive layered charge structures promote lateral development. Highly radiant "superbolts" occur in two scenarios: embedded within raining stratiform regions or in nonraining boundary/anvil clouds where optical emissions can take a relatively clear path to the satellite.

The Geostationary Lightning Mapper (GLM) is the first of four instruments in the Geostationary Operational Environmental Satellites (GOES)-R Series that will observe lightning throughout much of the Western Hemisphere through 2036. The GLM observes similar spatial and temporal lightning distributions to those reported by many previous studies. The continuous sampling of the GLM provides similar insights over a much shorter period of time (i.e., months vs years). Examples of GLM applications include severe storm monitoring and diagnosis, lightning safety, aviation planning, and fire weather. A suite of gridded GLM products has been developed to help forecasters characterize the convective scene. The GLM is a wholly new instrument undergoing extensive calibration and validation, work continues to mitigate several sources of false events. The GLM has contributed to a golden age of lightning observations. The continuous availability of spatially extensive total lightning data will spur more rapid progress toward synthesis of these observations with other meteorological data sets and forecasting tools. Wide-ranging efforts continue toward (1) documenting, diagnosing, and communicating the GLM data quality, (2) developing and implementing the most useful operational products, and (3) promoting proper use of the GLM data among a broad range of operational and research users.

The composition and time evolution of lightning are examined using the Lighting Imaging Sensor (LIS). Frame-by-frame optical lightning measurements are clustered into features whose radiant energy, horizontal footprint, and timing may be analyzed statistically. A LIS series feature is used to describe distinct periods of near continuous illumination that persists over multiple LIS frames. Series are integrated into the LIS clustering hierarchy between the group and flash level. An average series illuminates 40% of the flash footprint while accounting for 20% of the flash radiance, and just 1% of the flash duration. LIS flashes typically contain optical emissions that are exceptionally radiant and may persist over multiple frames. Series features cluster these bright optical pulses, allowing their number and time separation to be quantified in each flash. This optical multiplicity averages 1.7 for flashes with at least one particularly radiant group. Multigroup series most often occur early in the flash duration with 13% to 18% at first light. Series are typically separated by 100 ms or more in multiseries flashes. Bright series, by contrast, typically occur in rapid succession, with at most a few dozen milliseconds between them. Because series are optical features, they may result from any physical process that produces strong optical emissions. The statistics presented herein support the idea that series may originate from multiple physical processes.

Hurricane Sandy – a tropical cyclone that transitioned into an extratropical cyclone near the time of landfall along the east coast of the United States – caused historic damage in many regions which rarely receive such a direct hit from a storm of this magnitude, including many of the large metropolitan areas along the U.S. eastern seaboard. Specifically, Sandy generated record low-pressure, a large wind field with corresponding storm surge and copious amounts of precipitation in some areas, including record snowfall in mountainous regions. Sandy presented several forecast challenges to the National Oceanic and Atmospheric Administration's (NOAA) National Weather Service (NWS). Satellites played an integral role in the analysis and forecast of Sandy's track and intensity. The NOAA National Hurricane Center, Ocean Prediction Center, and Weather Prediction Center all relied on information from satellites to make critical warning decisions using various satellite products that assist with diagnosing tropical cyclone intensity, surface winds over the ocean, and heavy precipitation. All of the skillful global forecast models used satellite data for initiation to better forecast the track and intensity of Sandy. As part of the Geostationary Operational Environmental Satellite — R-series (GOES-R) and Joint Polar Satellite System (JPSS) Proving Ground activities, new satellite products were available to forecasters at these national centers in experimental form to assist with observing this unique, high impact event. This paper will demonstrate how the current satellite products assisted NOAA forecasters during Sandy and introduce some new satellite products that could be used to analyze and predict future high impact weather systems.

