Characteristics Of Convective Storms Research Articles

Assessing RRFS versus HRRR in Predicting Widespread Convective Systems over the Eastern CONUS

Abstract This study provides a comparison of the operational HRRR version 4 and its eventual successor, the experimental Rapid Refresh Forecast System (RRFS) model (summer 2022 version), at predicting the evolution of convective storm characteristics during widespread convective events that occurred primarily over the eastern United States during summer 2022. In total 32 widespread convective events were selected using observations from the MRMS composite reflectivity, which includes an equal number of MCSs, quasi-linear convective systems (QLCSs), clusters, and cellular convection. Each storm system was assessed on four primary characteristics: total storm area, total storm count, storm area ratio (an indicator of mean storm size), and storm size distributions. It was found that the HRRR predictions of total storm area were comparable to MRMS, while the RRFS overpredicted total storm area by 40%–60% depending on forecast lead time. Both models tended to underpredict storm counts particularly during the storm initiation and growth period. This bias in storm counts originates early in the model runs (forecast hour 1) and propagates through the simulation in both models indicating that both miss storm initiation events and/or merge individual storm objects too quickly. Thus, both models end up with mean storm sizes that are much larger than observed (RRFS more so than HRRR). Additional analyses revealed that the storm area and individual storm biases were largest for the clusters and cellular convective modes. These results can serve as a benchmark for assessing future versions of RRFS and will aid model users in interpreting forecast guidance.

Technical Evaluation of Precipitation Forecast by Blending Weather Radar Based on New Spatial Test Method

The Fourier–Merlin transform method, multi-scale optical flow method, and Weibull distribution are used to integrate the GRAPES_3 km model and Radar Extrapolation Forecast (REF) both developed independently by China. Taking GRAPES_3 km, Wuhan Rapid Update Cycle (WHRUC), and the REF as examples, the prediction performance of the Blending forecast is evaluated comprehensively by the traditional point-to-point method. A new spatial test method is introduced to evaluate the applicability and difference of high-resolution model evaluation. The area, position, shape, and intensity of the precipitation area are matched through the target object test method. The potential forecast information of the spatial field is obtained and the related results are compared and analyzed. The results show that: (1) the comprehensive application of various evaluation methods can evaluate the convective storm forecast more comprehensively. The Blending forecast effect is obviously better than those of other models by using the point-to-point scoring method, especially in the heavy precipitation forecast. The shorter the prediction time is, the better the effect is. (2) The new spatial test method can evaluate the prediction effect of convective storm characteristics, and the target recognition hit rate of the Blending forecast is highest. The scores of target area, position, shape, and median intensity of precipitation are better than those of other forecasts. The variation in the east–west direction is less than that in the north–south direction, which is basically consistent with the actual observation. The variation range of the forecast grid before and after translation is the closest to the reality. (3) The Blending forecast method combines the advantages and disadvantages of the numerical model and REF, which can not only grasp the precipitation area but also improve the prediction ability of rainfall intensity. The traditional point-to-point scoring method and the new spatial test method have the same conclusion as the convective storm forecast of the high-resolution model, which has a certain reference value, and the new spatial test method can provide more detailed evaluation information.

Radar observed convective storm characteristics at the eastern edge of the Indian summer monsoon trough

Radar observed convective storm characteristics at the eastern edge of the Indian summer monsoon trough

Biases in the Prediction of Convective Storm Characteristics with a Convection Allowing Ensemble

Abstract The severity, duration, and spatial extent of thunderstorm impacts are related to convective storm mode. This study assesses the skill of the High-Resolution Rapid Refresh Ensemble (HRRR-E) and its deterministic counterpart (HRRRv4) at predicting convective mode and storm macrophysical properties using 35 convective events observed during the 2020 warm season across the eastern United States. Seven cases were selected from each of five subjectively determined convective organization modes: tropical cyclones, mesoscale convective systems (MCSs), quasi-linear convective systems, clusters, and cellular convection. These storm events were assessed using an object-based approach to identify convective storms and determine their individual size. Averaged across all 35 cases, both the HRRR-E and HRRRv4 predicted storm areas were generally larger than observed, with this bias being a function of storm lifetime and convective mode. Both modeling systems also underpredicted the rapid increase in storm counts during the initiation period, particularly for the smaller-scale storm modes. Interestingly, performance of the HRRRv4 differed from that of the HRRR-E, with the HRRRv4 generally having a larger bias in total storm area than the HRRR-E due to HRRRv4 predicting up to 66% more storm objects than the HRRR-E. The HRRR-E accurately predicted the convective mode 65% of the time, with complete misses being very rare (<5% of the time overall). However, an evaluation of rank histograms across all 35 cases revealed that the HRRR-E tended to be underdispersive when predicting storm size for all but the MCS mode.

