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Radar data, which have incomparably high temporal and spatial resolution, and lightning data, which are great indicators of severe convection, have been used to improve the initial field and increase the accuracies of nowcasting and short-term forecasting. Physical initialization combined with the three-dimensional variational data assimilation method (PI3DVar_rh) is used in this study to assimilate two kinds of observation data simultaneously, in which radar data are dominant and lightning data are introduced as constraint conditions. In this way, the advantages of dual observations are adopted. To verify the effect of assimilating radar and lightning data using the PI3DVar_rh method, a severe convective activity that occurred on 5 June 2009 is utilized, and five assimilation experiments are designed based on the Weather Research and Forecasting (WRF) model. The assimilation of radar and lightning data results in moister conditions below cloud top, where severe convection occurs; thus, wet forecasts are generated in this study. The results show that the control experiment has poor prediction accuracy. Radar data assimilation using the PI3DVar_rh method improves the location prediction of reflectivity and precipitation, especially in the last 3-h prediction, although the reflectivity and precipitation are notably overestimated. The introduction of lightning data effectively thins the radar data, reduces the overestimates in radar data assimilation, and results in better spatial pattern and intensity predictions. The predicted graupel mixing ratio is closer to the distribution of the observed lightning, which can provide more accurate lightning warning information.

Infrequent lightning flashes occurring outside of surface precipitation pose challenges to Impact-Based Decision Support Services (IDSS) for outdoor activities. This paper examines the remote sensing observations from an event on 20 August 2019 where multiple cloud-to-ground flashes occurred over 10 km outside surface precipitation (lowest radar tilt reflectivity < 10 dBZ and no evidence of surface precipitation) in a trailing stratiform region of a mesoscale convective system. The goal is to demonstrate the fusion of radar with multiple lightning observations and a lightning risk model to demonstrate how reflectivity and differential reflectivity combined provided the best indicator for the potential of lightning where all of the other lightning safety methods failed. A total of 13 lightning flashes were observed by the Geostationary Lightning Mapper (GLM) within the trailing stratiform region between 2100 and 2300 UTC. The average size of the 13 lightning flashes was 3184 km2, with an average total optical energy of 7734 fJ. A total of 75 NLDN flash locations were coincident with the 13 GLM flashes, resulting in an average of 5.8 NLDN flashes [in-cloud (IC) and cloud-to-ground (CG)] per GLM flash. In total, five of the GLM flashes contained at least one positive cloud-to-ground flash (+CG) flash identified by the NLDN, with peak amplitudes ranging between 66 and 136 kA. All eight CG flashes identified by the NLDN were located more than 10 km outside surface precipitation. The only indication of the potential of these infrequently large flashes was the presence of depolarization streaks in differential reflectivity (ZDR) and enhanced reflectivity near the melting layer.

The squall line is a linear mesoscale convective system often seen in summer, which could bring severe disasters, such as thunderstorms, hails and tornadoes. So, the identification and forecast of squall lines are the important and difficult problems in operational weather nowcasting. In this paper, based on weather radar data an automatic identifying method of the squall line is presented. After image de-noising, extraction of the central axis of strong echo areas, and the Hough Transform, the spatial form and intensity variation characteristics of the reflectivity factors in the radar image are analyzed. On this basis, the automatic identification of squall line could be achieved. This method can overcome the adverse effects of the discontinuity of strong echo areas on the automatic identification of squall lines. The verification of squall line cases shows that the successful identification rate of squall lines is over 95%. Especially, when the boundary of the strong echo area is clear and is in a straight line or minor arc, the identification rate is higher. Overall, this new method has realized the automatic identification of squall lines on radar images, which could greatly improve the accuracy and time-effectiveness of squall line identification, and could provide a solid basis for the automatic forecast of squall lines in operational weather nowcasting.

In an effort to improve radar data assimilation configurations for potential operational implementation, GSI EnKF data assimilation experiments based on the operational system employed by the Center for Analysis and Prediction of Storms (CAPS) real-time Spring Forecast Experiments are performed. These experiments are followed by 6-h forecasts for an MCS on 28–29 May 2017. Configurations examined include data thinning, covariance localization radii and inflation, observation error settings, and data assimilation frequency for radar observations. The results show experiments that assimilate radar observations more frequently (i.e., 5–10 min) are initially better at suppressing spurious convection. However, assimilating observations every 5 min causes spurious convection to become more widespread with time, and modestly degrades forecast skill through the remainder of the forecast window. Ensembles that assimilate more observations with less thinning of data or use a larger horizontal covariance localization radius for radar data predict fewer spurious storms and better predict the location of observed storms. Optimized data thinning and horizontal covariance localization radii have positive impacts on forecast skill during the first forecast hour that are quickly lost due to the growth of forecast error. Forecast skill is less sensitive to the ensemble spread inflation factors and observation errors tested during this study. These results provide guidance toward optimizing the configuration of the GSI EnKF system. Among the DA configurations tested, the one employed by the CAPS Spring Forecast Experiment produces the most skilled forecasts while remaining computationally efficient for real-time use.

This study examined a typical case of deep convective storm that formed over southwest India on October 12, 2011, using ground-based X-band radar measurements and Weather Research and Forecasting (WRF) model simulations. The radar observation showed isolated pockets of convective storm, which merged later to form a convective cluster. The observed storms were tall, extending well into the mixed-phase region. Few storms even extended up to the tropopause height. Three different WRF cloud microphysics schemes (WRF Double-Moment 6-Class, Morrison Double-Moment, and Milbrandt–Yau Double-Moment) were used to simulate the observed deep convective storm to examine the vertical structure of hydrometeors. All the cloud microphysics schemes were able to reproduce the convective storm event with a lag time of almost two and a half hours. The WRF Double-Moment 6-Class scheme better simulates the vertical structure of storm compared to the other two microphysics schemes. The WRF model reasonably simulated the observed patterns of convective storm when the WRF cloud microphysics scheme better simulate the graupel and snow. The differences in simulated storm structure obtained by different microphysics schemes compared to observation highlight the deficiency involved in the simulations in capturing the microphysics that is guiding the intensity of convective storms. The present study thus underscores the importance of microphysics in different parameterization schemes of WRF simulation over southwest India, which has an implication in the forecasting of convective storms.

