An early warning approach for the rapid identification of extreme weather disasters based on phased array dual polarization radar cooperative network data

In recent years, X-band phase array dual polarization weather radar technology has matured. The cooperative networking data from X-band phase array dual polarization weather radar have many advantages compared with traditional methods, namely, high spatial and temporal resolution (approximately 70 seconds in one scan, 30 m in radial distance resolution), wide coverages that can compensate for the observation blind spots, and data fusion technology that is used in the observation overlap area to ensure that the observed precipitation data have spatial continuity. Based on the above radar systems, this study proposes an improved hail and lightning weather disaster rapid identification and early warning algorithm. The improved thunderstorm identification, tracking, analysis, and nowcasting (TITAN) algorithm is used to quickly identify three-dimensional strong convective storm cells. Large sample observation experiment data are used to invert the localized hail index (HDR) to identify the hail position. The fuzzy logic method is used to comprehensively determine the probability of lightning occurrence. The comparative analysis experiment shows that, compared with the live observation data from the ground-based automatic station, the hail and lightning disaster weather warning algorithm developed by this study can increase warning times by approximately 7 minutes over the traditional algorithm, and its critical success index (CSI), false alarm ratio (FAR) and omission alarm ratio (OAR) scores are better than those of the traditional method. The average root mean square error (ARMSE) for identifying hail and lightning locations by this improved method is also significantly better than that of traditional methods. We show that our method can provide probabilistic predictions that improve hail and lightning identification, improve the precision of early warning and support operational utility at higher resolutions and with greater lead times that traditional methods struggle to achieve.

Characteristics of Regions with High-Density Initiation of Flashes in Mesoscale Convective Systems

To investigate the characteristics of regions exhibiting multiple lightning initiations within a finite volume and a short time, the lightning location data obtained from the convective regions of 14 mesoscale convective systems were analyzed in combination with data from radar. In total, 415 out of 5996 radar grids (1 km × 1 km × 0.5 km) were found to initiate more than one flash within 6 min. Only 49 grids showed an initiation density of more than two flashes within 6 min. The grids with high flash initiation densities were found to have a similar distribution to those with one lightning initiation within 6 min, in terms of altitude and reflectivity relative to altitude. They also showed similar trends in their frequency evolution. The grids with higher initiation densities seemed to be more concentrated in the altitude range of 9–13 km. However, only one was found to form at a lower altitude near the melting level when lightning initiation clearly declined. Moreover, the spatial relationship of this lower higher-initiation density grid to the reflectivity core was different to that in the main altitude range. In this paper, the possible dynamic and electrical mechanisms of the formation of this lower higher-initiation density grid are discussed.

The Illumination of Thunderclouds by Lightning: 3. Retrieving Optical Source Altitude

Optical space-based lightning sensors such as the Geostationary Lightning Mapper (GLM) detect and geolocate lightning by recording rapid changes in cloud top illumination. While lightning locations can be determined to within a pixel on the GLM imaging array, these instruments are not individually able to natively report lightning altitude. It has previously been shown that thunderclouds are illuminated differently based on the altitude of the optical source. In this study, we examine how altitude information can be extracted from the spatial distributions of GLM energy recorded from each optical pulse. We match GLM "groups" with Lightning Mapping Array (LMA) source data that accurately report the 3-D positions of coincident Radio-Frequency (RF) emitters. We then use machine learning methods to predict the mean LMA source altitudes matched to GLM groups using metrics from the optical data that describe the amplitude, breadth, and texture of the group spatial energy distribution. The resulting model can predict the LMA mean source altitude from GLM group data with a median absolute error of <1.5 km, which is sufficient to determine the location of the charge layer where the optical energy originated. This model is able to capture changes to the source altitude distribution in response to convective processes in the thunderstorm, and the GLM predictions can reveal the vertical structure of individual flashes - enabling 3-D flash geolocation with GLM for the first time. Future work will account for differences in thunderstorm charge/precipitation structures and viewing angle across the GLM Field of View.

Thunderstorm Cloud-Type Classification from Space-Based Lightning Imagers

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 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) multi-spectral 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 / non-convective regions in thunderstorms based on signatures of interactions with non-convective charge structures in the lightning flash data. The GLM sensor permits a rapid (20-s) update cycle over the combined GOES-16 / GOES-17 domain 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 anvils 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.

Changes to the Appearance of Optical Lightning Flashes Observed From Space According to Thunderstorm Organization and Structure

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.