The Geostationary Operational Environmental Satellite-14 (GOES-14) imager was operated by the National Oceanic and Atmospheric Administration (NOAA) in an experimental rapid scan 1-min mode during parts of the summers of 2012 and 2013. This scan mode, known as the super rapid scan operations for GOES-R (SRSOR), emulates the high-temporal-resolution sampling of the mesoscale region scanning of the Advanced Baseline Imager (ABI) on the next-generation GOES-R series. This paper both introduces these unique datasets and highlights future satellite imager capabilities. Many phenomena were observed from GOES-14, including fog, clouds, severe storms, fires and smoke (including the California Rim Fire), and several tropical cyclones. In 2012 over 6 days of SRSOR data of Hurricane Sandy were acquired. In 2013, the first two days of SRSOR in June observed the propagation and evolution of a mid-Atlantic derecho. The data from August 2013 were unique in that the GOES imager operated in nearly continuous 1-min mode; prior to this time, the 1-min data were interrupted every 3 h for full disk scans. Used in a number of NOAA test beds and operational centers, including NOAA’s Storm Prediction Center (SPC), the Aviation Weather Center (AWC), the Ocean Prediction Center (OPC), and the National Hurricane Center (NHC), these experimental data prepare users for the next-generation imager, which will be able to routinely acquire mesoscale (1,000 km × 1,000 km) images every 30 s (or two separate locations every minute). Several animations are included, showcasing the rapid change of the many phenomena observed during SRSOR from the GOES-14 imager.

This study documents the composition, morphology, and motion of extreme optical lightning flashes observed by the Lightning Imaging Sensor (LIS). The furthest separation of LIS events (groups) in any flash is 135 km (89 km), the flash with the largest footprint had an illuminated area of 10,604 km2, and the most dendritic flash has 234 visible branches. The longest-duration convective LIS flash lasted 28 s and is overgrouped and not physical. The longest-duration convective-to-stratiform propagating flash lasted 7.4 s, while the longest-duration entirely stratiform flash lasted 4.3 s. The longest series of nearly consecutive groups in time lasted 242 ms. The most radiant recorded LIS group (i.e., "superbolt") is 735 times more radiant than the average group. Factors that impact these optical measures of flash morphology and evolution are discussed. While it is apparent that LIS can record the horizontal development of the lightning channel in some cases, radiative transfer within the cloud limits the flash extent and level of detail measured from orbit. These analyses nonetheless suggest that lightning imagers such as LIS and Geostationary Lightning Mapper can complement ground-based lightning locating systems for studying physical lightning phenomena across large geospatial domains.

Two overlapping efforts help to characterize the Geostationary Lightning Mapper (GLM) performance. The Post Launch Test (PLT) phase validates the predicted pre-launch instrument performance and the Post Launch Product Test (PLPT) phase validates the lightning detection product used in forecast and warning decision-making. This presentation documents the calibration and validation activities for the first 6 months of GLM on-orbit testing and validation commencing with first light on 4 January 2017. The PLT phase addresses image quality, on-orbit calibration, RTEP threshold tuning, image navigation, noise filtering, and solar intrusion assessment, resulting in a GLM calibration parameter file. The PLPT includes four main activities, the Reference Data Comparisons (RDC), Algorithm Testing (AT), Instrument Navigation and Registration Testing (INRT), and Long Term Baseline Testing (LTBT). A field campaign also provided valuable insights into the GLM performance capabilities. The PLPT tests each contribute to the beta, provisional, and fully validated GLM data.

Storm severity in the mid-Atlantic region of the United States is examined using lightning, radar, and model-derived information. Automated Warning Decision Support System (WDSS) procedures are developed to create grids of lightning and radar parameters, cluster individual storm features, and data mine the lightning and radar attributes of 1252 severe and nonsevere storms. The study first examines the influence of serial correlation and uses autocorrelation functions to document the persistence of lightning and radar parameters. Decorrelation times are found to vary by parameter, storm severity, and mathematical operator, but the great majority are between three and six lags, suggesting that consecutive 2-min storm samples (following a storm) are effectively independent after only 6–12 min. The study next describes the distribution of lightning jumps in severe and nonsevere storms, differences between various types of severe storms (e.g., severe wind only), and relationships between lightning and radar parameters. The 2σ lightning jump algorithm (with a 10 flashes min−1 activation threshold) yields 0.92 jumps h−1 for nonsevere storms and 1.44 jumps h−1 in severe storms. Applying a 10-mm maximum expected size of hail (MESH) threshold to the 2σ lightning jump algorithm reduces the frequency of lightning jumps in nonsevere storms to 0.61 jumps h−1. Although radar-derived parameters are comparable between storms that produce severe wind plus hail and those that produce tornadoes, tornadic storms exhibit much greater intracloud (IC) and cloud-to-ground (CG) flash rates. Correlations further illustrate that lightning data provide complementary storm-scale information to radar-derived measures of storm intensity.