The split characteristics of the tropical Mesoscale Convective System (MCS) of April 9, 2018, in northern Ghana were studied using infrasound data measured by the mobile array (I68CI) which was deployed by C?te d’Ivoire National Data Center (NDC) in collaboration with the Comprehensive Nuclear-Test-Ban Treaty (CTBT). These infrasound measurements were made during a measurement campaign from January 1st, 2018 to December 31, 2018, in northeast Cote d’Ivoire, precisely in Comoe National Park. Graphic Progressive Multi-Channel Correlation (GPMCC) method based on a progressive study of the correlation functions was used to analyze and visualize data. The infrasound detection from this MCS shows clearly a division of the MCS structure into 2 distinct subsystems under the effect of internal and external constraints not well known but related to convection; a smaller subsystem in the north, associated with an area of intense rainfall of about 30 mm/hour and located at 9.5°N - 2°E with an azimuth of 70° and, a large subsystem in the south, associated with a zone of high rainfall of about 96 mm/hour and located at 8.8°N - 1.4°E with an azimuth of 90°. These two subsystems were located 200 km and 260 km from the I68CI station with frequencies of 2.3 Hz and 1 Hz respectively. The mesoscale convective systems in this region are moving from East to West and including several storm cells.

We examine the physical processes that led to the meteotsunami observed along the English Channel and North Sea coasts on 29 May 2017. It was most notably reported along the Dutch coast, but also observed on tide gauges from the Channel Islands to the coast of Germany, and also those in eastern England. From an assessment of multiple observations, including rain radar, LIDAR, satellite, surface observations and radiosonde reports we conclude that the event was driven by a rear flank downdraft in association with a mesoscale convective system (MCS). This downdraft, from a medium level or elevated MCS, led to a hydrostatically forced internal or ducted gravity wave below the MCS. The gravity wave was manifested by a marked rise and fall in pressure, a meso-high, which then interacted with the sea surface through Proudman resonance causing a measured wave of close to 0.9 m in amplitude, and an estimated wave run-up on Dutch beaches of 2 m. Through examination of existing research, we show that the basic assumptions here relating to the formation of the Dutch meteotsunami are consistent with previously described physical processes, and confirm the correlation between the speed of the ocean wave and medium level steering winds. This raises the possibility that high-resolution, coupled, weather-ocean numerical weather prediction (NWP) models can be utilised to predict future events. However, deterministic high-resolution NWP models still struggle with modelling convective systems with sufficient precision because of the chaotic nature of the atmosphere and incomplete observations. A way forward is proposed here to improve forecasting through post-processing of NWP model output by overlaying medium level wind fields with ocean bathymetry.

The recently launched Geostationary Operational Environmental Satellite “R-series” (GOES-R) satellites carry the Geostationary Lightning Mapper (GLM) that measures from space the total lightning rate in convective storms at high spatial and temporal frequencies. This study assimilates, for the first time, real GLM total lightning data in an ensemble Kalman filter (EnKF) framework. The lightning flash extent density (FED) products at 10-km pixel resolution are assimilated. The capabilities to assimilate GLM FED data are first implemented into the GSI-based EnKF data assimilation (DA) system and tested with a mesoscale convective system (MCS). FED observation operators based on graupel mass or graupel volume are used. The operators are first tuned through sensitivity experiments to determine an optimal multiplying factor to the operator, before being used in FED DA experiments FEDM and FEDV that use the graupel-mass or graupel-volume-based operator, respectively. Their results are compared to a control experiment (CTRL) that does not assimilate any FED data. Overall, both DA experiments outperform CTRL in terms of the analyses and short-term forecasts of FED and composite/3D reflectivity. The assimilation of FED is primarily effective in regions of deep moist convection, which helps improve short-term forecasts of convective threats, including heavy precipitation and lightning. Direct adjustments to graupel mass via observation operator as well as adjustments to other model state variables through flow-dependent ensemble cross covariance within EnKF are shown to work together to generate model-consistent analyses and overall improved forecasts.

This case study analyzes a nocturnal mesoscale convective system (MCS) that was observed on 25–26 June 2015 in northeastern Kansas during the Plains Elevated Convection At Night (PECAN) project. Over the course of the observational period, a broken line of elevated nocturnal convective cells initiated around 0230 UTC on the cool side of a stationary front and subsequently merged to form a quasi-linear MCS that later developed strong, surface-based outflow and a trailing stratiform region. This study combines radar observations with mobile and fixed mesonet and sounding data taken during PECAN to analyze the kinematics and thermodynamics of the MCS from 0300 to 0630 UTC. This study is unique in that 38 consecutive multi-Doppler wind analyses are examined over the 3.5 h observation period, facilitating a long-duration analysis of the kinematic evolution of the nocturnal MCS. Radar analyses reveal that the initial convective cells and linear MCS are elevated and sustained by an elevated residual layer formed via weak ascent over the stationary front. During upscale growth, individual convective cells develop storm-scale cold pools due to pockets of descending rear-to-front flow that are measured by mobile mesonets. By 0500 UTC, kinematic analysis and mesonet observations show that the MCS has a surface-based cold pool and that convective line updrafts are ingesting parcels from below the stable layer. In this environment, the elevated system has become surface based since the cold pool lifting is sufficient for surface-based parcels to overcome the CIN associated with the frontal stable layer.