Lightning Geolocation and Flash Rates From LF Radio Observations During the RELAMPAGO Field Campaign.

The lightning data products generated by the low‐frequency (LF) radio lightning locating system (LLS) deployed during the Remote sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observation (RELAMPAGO) field campaign in Argentina provide a valuable data set to research the lightning evolution and characteristics of convective storms that produce high‐impact weather. LF LLS data sets offer a practical range for mesoscale studies, allowing for the observation of lightning characteristics of storms such as mesoscale convective systems or large convective lines that travel longer distances which are not necessarily staying in range of regional VHF‐based lightning detection systems throughout their lifetime. LF LLSs also provide different information than optical space‐borne lightning detectors. Lightning measurements exclusive to LF systems include discharge peak current, lightning polarity, and lightning type classification based on the lightning‐emitted radio waveform. Furthermore, these measurements can provide additional information on flash rates (e.g., positive cloud‐to‐ground flash rate) or narrow bipolar events which may often be associated with dynamically intense convection. In this article, the geolocation and data processing of the LF data set collected during RELAMPAGO is fully described and its performance characterized, with location accuracy better than 10 km. The detection efficiency (DE) of the data set is compared to that of the Geostationary Lightning Mapper, and spatiotemporal DE losses in the LF data set are discussed. Storm case studies on November 10, 2018, highlight the strengths of the data set, which include robust flash clustering and insightful flash rate and peak current measures, while illustrating how its limitations, including DE losses, can be managed.

Power laws in intra-storm temporal rainfall variability

During a storm event, rainfall intensity rarely remains uniform but rather shows radical temporal variation. Given the time window w within a storm, the maximum rainfall intensity over w, namely iw decreases as w increases. Our analysis of individual storm events using ground measurements of 1-minute temporal resolution reveals that the iw–w relationship follows either a single or broken power law. Such scale-invariance is likely associated with known fractal or multi-fractal characteristics of rainfall structure. In broken power-law events, two power-law exponents of βm (at minute time scale) and βh (at hourly time scale) are fitted for segments separated by the characteristic time τ. Mostly βm < βh, implying the persistence of high rainfall concentration prior to τ. This reflects the characteristics of convective storms, and the range of τ agrees with the known time span of convective storm duration. The more concentrated storm event exhibits the stronger persistence (a smaller βm) prior to τ and the faster moisture depletion (a greater βh) thereafter. The knowledge gained from this study provides important implications to the existing design storm formula, often called the Mononobe formula: observed scale-free intra-storm variability is in accordance with this formula while the scaling transition at sub-hourly time scale explains the known limitations of its practical applications.

Connecting flash flood events with radar-derived convective storm characteristics on the northwestern Mediterranean coast: knowing the present for better future scenarios adaptation

The northwestern Mediterranean coast is one of the areas most affected by flash floods and adverse weather, where also global climate scenarios are predicting an increase in precipitation extremes. This, along with the sea-level rise and the associated potential infrastructure and economical losses, represents a major risk for coastal settlements, as the one studied in this work: a major touristic area on the southern coast of Catalonia. The objective of the present paper is, thus, to understand in-depth the behavior of convective activity in the area and its link with flash flood events, to improve adverse and severe weather early warning systems in a rapidly changing weather as an adaptation measure. With this purpose, the study focuses on the period 2014–2018, analyzing the convective cells and storm motion in the region of study, through the new identification and tracking algorithm developed by the authors and applied in the Meteorological Service of Catalonia. The results show that the area is mostly affected by shallow but efficient convection, probably embedded in major weather systems with high precipitation rain rates, which tends to saturate the catchments and produce flooding. The work also shows the role of the topography as a triggering factor for storms outside the convective season, as a key feature for the convergence lines organization, and as a capping for stationary cells upstream. Improvements in the current warning system, such as different precipitation thresholds considering the time of exposure to that rain, as well as a further study are also presented as main results. These results can be extrapolated and applied to other regions affected by the same kind of heavy precipitation and flash floods.