[1] This study evaluates 4 years (2009–2012) of World Wide Lightning Location Network (WWLLN) data relative to the Tropical Rainfall Measuring Mission Lightning Imaging Sensor (LIS). In the Western Hemisphere, between 38°N and 38°S, the WWLLN detection efficiency (DE) (of LIS flashes) steadily improves from 6% during 2009 to 9.2% during 2012. The WWLLN is approximately three times more likely to detect a LIS flash over the ocean (17.3%) than over land (6.4%), and DE values greater than 20% only occur over the oceans. An average of 1.5 WWLLN strokes occurs during each matched LIS flash, but 71.5% of matched flashes are single stroke. Matched LIS flashes have more events/groups, longer durations, and larger areas than non-matched flashes. The close spatial proximity (11 km) and temporal proximity (+62 ms) between matched WWLLN and LIS flashes are important for Geostationary Lighting Mapper risk reduction studies that use existing networks to develop proxy data sets.

Seasonal, regional, and storm-scale variations of cloud-to-ground (CG) lightning characteristics in Florida are presented. Strong positive CG (+CG) and negative CG (−CG) flashes (i.e., having large peak current) are emphasized since they often are associated with strong storms, structural damage, and wildfire ignitions. Although strong −CG flashes are most common during the warm season (May–September) over the peninsula, the greatest proportion of strong +CG flashes occurs during the cool season (October–April) over the panhandle. The warm season exhibits the smallest +CG percentage but contains the greatest +CG flash densities, due in part to more ambiguous +CG reports (15–20 kA). The more frequent occurrence of ambiguous +CG reports helps explain the unusually small average +CG peak current during the warm season, whereas strong +CG reports (>20 kA) appear to be responsible for the greater average warm season +CG multiplicity. The −CG flash density, multiplicity, and peak current appear to be directly related, exhibiting their greatest values during the warm season when deep storms are most common. A case study examines the atmospheric conditions and storm-scale processes associated with two distinct groups of storms on 13–14 May 2007. Although these groups of storms form in close proximity, several factors combine to produce predominately strong +CG and −CG flashes in the northern (south Georgia) and southern (north Florida) regions, respectively. Results suggest that heat and smoke very near preexisting wildfires are key ingredients in producing reversed-polarity (+CG dominated) storms that often ignite subsequent wildfires.

The National Lightning Detection Network (NLDN) underwent a major upgrade during 2002–03 that increased its sensitivity and improved its performance. It is important to examine cloud-to-ground (CG) lightning distributions before and after this upgrade because CG characteristics depend on both measurement capabilities and meteorological variability. This study compares preupgrade (1996–99, 2001) and postupgrade (2004–09) CG distributions over the contiguous United States to examine the influence of the recent upgrade and to provide baseline postupgrade averages. Increased sensitivity explains most of the differences in the pre- and postupgrade distributions, including a general increase in total CG and positive CG (+CG) flash densities. The increase in +CG occurs despite the use of a greater weak +CG threshold for removing ambiguous +CG reports (post 15 kA versus pre 10 kA). Conversely, the average +CG percentage decreased from 10.61% to 8.65% following the upgrade. The average +CG (−CG) multiplicity increased from 1.10 (2.05) before to 1.54 (2.41) after the upgrade. Since true +CG flashes rarely contain more than one return stroke, explanations for the greater than unity +CG multiplicities remain unclear. Postupgrade results indicate that regions with mostly weak peak current +CG flashes now exhibit greater average +CG multiplicities, whereas regions with mainly strong +CG flashes now exhibit smaller average +CG multiplicities. The combination of NLDN performance, meteorological conditions, and physical differences in first −CG return strokes over saltwater produce maxima in −CG multiplicity and peak current over the coastal waters of the southeast United States.