Deployed on the mountainous island of Corsica for thunderstorm monitoring purposes in the Mediterranean Basin, SAETTA is a network of 12 LMA (Lightning Mapping Array, designed by New Mexico Tech, USA) stations that allows the 3-D mapping of very high-frequency (VHF) radiation emitted by cloud discharges in the 60–66 MHz band. It works at high temporal (∼40 ns in each 80 µs time window) and spatial (tens of meters at best) resolution within a range of about 350 km. Originally deployed in May 2014, SAETTA was commissioned during the summer and autumn seasons and has now been permanently operational since April 2016 until at least the end of 2020. We first evaluate the performances of SAETTA through the radial, azimuthal, and altitude errors of VHF source localization with the theoretical model of Thomas et al. (2004). We also compute on a 240 km × 240 km domain the minimum altitude at which a VHF source can be detected by at least six stations by taking into account the masking effect of the relief. We then report the 3-year observations on the same domain in terms of number of lightning days per square kilometer (i.e., total number of days during which lightning has been detected in a given 1 km square pixel) and in terms of lightning days integrated across the domain. The lightning activity is first maximum in June because of daytime convection driven by solar energy input, but concentrates on a specific hot spot in July just above the intersection of the three main valleys. This hot spot is probably due to the low-level convergence of moist air fluxes from sea breezes channeled by the three valleys. Lightning activity increases again in September due to numerous small thunderstorms above the sea and to some high-precipitation events. Finally we report lightning observations of unusual high-altitude discharges associated with the mesoscale convective system of 8 June 2015. Most of them are small discharges on top of an intense convective core during convective surges. They are considered in the flash classification of Thomas et al. (2003) to be small–isolated and short–isolated flashes. The other high-altitude discharges, much less numerous, are long-range flashes that develop through the stratiform region and suddenly undergo upward propagations towards an uppermost thin layer of charge. This latter observation is apparently consistent with the recent conceptual model of Dye and Bansemer (2019) that explains such an upper-level layer of charge in the stratiform region by the development of a non-riming ice collisional charging in a mesoscale updraft.

Mesoscale convective systems (MCSs) are one of the most climatically significant forms of convection because of their large role in water and energy cycles. The mesoscale features associated with MCS are difficult to represent in climate models because the relevant dynamics and physics are absent or poorly represented with coarse model resolution (∼100 km). Using a regionally refined model (RRM) with 0.25° grid spacing embedded in the Energy Exascale Earth System Model (E3SM), we explore the impact of cloud microphysics parameterizations on the simulation of precipitation, particularly MCS precipitation over the contiguous United States. The Predicted Particle Properties (P3) cloud microphysics scheme has been modified and implemented into E3SM to overcome the limitations of the default Morrison and Gettelman (MG2) scheme in which rimed precipitating ice particles (graupel/hail) are absent and frozen particles are artificially partitioned into cloud ice and snow. We show that P3 improves the simulation of precipitation statistics including frequency distribution compared with MG2 with a limited effect on the diurnal cycle. P3 predicts higher hourly rain rates, resulting in 20% more MCSs and a higher total MCS precipitation (4.4%) compared to MG2, agreeing better with observations. The improvements with P3 mainly result from improved representations of ice microphysics, which not only produces higher rain rates through melting but also leads to a stronger large‐scale ascending motion by releasing more latent heating. This study suggests that improving microphysics parameterization is important for simulating MCS precipitation as future climate model resolutions continue to increase.

Mesoscale Convective Complex (MCC) merupakan salah satu jenis dari Mesoscale Convective System (MCS). MCC membentuk sistem awan badai yang luas akibat dari banyaknya sel tunggal awan Cumulunimbus yang berkumpul dan tumbuh sehingga disebut gugusan awan konvektif berskala meso. Pada 14 Agustus 2017 terbentuk MCC di wilayah Papua Barat dengan masa hidup dari pukul 14.00 hingga 19.00 UTC. Fenomena MCC tersebut menghasilkan hujan yang berlangsung cukup lama dan bersifat terus-menerus. Penelitian ini bertujuan untuk mengkaji kondisi atmosfer saat terjadinya MCC di Papua Barat pada 14 Agustus 2017. Kajian ini menggunakan data reanalysis dari ECMWF berupa parameter komponen angin meridional dan zonal, vortisitas, dan kelembaban udara. Selain itu, juga perlu dikaji dengan menggunakan citra satelit Himawari 8 dan data Radiosonde. Dari komponen angin meridional dan zonal pada pukul 06.00-24.00 UTC terdapat angin yang cukup kencang di Papua Barat dengan arah pergerakan ke barat laut hingga utara. Berdasarkan kajian sementara, nilai vortisitas lapisan 500 mb pada pukul 06.00-24.00 UTC bernilai negatif yang mengindikasikan adanya sirkulasi siklonik pada troposfer bagian tengah. Kondisi tersebut didukung dengan nilai kelembaban udara yang berkisar antara 70-100% yang menunjukkan kondisi lapisan pada saat kejadian relatif basah. Pada citra satelit Himawari menunjukkan adanya gugusan awan Cumulonimbus dengan suhu puncak -80 0C dan berdiameter sekitar 200 km, yang bercampur dengan awan jenis lain. Sehingga, MCC tersebut tergolong pada MCS kategori beta.

This study evaluates the impact of dust–radiation–cloud interactions on the development of a mesoscale convective system (MCS) by comparing numerical experiments run with and without dust–radiation and/or dust–cloud interactions. An MCS that developed over North Africa on 4–6 July 2010 is used as a case study. The CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellites passed over the center of the MCS after it reached maturity, providing valuable profiles of aerosol backscatter and cloud information for model verification. The model best reproduces the MCS’s observed cloud structure and morphology when both dust–radiation and dust–cloud interactions are included. Our results indicate that the dust–radiation effect has a far greater influence on the MCS’s development than the dust-cloud effect. Results show that the dust-radiative effect, both with and without the dust–cloud interaction, briefly delays the MCS’s formation but ultimately produces a stronger storm with a more extensive anvil cloud. This is caused by dust–radiation-induced changes to the MCS’s environment. The impact of the dust–cloud effect on the MCS, on the other hand, is greatly affected by the presence of the dust–radiation interaction. The dust–cloud effect alone slows initial cloud development but enhances heterogeneous ice nucleation and extends cloud lifetime. When the dust–radiation interaction is added, increased transport of dust into the upper portions of the storm—due to a dust–radiation-driven increase in convective intensity—allows dust–cloud processes to more significantly enhance heterogeneous freezing activity earlier in the storm’s development, increasing updraft strength, hydrometeor growth (particularly for ice particles), and rainfall.