Microphysical and Dynamical Effects of Mixed-Phase Hydrometeors in Convective Storms Using a Bin Microphysics Model: Melting

AbstractThe dynamics of convective systems are inherently linked to microphysical processes through phase changes that result in warming or cooling. This is especially true of near-surface cooling via evaporation and melting of falling hydrometeors. In most numerical simulations, the melting of frozen hydrometeors (e.g., hail, graupel, snow) is computed within parameterized bulk microphysics schemes, many of which lack the ability to accurately represent mixed-phase hydrometeors (i.e., partially melted ice), which can affect hydrometeor sedimentation, melting, and evaporation of shed drops. To better understand the microphysical and dynamical effects of melting in convective storms, a bin microphysics scheme was used in the Weather Research and Forecasting Model for two idealized cases: a supercell storm and a squall line. Physically based predicted liquid fraction, instantaneous melting, and instantaneous shedding schemes were used to examine the role and importance of melting hydrometeors for these two storm modes. The results suggest that the amount of precipitation is dependent on the representation of melting. Moreover, the dynamic and thermodynamic characteristics of the simulated storms are found to differ substantially between the melting scenarios, resulting in varied storm system evolution; these differences are found to be dependent on the ambient aerosol concentration, although the differences induced by changing the representation of melting generally outweigh those of changing the aerosol loading. The results highlight the large role of melting in convective storm characteristics and suggest that further model improvements are needed in the near future.

Radar-derived convective storms' climatology for the Prut River basin: 2003–2017

Abstract. Weather radar measurements are used to study the climatology of convective storms and their characteristics in the transboundary Prut River basin. The Storm Cell Identification and Tracking (SCIT) algorithm was used to process the volumetric reflectivity measurements, in order to identify, characterize, and track the convective storm cells. The storm attribute table output of the algorithm was used to separate the convective from the stratiform storm cells, by applying a simple selection criterion based on the average vertically integrated liquid (VIL) values. The radar-derived characteristics of convective storms were used to document the spatial and temporal distributions and storm properties in terms of duration, distance travelled, movement direction, and intensity. The results show that 94.3 % of all convective storm cells were detected during May–August, with the peak in July. The peak time for convective storm cells' occurrence was in the afternoon and evening hours between 10:00 and 18:00 UTC. The median duration of a convective storm was 42 min, the median distance travelled was 23 km, and the median movement speed was 7.7 m s−1. The average movement of storms varied with months, but overall most convective storms move from the south-west and south–south-east. Also, the analysis shows that the longer-lasting convective storms were the most intense. The spatial distribution of the convective cells reveals yearly variation patterns and hotspots but also highlights the limitations of radar measurement at longer distances. Reanalysis data suggest that low values of sea level pressure over the Black Sea can act as a dynamical driver of convective storms in the analysed area.

Object-Based Evaluation of a Numerical Weather Prediction Model’s Performance through Forecast Storm Characteristic Analysis

AbstractTraditional pixel-versus-pixel forecast evaluation scores such as the critical success index (CSI) provide a simple way to compare the performances of different forecasts; however, they offer little information on how to improve a particular forecast. This paper strives to demonstrate what additional information an object-based forecast evaluation tool such as the Method for Object-Based Diagnostic Evaluation (MODE) can provide in terms of assessing numerical weather prediction models’ convective storm forecasts. Forecast storm attributes evaluated by MODE in this paper include storm size, intensity, orientation, aspect ratio, complexity, and number of storms. Three weeks of the High Resolution Rapid Refresh (HRRR) model’s precipitation forecasts during the summer of 2010 over the eastern two-thirds of the contiguous United States were evaluated as an example to demonstrate the methodology. It is found that the HRRR model was able to forecast convective storm characteristics rather well either as a function of time of day or as a function of storm size, although significant bias does exist, especially in terms of storm number and storm size. Another interesting finding is that the model’s ability of forecasting new storm initiation varies substantially by regions, probably as a result of its different skills in forecasting convection driven by different forcing mechanisms (i.e., diurnal heating vs synoptic-scale frontal systems).