Analysis of Lightning and Precipitation Activities in Three Severe Convective Events Based on Doppler Radar and Microwave Radiometer over the Central China Region

Hubei Province Region (HPR), located in Central China, is a concentrated area of severe convective weather. Three severe convective processes occurred in HPR were selected, namely 14–15 May 2015 (Case 1), 6–7 July 2013 (Case 2), and 11–12 September 2014 (Case 3). In order to investigate the differences between the three cases, the temporal and spatial distribution characteristics of cloud–ground lightning (CG) flashes and precipitation, the distribution of radar parameters, and the evolution of cloud environment characteristics (including water vapor (VD), liquid water content (LWC), relative humidity (RH), and temperature) were compared and analyzed by using the data of lightning locator, S-band Doppler radar, ground-based microwave radiometer (MWR), and automatic weather stations (AWS) in this study. The results showed that 80% of the CG flashes had an inverse correlation with the spatial distribution of heavy rainfall, 28.6% of positive CG (+CG) flashes occurred at the center of precipitation (>30 mm), and the percentage was higher than that of negative CG (−CG) flashes (13%). Moreover, the quantity of thunderstorm cells in Case 1 was more than other cases, the peak time of +CG flashes was prior to that of total CG flashes in Case 2 and Case 3, and the time of +CG flashes’ peak in Case 2 was prior to that of precipitation at about 2 h. Based on the analysis of the cloud environment, there are three main reasons for the differences of CG flashes and precipitation. Firstly, the structure of the LWC vertical profile and the height of the LWC peak are different, and high LWC makes it difficult for the collision of ice particles to generate electricity. Secondly, the differences between convective available potential energy (CAPE), precipitation, and CG flashes is caused by the sudden increase of VD from 1.5 km to 3 km, and thirdly, the production of CG flashes is very sensitive to RH at the surface layer and the total CG flashes increase as the RH increasing.

Mapping the Lateral Development of Lightning Flashes From Orbit

Optical lightning measurements from the Lightning Imaging Sensor (LIS) are used to map the lateral development of lightning flashes and produce statistics that describe their motion through the electrified cloud. This is accomplished by monitoring the frame-by-frame (group-level) evolution of the optical signals produced during each flash. While the optical flash properties recorded by LIS gravitate towards the most exceptional optical signals produced during the flash, group-level data describe the evolution and lateral development of the flash resulting from physical lightning process that emits enough light out of the top of the cloud to be detected from orbit. The groups that comprise LIS flashes constitute examples of complex lateral flash structure that can extend 80 km in length with dozens to hundreds of visible branches. The lateral development of individual flashes is described in terms of its speed and direction of motion, whether the development extends the overall length of the flash or reilluminates an existing segment, and whether it is directed inbound or outbound with respect to the origin. Sixty-five percent of propagating groups are directed outbound from the origin, 22% extend the length of the flash, and 3-5% reilluminate an existing branch. LIS flashes are commonly oriented from east to west and develop at speeds ranging from 104 to 106 m/s, consistent with large-scale leader development. These results provide evidence that lightning imagers may be used in conjunction with Lightning Mapping Array systems to document physical lightning phenomena across global domains.

Characteristics of Lightning within Electrified Snowfall Events using Lightning Mapping Arrays

This study examined 34 lightning flashes within four separate thundersnow events derived from lightning mapping arrays (LMAs) in northern Alabama, central Oklahoma, and Washington DC. The goals were to characterize the in-cloud component of each lightning flash, as well as the correspondence between the LMA observations and lightning data taken from national lightning networks like the National Lightning Detection Network (NLDN). Individual flashes were examined in detail to highlight several observations within the dataset. The study results demonstrated that the structures of these flashes were primarily normal polarity. The mean area encompassed by this set of flashes is 375 km², with a maximum flash extent of 2300 km², a minimum of 3 km², and a median of 128 km². An average of 2.29 NLDN flashes were recorded per LMA-derived lightning flash. A maximum of 11 NLDN flashes were recorded in association with a single LMA-derived flash on 10 January 2011. Additionally, seven of the 34 flashes in the study contain zero NLDN identified flashes. Eleven of the 34 flashes initiated from tall human-made objects (e.g., communication towers). In at least six lightning flashes, the NLDN detected a return stroke from the cloud back to the tower and not the initial upward leader. This study also discusses lightning's interaction with the human built environment and provides an example of lightning within heavy snowfall observed by GOES-16's Geostationary Lightning Mapper.

Evaluation of Microphysical Retrievals from Polarimetric Radar with Wind Profiler Data

Polarimetric radar data have been used to produce microphysical classifications. This kind of analysis is run in a real-time mode from several research radars, including the C-band polarimetric (C-Pol) radar in Darwin, Australia. However, these classifications have had very little systematic evaluation with independent data. Using surface data is often difficult because of sampling issues, particularly for hail. The approach taken here is to use a combination of 50- and 920-MHz wind profiler estimates of rain and hail to provide validation data for the radar pixels over the profiler. The profilers also observe signals associated with lightning, and some comparisons are made between lightning occurrence and the radar measurements of graupel. The retrievals of hail–rain mixtures are remarkably robust; there are some issues regarding other microphysical classes, however, including difficulties in detecting melting snow layers in stratiform rain. These difficulties are largely due to the resampling of the radar volume data onto a grid and to poor separation of the snow classes.