On the evening of 23 June 2016 around 18:00 UTC, a mesoscale convective system (MCS) with hail and wind gusts passed the southern province Noord-Brabant in the Netherlands, and caused 675 millions of euros damage. This study evaluates the performance of the Weather Research and Forecasting model with three cumulus parameterisation schemes (Betts&ndash, Miller&ndash, Janjic, Grell&ndash, Freitas and Kain&ndash, Fritsch) on a grid spacing of 4 km in the &lsquo, grey-zone&rsquo, and with explicitly resolved convection at 2 and 4 km grid spacing. The results of the five experiments are evaluated against observations of accumulated rainfall, maximum radar reflectivity, the CAPE evolution and wind speed. The results show that the Betts&ndash, Janjic scheme is activated too early and can therefore not predict any MCS over the region of interest. The Grell&ndash, Fritsch schemes do predict an MCS, but its intensity is underestimated. With the explicit convection, the model is able to resolve the storm, though with a delay and an overestimated intensity. We also study whether spatial uncertainty in soil moisture is scaled up differently using parameterised or explicitly resolved convection. We find that the uncertainty in soil moisture distribution results in larger uncertainty in convective activity in the runs with explicit convection and the Grell&ndash, Freitas scheme, while the Kain&ndash, Fritsch and Betts&ndash, Janjic scheme clearly present a smaller variability.

In this paper, we report the location results for the parent lightning strokes of more than 30 red sprites observed over an asymmetric mesoscale convective system (MCS) on 30 July 2015 in Shandong Province, China, with a long-baseline lightning location network of very-low-frequency/low-frequency magnetic field sensors. The results show that almost all of these cloud-to-ground (CG) strokes are produced during the mature stage of the MCS, and are predominantly located in the trailing stratiform region, which is similar to analyses of sprite-productive MCSs in North America and Europe. Comparison between the location results for the sprite-producing CG strokes and those provided by the World Wide Lightning Location Network (WWLLN) indicates that the location accuracy of WWLLN for intense CG strokes in Shandong Province is typically within 10 km, which is consistent with the result based on analysis of 2838 sprite-producing CG strokes in the continental United States. Also, we analyze two cases where some minor lightning discharges in the parent flash of sprites can also be located, providing an approach to confine the thundercloud region tapped by the sprite-producing CG strokes.

The convective area within a mesoscale convective system (MCS) contains intense convective cells or storms which themselves could be made of a single cumulonimbus cloud or several of them joined together. Interconnection between MCS evolution and storms has not been reported previously. We address this gap area by using the Doppler Weather Radar (DWR) data collected at four stations in India during the summer monsoon season of 2013. The four DWR locations selected have different climates ranging from coastal to semi-arid. Storm is defined as a set of contiguous radar pixels in three-dimensional space with a reflectivity threshold of 30 dBZ and the threshold criterion is satisfied in a volume of at least 50km(3). Monsoonal MCSs contain a few to more than 20 storms depending on geographic location and MCS life stage. The average area of storms ranges from 13 to 170km(2) while storm heights mostly lie between 6 and 10km. The growth stage of an MCS is characterized by a rapid increase in the number of storms, while their number and average area decrease in the dissipation stage. Storms occupy 30-70% of the convective area within an MCS and contribute 90-97% of the convective precipitation at any given instant. Thus, a few to several cumulonimbus clouds grouped together in a contiguous manner matter most for convective precipitation, making storm scale an important scale in the hierarchy of scales in tropical deep convective cloud systems. This has implications for cumulus parametrization as well as planning satellite payloads for observing precipitation.

Mesoscale convective systems (MCSs) and their associated cloud properties are the important factors that influence the aviation activities, yet they present a forecasting challenge in numerical weather prediction. In this study, the sensitivity of numerical simulations of an MCS over the US Southern Great Plains to ice hydrometeors in bulk microphysics (MP) schemes has been investigated using the Weather Research and Forecasting (WRF) model. It is found that the simulated structure, life cycle, cloud coverage, and precipitation of the convective system as well as its associated cold pools are sensitive to three selected MP schemes, namely, the WRF single-moment 6-class (WSM6), WRF double-moment 6-class (WDM6, with the double-moment treatment of warm-rain only), and Morrison double-moment (MORR, with the double-moment representation of both warm-rain and ice) schemes. Compared with observations, the WRF simulation with WSM6 only produces a less organized convection structure with a short lifetime, while WDM6 can produce the structure and length of the MCS very well. Both simulations heavily underestimate the precipitation amount, the height of the radar echo top, and stratiform cloud fractions. With MORR, the model performs well in predicting the lifetime, cloud coverage, echo top, and precipitation amount of the convection. Overall results demonstrate the importance of including double-moment representation of ice hydrometeors along with warm-rain. Additional experiments are performed to further examine the role of ice hydrometeors in numerical simulations of the MCS. Results indicate that replacing graupel with hail in the MORR scheme improves the prediction of the convective structure, especially in the convective core region.

This paper uses the daily precipitation observation data and Doppler weather radar observation data from 2017 observatories and regional automatic stations in Fujian, China from 2009 to 2015. The characteristics of formation mode, organization mode, moving direction and duration of linear mesoscale convective system during non-typhoon continuous rainstorm in Fujian were analyzed. This paper gives the definition of linear mesoscale convective systems, trailing and parallel mesoscale convective systems. The above characteristics of the linear mesoscale convective system during the continuous heavy rain in Fujian differ greatly from the non-sustained heavy rain process: The linear mesoscale convective system in the continuous heavy rain in Fujian is mainly constructed later, and the trailing and parallel mesoscale convective system is conducive to the occurrence of continuous heavy rain in Fujian. The moving direction of the linear mesoscale convective system and convective monomer is mainly in the east direction, and the system duration is mostly 4 - 10 hours. The formation time of the monomer to form a linear convection time is mainly 1 - 3 hours, which is 2 hours earlier than the organization process of the general linear mesoscale convective system. The linear convective system formed to a dead time of an average of 5 hours, slightly longer than the general linear mesoscale convective system.