On the sensitivity of urban hydrodynamic modelling to rainfall spatial and temporal resolution

Abstract. Cities are increasingly vulnerable to floods generated by intense rainfall, because of urbanisation of flood-prone areas and ongoing urban densification. Accurate information of convective storm characteristics at high spatial and temporal resolution is a crucial input for urban hydrological models to be able to simulate fast runoff processes and enhance flood prediction in cities. In this paper, a detailed study of the sensitivity of urban hydrodynamic response to high resolution radar rainfall was conducted. Rainfall rates derived from X-band dual polarimetric weather radar were used as input into a detailed hydrodynamic sewer model for an urban catchment in the city of Rotterdam, the Netherlands. The aim was to characterise how the effect of space and time aggregation on rainfall structure affects hydrodynamic modelling of urban catchments, for resolutions ranging from 100 to 2000 m and from 1 to 10 min. Dimensionless parameters were derived to compare results between different storm conditions and to describe the effect of rainfall spatial resolution in relation to storm characteristics and hydrodynamic model properties: rainfall sampling number (rainfall resolution vs. storm size), catchment sampling number (rainfall resolution vs. catchment size), runoff and sewer sampling number (rainfall resolution vs. runoff and sewer model resolution respectively). Results show that for rainfall resolution lower than half the catchment size, rainfall volumes mean and standard deviations decrease as a result of smoothing of rainfall gradients. Moreover, deviations in maximum water depths, from 10 to 30% depending on the storm, occurred for rainfall resolution close to storm size, as a result of rainfall aggregation. Model results also showed that modelled runoff peaks are more sensitive to rainfall resolution than maximum in-sewer water depths as flow routing has a damping effect on in-sewer water level variations. Temporal resolution aggregation of rainfall inputs led to increase in de-correlation lengths and resulted in time shift in modelled flow peaks by several minutes. Sensitivity to temporal resolution of rainfall inputs was low compared to spatial resolution, for the storms analysed in this study.

Assimilation of Doppler radar observations with an ensemble square root filter: A squall line case study

The effectiveness of using an Ensemble Square Root Filter (EnSRF) to assimilate real Doppler radar observations on convective scale is investigated by applying the technique to a case of squall line on 12 July 2005 in midwest Shandong Province using the Weather Research and Forecasting (WRF) model. The experimental results show that: (1) The EnSRF system has the potential to initiate a squall line accurately by assimilation of real Doppler radar data. The convective-scale information has been added into the WRF model through radar data assimilation and thus the analyzed fields are improved noticeably. The model spin-up time has been shortened, and the precipitation forecast is improved accordingly. (2) Compared with the control run, the deterministic forecast initiated with the ensemble mean analysis of EnSRF produces more accurate prediction of microphysical fields. The predicted wind and thermal fields are reasonable and in accordance with the characteristics of convective storms. (3) The propagation direction of the squall line from the ensemble mean analysis is consistent with that of the observation, but the propagation speed is larger than the observed. The effective forecast period for this squall line is about 5–6 h, probably because of the nonlinear development of the convective storm.

Analysis of a statistically initialized fuzzy logic scheme for classifying the severity of convective storms in Finland

This paper proposes a method for classifying the severity of individual convective storms with real-time weather radar and lightning location data. The algorithm is based on a statistically initialized fuzzy logic model with human-oriented linguistic inference rules. When combined with an object-oriented convective storm tracking algorithm, the severity classification uses the past severity values in addition to the current state of the storm. Furthermore, the proposed method can be customized to correspond to the user-specific needs of different end user groups. The membership functions of the fuzzy logic model are initialized using the statistical analysis of various storm attributes derived from radar and lightning data, which potentially allows the adaptation of the model to different climates. The statistically initialized severity classification is also stable with respect to systematic errors in the measurements. To adjust the model to the Finnish climate, approximately 40 000 storms were tracked with a weather radar-based object-oriented convective storm tracking algorithm. The tracking was performed over a relatively large study area, with the composite of eight C-band weather radars, enabling also the analysis of relatively long-lived intense storms, such as mesoscale convective systems. In addition, the presented work illustrates the statistical characteristics of convective storms in Finland. The study also discusses the importance of careful data quality control when conducting a statistical analysis with an object-oriented storm tracking algorithm. Copyright © 2013 Royal Meteorological Society