Mesoscale convective system (MCS)-organized convective storms with a size of ~100 km have increased in frequency and intensity in the USA over the past 35 years 1 , causing fatalities and economic losses 2 . However, their poor representation in traditional climate models hampers the understanding of their change in the future 3 . Here, a North American-scale convection-permitting model which is able to realistically simulate MSCs 4 is used to investigate their change by the end-of-century under RCP8.5 (ref. 5 ). A storm-tracking algorithm 6 indicates that intense summertime MCS frequency will more than triple in North America. Furthermore, the combined effect of a 15–40% increase in maximum precipitation rates and a significant spreading of regions impacted by heavy precipitation results in up to 80% increases in the total MCS precipitation volume, focussed in a 40 km radius around the storm centre. These typically neglected increases substantially raise future flood risk. Current investments in long-lived infrastructures, such as flood protection and water management systems, need to take these changes into account to improve climate-adaptation practices. Limitations with climate models have previously prevented accurate diagnosis of future changes in mesoscale convective systems (MCSs). A convection-permitting model now indicates that summer MCSs will triple by 2100 in the United States, with a corresponding increase in rainfall rates and areal extent.

Sprites are transient luminous events (TLEs) that occur over thunderstorm clouds that represent the direct coupling relationship between the troposphere and the upper atmosphere. We report the evolution of a mesoscale convective system (MCS) that produced only one sprite event, and the characteristics of this thunderstorm and the related lightning activity are analyzed in detail. The results show that the parent flash of the sprite was positive cloud-to-ground lightning (+CG) with a single return stroke, which was located in the trailing stratiform region of the MCS with a radar reflectivity of 25 to 35 dBZ. The absolute value of the negative CG (−CG) peak current for half an hour before and after the occurrence of the sprite was less than 50 kA, which was not enough to produce the sprite. Sprites tend to be produced early in the maturity-to-dissipation stage of the MCS, with an increasing percentage of +CG to total CG (POP), indicating that the sprite production was the attenuation of the thunderstorm and the area of the stratiform region.

The primary goal of present study is to investigate the impact of assimilation of conventional and satellite radiance observations in simulating the mesoscale convective system (MCS) formed over south east India. An assimilation methodology based on Weather Research and Forecasting model three dimensional variational data assimilation is considered. Few numerical experiments are carried out to examine the individual and combined impact of conventional and non-conventional (satellite radiance) observations. After the successful inclusion of additional observations, strong analysis increments of temperature and moisture fields are noticed and contributed to significant improvement in model’s initial fields. The resulting model simulations are able to successfully reproduce the prominent synoptic features responsible for the initiation of MCS. Among all the experiments, the final experiment in which both conventional and satellite radiance observations assimilated has showed considerable impact on the prediction of MCS. The location, genesis, intensity, propagation and development of rain bands associated with the MCS are simulated reasonably well. The biases of simulated temperature, moisture and wind fields at surface and different pressure levels are reduced. Thermodynamic, dynamic and vertical structure of convective cells associated with the passage of MCS are well captured. Spatial distribution of rainfall is fairly reproduced and comparable to TRMM observations. It is demonstrated that incorporation of conventional and satellite radiance observations improved the local and synoptic representation of temperature, moisture fields from surface to different levels of atmosphere. This study highlights the importance of assimilation of conventional and satellite radiances in improving the models initial conditions and simulation of MCS.

This study examines the effectiveness of an ensemble Kalman filter based on the weather research and forecasting model to assimilate Doppler-radar radial-velocity observations for convection-permitting prediction of convection evolution in a high-impact heavy-rainfall event over coastal areas of South China during the pre-summer rainy season. An ensemble of 40 deterministic forecast experiments (40 DADF) with data assimilation (DA) is conducted, in which the DA starts at the same time but lasts for different time spans (up to 2 h) and with different time intervals of 6, 12, 24, and 30 min. The reference experiment is conducted without DA (NODA).To show more clearly the impact of radar DA on mesoscale convective system (MCS) forecasts, two sets of 60-member ensemble experiments (NODA EF and exp37 EF) are performed using the same 60-member perturbed-ensemble initial fields but with the radar DA being conducted every 6 min in the exp37 EF experiments from 0200 to 0400 BST. It is found that the DA experiments generally improve the convection prediction. The 40 DADF experiments can forecast a heavy-rain-producing MCS over land and an MCS over the ocean with high probability, despite slight displacement errors. The exp37 EF improves the probability forecast of inland and offshore MCSs more than does NODA EF. Compared with the experiments using the longer DA time intervals, assimilating the radial-velocity observations at 6-min intervals tends to produce better forecasts. The experiment with the longest DA time span and shortest time interval shows the best performance. However, a shorter DA time interval (e.g., 12 min) or a longer DA time span does not always help. The experiment with the shortest DA time interval and maximum DA window shows the best performance, as it corrects errors in the simulated convection evolution over both the inland and offshore areas. An improved representation of the initial state leads to dynamic and thermodynamic conditions that are more conducive to earlier initiation of the inland MCS and longer maintenance of the offshore MCS.

Despite an important role the aerosols play in all stages of cloud lifecycle, their representation in numerical weather prediction models is often rather crude. This paper investigates the effects the explicit versus implicit inclusion of aerosols in a microphysics parameterization scheme in Weather Research and Forecasting (WRF) – Advanced Research WRF (WRF-ARW) model has on cloud dynamics and microphysics. The testbed selected for this study is a severe mesoscale convective system with supercells that struck west and central parts of Serbia in the afternoon of July 21, 2014. Numerical products of two model runs, i.e. one with aerosols explicitly (WRF-AE) included and another with aerosols implicitly (WRF-AI) assumed, are compared against precipitation measurements from surface network of rain gauges, as well as against radar and satellite observations. The WRF-AE model accurately captured the transportation of dust from the north Africa over the Mediterranean and to the Balkan region. On smaller scales, both models displaced the locations of clouds situated above west and central Serbia towards southeast and under-predicted the maximum values of composite radar reflectivity. Similar to satellite images, WRF-AE shows the mesoscale convective system as a merged cluster of cumulonimbus clouds. Both models over-predicted the precipitation amounts; WRF-AE over-predictions are particularly pronounced in the zones of light rain, while WRF-AI gave larger outliers. Unlike WRF-AI, the WRF-AE approach enables the modelling of time evolution and influx of aerosols into the cloud which could be of practical importance in weather forecasting and weather modification. Several likely causes for discrepancies between models and observations are discussed and prospects for further research in this field are outlined.

On 24 May 2009, a large size mesoscale convective system crossed south-eastern Europe causing severe weather, heavy precipitation, and strong wind. The system met both spatial and duration criteria of a mesoscale convective complex (MCC). The case was analysed using different image processing techniques based on the high spectral resolution of Meteosat Second Generation (MSG) satellite data. First, an automatic cloud tracking algorithm was applied on successive infrared images to objectively characterize the life cycle of the MCC and analyse the temporal evolution of several morphological, positional, and spectral parameters. Then, satellite data were processed and visualized as single channel, channel differences, RGB (Red-Green-Blue) composite, and ‘blended multi-layer’ images to reveal important information on the development and cloud-top structure and microphysics of the MCC. Lightning data were also used as a measure of intense convective activity in the MCC. The MCC exhibited extraordinary ...

Deep convective transport of surface moisture and pollution from the planetary boundary layer to the upper troposphere and lower stratosphere affects the radiation budget and climate. This study analyzes the deep convective transport in three different convective regimes from the 2012 Deep Convective Clouds and Chemistry (DC3) field campaign: May 21st Alabama airmass thunderstorms, May 29th Oklahoma supercell severe storm, and June 11th mesoscale convective system (MCS). Lightning data assimilation within the Weather Research and Forecasting (WRF) model coupled with chemistry (WRF-Chem) is utilized to improve the simulations of storm location, vertical structure and chemical fields. Analysis of vertical flux divergence shows that deep convective transport in the May 29th supercell case is the strongest per unit area while transport of boundary layer insoluble trace gases is relatively weak in the MCS and airmass cases. The weak deep convective transport in the strong MCS is unexpected and is caused by the injection into low levels of mid-level clean air by a strong rear inflow jet. In each system, the magnitude of tracer vertical transport is more closely related to the vertical distribution of mass flux density than the vertical distribution of trace gas mixing ratio. Finally, the net vertical transport is strongest in high composite reflectivity regions and dominated by upward transport.

Extratropical cyclones with damaging winds can have large socioeconomic impacts when they make landfall. During the last decade, studies have identified a mesoscale transient jet, the sting jet, that descends from the tip of the hooked cloud head toward the top of the boundary layer in the dry intrusion region as a cause of strong surface winds, and especially gusts, in some cyclones. While many case studies have focused on the dynamics and characteristics of these jets, there have been few studies that assess the climatology of the associated cyclones and their importance for wind risk. Here the climatological characteristics of North Atlantic cyclones are determined in terms of the possibility that they had sting jets using a previously published sting-jet precursor diagnostic applied to ERA-Interim data over 32 extended winter seasons from 1979 to 2012. Of the 5447 cyclones tracked, 32% had the precursor (42% in the 22% of cyclones that developed explosively). Precursor storms have a more southerly and zonal storm track than storms without the precursor, and precursor storms tend to be more intense as defined by 850-hPa relative vorticity. This study also shows that precursor storms are the dominant cause of cyclone-related resolved strong wind events over the British Isles for 850-hPa wind speeds exceeding 30 m s−1. Hence, early detection of a sting-jet storm could give advance warning of enhanced wind risk. However, over continental northwestern Europe, precursor cyclone-related windstorm events occur far less often.

Cloud electrification leads to the production of nitrogen oxides (NOx), which has an effect on ozone concentrations. Currently large uncertainties exist regarding the contribution of lightning to the global and local NOx budget, even on a per flash basis. Most lightning NOx (LNOx) models distribute the LNOx at reflectivities (Z) ≥ 20 dBZ in the horizontal, while vertically, a Gaussian distribution function with a peak at − 15 °C is used for cloud-to-ground (CG) flashes and a bimodal distribution function with peaks at − 15 °C and − 45 °C is used for inter- and intra-cloud (IC) flashes. This research aims to improve our basic understanding of lightning location relative to radar Z as a function of storm and flash type. Using data from the North Alabama Lightning Mapping Array (NALMA) and the Multi-Radar Multi-Sensor data suite, the results from analyzing a multicell storm, mesoscale convective system and supercell storm showed that 29.7%, 15.9% and 6.9% of all flashes initiated in regions where Z

On Pentecost Monday, 9 June 2014, a severe linearly organized mesoscale convective system (MCS) hit Belgium and western Germany. This storm was one of the most severe thunderstorms in Germany in decades. The synoptic-scale and mesoscale characteristics of this storm are analyzed based on remote sensing data and in situ measurements. Moreover, the forecast potential of the storm is evaluated using sensitivity experiments with a regional climate model. The key ingredients for the development of the Pentecost storm were the concurrent presence of low-level moisture, atmospheric conditional instability, and wind shear. The synoptic and mesoscale analysis shows that the outflow of a decaying MCS above northern France triggered the storm, which exhibited the typical features of a bow echo like a bookend vortex and a rear-inflow jet. This resulted in hurricane-force wind gusts (reaching 40 m s−1) along a narrow swath in the Rhine–Ruhr region leading to substantial damage. Operational numerical weather prediction models mostly failed to forecast the storm, but high-resolution regional model hindcasts enable a realistic simulation of the storm. The model experiments reveal that the development of the bow echo is particularly sensitive to the initial wind field and the lower-tropospheric moisture content. Adequate initial and boundary conditions are therefore essential for realistic numerical forecasts of such a bow echo event. It is concluded that the Pentecost storm exhibited a comparable structure and a similar intensity to observed bow echo systems in the United States.