Statistical Characteristics of Convective Storms in Belgium Derived from Volumetric Weather Radar Observations

AbstractHigh-resolution volumetric reflectivity measurements from a C-band weather radar are used to study the characteristics of convective storms in Belgium. After clutter filtering, the data are processed by the storm-tracking system Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) using a 40-dBZ reflectivity threshold. The 10-yr period of 5-min data includes more than 1 million identified storms, mostly organized in clusters. A storm is observed at a given point 6 h yr−1 on average. Regions of slightly higher probability are generally correlated with orographic variations. The probability of at least one storm in the study area is 15%, with a maximum of 35% for July and August. The number of storms, their coverage, and their water mass are limited most of the time. The probability to observe a high number of storms reaches a maximum in June and in the early afternoon in phase with solar heating. The probability of large storm coverage and large water mass is highest in July and in the late afternoon. Convective storms are mostly small and weak. Deeper ones are found mainly in the afternoon whereas bigger and more intense ones also appear in the evening. The occurrence of the most intense storms does not vary along the day. Simple tracks have a mean duration of 25 min. Complex tracks, involving splitting or merging, last 70 min on average. Most convective storms move in the northeast direction, with a median speed of 30 km h−1. Their motion is slower in summer and in the afternoon. Regions with slightly higher convective initiation are related to orography.

Statistic characteristics of severe convective storm during Warm-Season in the Beijing-Tianjin region and its vicinity

This study analyzed the climatological characteristics of severe convective storms in the Beijing and Tianjin region and its vicinity based on the Doppler radar data of Tanggu during May–August of 2003–2007. The climatological characteristics, e.g. storm area, volume, top height, max reflectivity, life time and motion, are analyzed. The results include: 75% of all storms in the Beijing-Tianjin region last no more than 30 minutes, and most storms have a volume less than 400 km3; most storms move from southwest to northeast while the speed is between 10–30 km/h; the mean storm top height is about 6 km, but some strong convective storms can have a top height larger than 15 km; finally, storm area and volume have a similar geographical distribution character showing increasing trends from west to east. Compared with the statistic results based on the conventional surface meteorological observations, the results based on the radar data can present not only 3D spatial statistic results of convective storms (e.g., volume and top height), but also the quantitative climatological characteristics, such as the lifetime and speed distributions. These statistical results are useful for studying the climatic characteristics of convective storms in the Beijing-Tianjin region and its vicinity.

Statistic characteristics and weather significance of infrared TBB during May–August in Beijing and its vicinity

In order to meet the demand of nowcasting convective storms in Beijing, the climatological characteristics of convective storms in Beijing and its vicinity were analyzed based on the infrared (IR) temperature of black body (TBB) data during May–August of 1997–2004. The climatological probabilities, the diurnal cycle and the spatial distribution of convective storms are given respectively in this paper. The results show that the climatological characteristics of convective storms denoted by TBB⩽−52°C are consistent with those statistic studies based on the surface and lightning observations. Furthermore, the climatological characteristics of May and June are very different from those of July and August, showing that there are two types of convective storms in this region. One occurs in the transient polar air mass on the midlatitude continent during the late spring and early summer. This type of convection arises with thunder, strong wind gust and hail over the mountainous area in the northern part of this region from afternoon to nightfall, the other occurs with heavy rainfall in the warm and moist air mass over the North China Plain and vicinity of Bohai Sea. This study also shows that the long-term data of IR TBB observed by geostationary satellite can complement the temporal and spatial limitation of the weather radar and surface observations.

Impacts of nucleating aerosol on anvil-cirrus clouds: A modeling study

This paper examines the potential effects of Saharan dust intrusions on the microphysical structure and optical properties of anvil-cirrus clouds. A series of 3-D LES simulations were initialized and forced by output data from mesoscale simulations in a previous study, in which we analyzed the impact of the enhanced low-level concentrations of cloud-nucleating aerosols on the characteristics of convective storms. The effects of enhancing aerosol concentrations on the ice-particle size distributions as well as some of their moments were analyzed as the LES model domain followed the trajectory of the simulated cirrus cloud. The experimental design was based on aerosol concentrations observed over the peninsula of Florida toward the end of the CRYSTAL-FACE field campaign held during July 2002. Results indicate that variations in the concentrations of nucleation aerosols have a significant effect on the optical properties and lifetime of cirrus anvil clouds. In addition, enhancing low-level aerosols can affect the radiation budget, leading to surface radiative cooling. Both IFN and CCN enhancements show important effects; however, results suggest that CCN and GCCN play a more dominant role.