Highly localized and persistent back-building mesoscale convective systems represent one of the most dangerous flash-flood-producing storms in the north-western Mediterranean area. Substantial warming of the Mediterranean Sea in recent decades raises concerns over possible increases in frequency or intensity of these types of events as increased atmospheric temperatures generally support increases in water vapour content. However, analyses of the historical record do not provide a univocal answer, but these are likely affected by a lack of detailed observations for older events. In the present study, 20th Century Reanalysis Project initial and boundary condition data in ensemble mode are used to address the feasibility of performing cloud-resolving simulations with 1 km horizontal grid spacing of a historic extreme event that occurred over Liguria: the San Fruttuoso case of 1915. The proposed approach focuses on the ensemble Weather Research and Forecasting (WRF) model runs that show strong convergence over the Ligurian Sea (17 out of 56 members) as these runs are the ones most likely to best simulate the event. It is found that these WRF runs generally do show wind and precipitation fields that are consistent with the occurrence of highly localized and persistent back-building mesoscale convective systems, although precipitation peak amounts are underestimated. Systematic small north-westward position errors with regard to the heaviest rain and strongest convergence areas imply that the reanalysis members may not be adequately representing the amount of cool air over the Po Plain outflowing into the Ligurian Sea through the Apennines gap. Regarding the role of historical data sources, this study shows that in addition to reanalysis products, unconventional data, such as historical meteorological bulletins, newspapers, and even photographs, can be very valuable sources of knowledge in the reconstruction of past extreme events.

An intense and fast moving convective line that crossed Massif Central/Cevennes-Vivarais area (south France) during the field campaign of Hydrological Cycle in Mediterranean Experiment (HyMeX) — Special Observing Period 1 (SOP1) is examined. The mesoscale analysis demonstrates a complex convective system with a V-shape in the Infrared (IR) satellite imagery and a squall line pattern on the radar imagery. Ground stations observed up to 60 mm h− 1 of rain accumulation, while the lightning activity, as observed by 4 detection networks, was also exceptionally high. The Weather Research and Forecasting (WRF) model was used to simulate this convective episode and sensitivity tests were performed with various microphysics and convective parameterization schemes. Satellite data from Meteosat SEVIRI Rapid Scanning Service were used in conjunction with radar, lightning and rain gauge data to conclude on the best simulation for which WRF model exhibits a rather precise and realistic distribution and evolution of the precipitation patterns. Finally, a study of the microphysics was performed indicating the interconnection of graupel with lightning activity, confirming recent results, compared against a sophisticated hydrometeor classification radar algorithm and lightning data.

Severe weather events are responsible for hundreds of fatalities and millions of euros of damage every year on the Mediterranean basin. Lightning activity is a characteristic phenomenon of severe weather and often accompanies torrential rainfall, which, under certain conditions like terrain type, slope, drainage, and soil saturation, may turn into flash flood. Building on the existing relationship between significant lightning activity and deep convection and precipitation, the performance of the Lightning Potential Index, as a measure of the potential for charge generation and separation that leads to lightning occurrence in clouds, is here evaluated for the V-shape back-building Mesoscale Convective System which hit Genoa city (Italy) in 2014. An ensemble of Weather Research and Forecasting simulations at cloud-permitting grid spacing (1km) with different microphysical parameterizations is performed and compared to the available observational radar and lightning data. The results allow gaining a deeper understanding of the role of lightning phenomena in the predictability of V-shape back-building Mesoscale Convective Systems often producing flash flood over western Mediterranean complex topography areas. Moreover, they support the relevance of accurate lightning forecasting for the predictive ability of these severe events.

The predictability and dynamics of the warm-core mesovortex associated with the northern flank of the 8 May 2009 “super derecho” event are examined by coupling the Advanced Research Weather Research and Forecasting Model with the ensemble adjustment Kalman filter implementation within the Data Assimilation Research Testbed facility. Cycled analysis started at 1200 UTC 2 May 2009, with observations assimilated every 6 h until 1200 UTC 7 May 2009, at which time a 50-member ensemble of 36-h convection-allowing ensemble forecasts were launched. The ensemble forecasts all simulated a mesoscale convective system, but only 7 out of 50 members produced a warm-core mesovortex-like feature similar in intensity to the observed mesovortex. Ensemble sensitivity and composite analyses were conducted to analyze the environmental differences between ensemble members. A more amplified upstream upper-level trough near the time of observed convection initiation is associated with a stronger simulated mesovortex. The amplification of the trough results in increases in the magnitudes of the low-level jet and thermal gradient. Consequently, more moisture is transported poleward into western Kansas, leading to earlier convection initiation in ensemble members with the strongest mesovortices. A circulation budget is performed on the ensemble members with the strongest (member 10) and weakest (member 5) time-averaged circulations. The ascending front-to-rear flow, descending rear-to-front flow, and divergent low-level flow of an MCS are more prominent in member 10, which is hypothesized to allow for the convergence of more background cyclonic absolute vorticity and, thus, facilitating the development of a stronger mesovortex.

This study investigates how the increasing concentration of black carbon aerosols, which act as radiation absorbers as well as agents for the cloud-particle nucleation, affects stability, dynamics and microphysics in a multiple-cloud system using simulations. Simulations show that despite increases in stability due to increasing concentrations of black carbon aerosols, there are increases in the averaged updraft mass fluxes (over the whole simulation domain and period). This is because aerosol-enhanced evaporative cooling intensifies convergence near the surface. This increase in the intensity of convergence induces an increase in the frequency of updrafts with the low range of speeds, leading to the increase in the averaged updraft mass fluxes. The increase in the frequency of updrafts induces that in the number of condensation entities and this leads to more condensation and cloud liquid that acts to be a source of the accretion of cloud liquid by precipitation. Hence, eventually, there is more accretion that offsets suppressed autoconversion, which results in negligible changes in cumulative precipitation as aerosol concentrations increase. The increase in the frequency of updrafts with the low range of speeds alters the cloud-system organization (represented by cloud-depth spatiotemporal distributions and cloud-cell population) by supporting more low-depth clouds. The altered organization in turn alters precipitation spatiotemporal distributions by generating more weak precipitation events. Aerosol-induced reduction in solar radiation that reaches the surface induces more occurrences of small-value surface heat fluxes, which in turn supports the more low-depth clouds and weak precipitation together with the greater occurrence of low-speed updrafts.

In this study, an ice-phase microphysics forward model has been developed for the Weather Research and Forecasting (WRF) Model three-dimensional variational data assimilation (WRF 3D-Var) system. Radar forward operators for reflectivity and the polarimetric variable, specific differential phase ( KDP), have been built into the ice-phase WRF 3D-Var package to allow modifications in liquid (cloud water and rain) and solid water (cloud ice and snow) fields through data assimilation. Experiments have been conducted to assimilate reflectivity and radial velocity observations collected by the Weather Surveillance Radar-1988 Doppler (WSR-88D) in Hytop, Alabama, for a mesoscale convective system (MCS) on 15 March 2008. Numerical results have been examined to assess the impact of the WSR-88D data using the ice-phase WRF 3D-Var radar data assimilation package. The main goals are to first demonstrate radar data assimilation with an ice-phase microphysics forward model and second to improve understanding on how to enhance the utilization of radar data in numerical weather prediction. Results showed that the assimilation of reflectivity and radial velocity data using the ice-phase system provided significant improvement especially in the mid- to upper troposphere. The improved initial conditions led to apparent improvement in the short-term precipitation forecast of the MCS. An additional experiment has been conducted to explore the assimilation of KDP data collected by the Advanced Radar for Meteorological and Operational Research (ARMOR). Results showed that KDP data have been successfully assimilated using the ice-phase 3D-Var package. A positive impact of the KDP data has been found on rainwater in the lower troposphere and snow in the mid- to upper troposphere.

Main objective of the present paper is to examine the role of various parameterization schemes in simulating the evolution of mesoscale convective system (MCS) occurred over south-east India. Using the Weather Research and Forecasting (WRF) model, numerical experiments are conducted by considering various planetary boundary layer, microphysics, and cumulus parameterization schemes. Performances of different schemes are evaluated by examining boundary layer, reflectivity, and precipitation features of MCS using ground-based and satellite observations. Among various physical parameterization schemes, Mellor–Yamada–Janjic (MYJ) boundary layer scheme is able to produce deep boundary layer height by simulating warm temperatures necessary for storm initiation; Thompson (THM) microphysics scheme is capable to simulate the reflectivity by reasonable distribution of different hydrometeors during various stages of system; Betts–Miller–Janjic (BMJ) cumulus scheme is able to capture the precipitation by proper representation of convective instability associated with MCS. Present analysis suggests that MYJ, a local turbulent kinetic energy boundary layer scheme, which accounts strong vertical mixing; THM, a six-class hybrid moment microphysics scheme, which considers number concentration along with mixing ratio of rain hydrometeors; and BMJ, a closure cumulus scheme, which adjusts thermodynamic profiles based on climatological profiles might have contributed for better performance of respective model simulations. Numerical simulation carried out using the above combination of schemes is able to capture storm initiation, propagation, surface variations, thermodynamic structure, and precipitation features reasonably well. This study clearly demonstrates that the simulation of MCS characteristics is highly sensitive to the choice of parameterization schemes.

Sprites are brief optical emissions occurring above thunderstorms. Features of sprites and their parent thunderstorms and lightning activities have been studied by many researchers. Here, we report a single sprite recorded over a mesoscale convective system during its life cycle in Northeast China. The results show that the sprite might have been a dancing one, with a 20 km horizontal displacement from its parent cloud-to-ground flash (CG) and a 38 ms time delay; all the sprite elements occurred during the continuing current process of the parent flash. The peak current of the parent CG was the largest during the almost one-hour time window containing the sprite, and the absolute values of all the negative flashes were smaller than 100 kA during the same time period and did not produce sprite. The sprite did not occur during the time period in which the maximum area of the thunderstorm reached. The occurrence of sprite corresponded well with the decay of the thunderstorm convection, and no significant relationship between the occurrence of sprite and the increase in the 30–35 dBZ and 35–40 dBZ interval was found. The large wind gradient in the 8–12 km region of the thunderstorm may have played an important role in the sprite production.

Polarimetric radar variables are simulated from members of the 2013 Center for Analysis and Prediction of Storms (CAPS) Storm-Scale Ensemble Forecasts (SSEF) with varying microphysics (MP) schemes and compared with observations. The polarimetric variables provide information on hydrometeor types and particle size distributions (PSDs), neither of which can be obtained through reflectivity (Z) alone. The polarimetric radar simulator pays close attention to how each MP scheme [including single- (SM) and double-moment (DM) schemes] treats hydrometeor types and PSDs. The recent dual-polarization upgrade to the entire WSR-88D network provides nationwide polarimetric observations, allowing for direct evaluation of the simulated polarimetric variables. Simulations for a mesoscale convective system (MCS) and supercell cases are examined. Five different MP schemes—Thompson, DM Milbrandt and Yau (MY), DM Morrison, WRF DM 6-category (WDM6), and WRF SM 6-category (WSM6)—are used in the ensemble forecasts. Forecasts using the partially DM Thompson and fully DM MY and Morrison schemes better replicate the MCS structure and stratiform precipitation coverage, as well as supercell structure compared to WDM6 and WSM6. Forecasts using the MY and Morrison schemes better replicate observed polarimetric signatures associated with size sorting than those using the Thompson, WDM6, and WSM6 schemes, in which such signatures are either absent or occur at abnormal locations. Several biases are suggested in these schemes, including too much wet graupel in MY, Morrison, and WDM6; a small raindrop bias in WDM6 and WSM6; and the underforecast of liquid water content in regions of pure rain for all schemes.