Impacts of Nucleating Aerosol on Florida Storms. Part I: Mesoscale Simulations

Abstract Toward the end of the Cirrus Regional Study of Tropical Anvils and Cirrus Layer–Florida Area Cirrus Experiment (CRYSTAL–FACE) field campaign held during July 2002, high concentrations of Saharan dust, which can serve as cloud condensation nuclei (CCN), giant CCN (GCCN), and ice-forming nuclei (IFN) were observed over the peninsula of Florida. To investigate the impacts of enhanced aerosol concentrations on the characteristics of convective storms and their subsequent anvil development, sensitivity tests are conducted using the Regional Atmospheric Modeling System (RAMS) model, in which the initialization profiles of CCN, GCCN, and IFN concentrations are varied. These variations are found to have significant effects on the storm dynamics and microphysical processes, as well as on the surface precipitation. Updrafts are consistently stronger as the aerosol concentrations are increased. The anvils cover a smaller area but are better organized and have larger condensate mixing ratio maxima in the cases with greater aerosol concentrations. Cloud water mass tends to increase with increasing aerosol concentrations, with enhanced GCCN concentrations having the most significant influence. Increasing either the GCCN or IFN concentrations produces the most rainfall at the surface whereas enhanced CCN concentrations reduce surface rainfall. Higher IFN concentrations produce ice at warmer temperatures and deeper anvils, but simultaneously increasing the concentrations of CCN and GCCN leads to more supercooled liquid water available for freezing and greater ice mixing ratios. Graupel mixing ratios decrease and hail mixing ratios increase with increasing aerosol concentrations. Higher concentrations of GCCN and IFN result in greater accumulated surface precipitation initially. By the end of the simulation period, however, the accumulated precipitation is the greatest for the case in which the aerosol concentrations are lowest. Such changes in the dynamical and microphysical characteristics of convective storms as a result of the variations in aerosol concentrations have potential climate consequences, both through cloud radiative effects and the hydrological cycle. The impacts of varying CCN, GCCN, and IFN concentrations on the anvils will be discussed more fully in Part II.

The Storm‐Structure‐Severity method for the identification of convective storm characteristics with conventional weather radar

AbstractRadar reflectivity information is often displayed in two dimensions, making it difficult to extract the structural characteristics of convective storms. The maximum radar reflectivity and the vertical profile of liquid water distribution in a vertical column of a convective cell is used to determine a structural and intensity classification of the cell. The application of this Storm‐Structure‐Severity classification is demonstrated by investigating a tornado event on 15 November 1998 in Harrismith, South Africa, by using volume‐scanned reflectivity data from the S‐band MRL‐5 radar. The regions of convective development, decay and mesocyclone evolution are identified with this classification. The method is able to extract the three‐dimensional structural information of convective storms embedded in volume‐scanned reflectivity data and present it in a simple display format. Copyright © 2001 Royal Meteorological Society

Severe Storm Identification with Satellite Microwave Radiometry: An Initial Investigation with Nimbus-7 SMMR Data

The severe weather characteristics of convective storms as observed by the Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) are investigated. Low 37 GHz brightness temperatures (due to scattering of upwelling radiation by precipitation size ice) are related to the occurrence of severe weather (large hail, strong winds or wind damage, tornadoes and funnel clouds) within one hour of the satellite observation time. During 1979 and 1980 over the United States there were 263 storms which had very cold 37 GHz signatures. Of these storms 15% were severe. The SMMR detected hail, wind, and tornadic storms equally well. Critical Success Indices (CSI's) of 0.32, 0.48, and 0.38 are achieved for the thresholding of severe vs. nonsevere low brightness temperature events during 1979, 1980, and the two years combined, respectively. Such scores are comparable to skill scores for early radar detection methods. These results suggest that a future geostationary passive microwave imaging capability at 37 GHz, with sufficient spatial and temporal resolution, would allow the detection of severe convective storms. This capability would provide a useful complement to radar, especially in areas not covered by radar.