Severe Convection Research Articles

Weather radar echo prediction method based on convolution neural network and Long Short-Term memory networks for sustainable e-agriculture

As one of the main reference data of weather forecast, weather radar echo image is very important to the stability of agricultural production. Different radar echo patterns represent different disastrous weather, such as hail, severe convection, and so on. Weather radar echo shape prediction can help meteorologists to judge the future changes of disastrous weather, help to avoid the harm of extreme weather to agriculture, and minimize agricultural economic losses. With the application of deep learning in the meteorological field, the deep learning method shows great potential in radar echo prediction. However, there are few research methods to predict the change of weather radar echo shape. This paper presents a radar echo prediction method based on c Convolutional Neural Networks and Long Short-Term Memory networks, which can effectively predict the shape of weather radar echo. The actual data is used to train and test our model. Experiments show that the model can accurately predict the change of echo shape. The quantitative evaluation of the model uses detection probability, false alarm rate, critical success index, and heidke skill score. The average scores predicted by this model in 1.5 h were 0.8223,0.2012,0.6812 and 0.7564, respectively, which were better than those predicted by ConvLSTM and TrajGRU models. The qualitative and quantitative results verify the effectiveness of the model, which shows that the model can be effectively applied to the actual weather forecast and improve the stability of agricultural production.

Impacts of the radar data assimilation frequency and large-scale constraint on the short-term precipitation forecast of a severe convection case

Radar data assimilation (DA) can improve short-term precipitation forecasts. However, assimilating radar observations into a numerical prediction model may produce spurious precipitation and large errors in the position and magnitude of precipitation, and high-frequency cyclic assimilation may exacerbate this problem. In this paper, a severe convection case that occurred on 27 June 2018 over eastern China was used to investigate the impacts of the radar DA frequency and large-scale constraint on precipitation forecasts. The Weather Research and Forecasting (WRF) three-dimensional variational (3D-Var) DA system was used to assimilate radial velocity and reflectivity data. The sensitivity of the DA frequency using 15-min and 1-h assimilation intervals was examined first, revealing that the 15-min DA interval produced a greater overprediction bias of precipitation. A diagnosis of the analysis fields showed that higher-frequency DA cycling produced excessive wind speed and water vapor convergence at low levels and exaggerated upward movement. Then, the large-scale constraint was imposed on the regional model by the spectral nudging (SN) technique, which assimilated Global Forecast System (GFS) forecast field data during the forecast periods. The results demonstrated that the application of SN could significantly reduce the positional deviation of the precipitation forecast and the magnitude of overpredicted precipitation. SN could effectively adjust large-scale circulation fields, thereby improving the conditions of water vapor convergence. Moreover, SN also improved the forecasts of surface variables such as wind, temperature, and humidity.

Assessment of convection‐permitting versions of the Unified Model over the Lake Victoria basin region

AbstractThe Met Office runs an operational high‐resolution convection‐permitting model over Tropical Africa to support National Meteorological Services across the continent. This article evaluates the prediction of convective storms in two convection‐permitting versions of the Met Office Unified Model over the Lake Victoria basin region in East Africa. Two model configurations are compared for a case of severe convection: the first was tuned for tropical regions and the second was based on the operational UK model configuration. The tropical configuration compares better with satellite‐derived rainfall observations in terms of domain‐average rain‐rate and the distribution of rain‐rates is better, particularly for the more intense rain‐rates. However, both configurations generally produce too much rain, too many small storms, and a lack of light rain, which is compensated for by heavy rain in the cores of the storms. The tropical configuration was then assessed over two rainy seasons. In both seasons, the onset time of night‐time precipitation over Lake Victoria is delayed and the amount of precipitation is underestimated, which suggests the model may miss night‐time storms that occur over the lake. During the first 12–24 hr of the simulations, the precipitation field is still spinning up from the lower‐resolution global model fields used to initialise the convection‐permitting model. This leads to large overestimates in the domain‐average precipitation during this period and highlights the need to run convection‐permitting models for at least two days to reduce the impact of cold‐starting from a non‐convective‐permitting analysis. Despite the spin‐up issues, fractions skill score analysis shows that forecast skill decreases with increasing lead time. This implies that there is inherent skill in the early stages of the model forecast and underlines that short‐term forecast (0–48 hr) improvements in the model are likely to be achieved when using an improved analysis for initialisation.

XGB+FM for Severe Convection Forecast and Factor Selection

In the field of meteorology, radiosonde data and observation data are critical for analyzing regional meteorological characteristics. Because of the high false alarm rate, severe convection forecasting is still challenging. In addition, the existing methods are difficult to use to capture the interaction of meteorological factors at the same time. In this research, a cascade of extreme gradient boosting (XGBoost) for feature transformation and a factorization machine (FM) for second-order feature interaction to capture the nonlinear interaction—XGB+FM—is proposed. An attention-based bidirectional long short-term memory (Att-Bi-LSTM) network is proposed to impute the missing data of meteorological observation stations. The problem of class imbalance is resolved by the support vector machines–synthetic minority oversampling technique (SVM-SMOTE), in which two oversampling strategies based on the support vector discrimination mechanism are proposed. It is proven that the method is effective, and the threat score (TS) is 7.27~14.28% higher than other methods. Moreover, we propose the meteorological factor selection method based on XGB+FM and improve the forecast accuracy, which is one of our contributions, as well as the forecast system.

Exploring Convection-Allowing Model Evaluation Strategies for Severe Local Storms Using the Finite-Volume Cubed-Sphere (FV3) Model Core

AbstractVerification methods for convection-allowing models (CAMs) should consider the finescale spatial and temporal detail provided by CAMs, and including both neighborhood and object-based methods can account for displaced features that may still provide useful information. This work explores both contingency table–based verification techniques and object-based verification techniques as they relate to forecasts of severe convection. Two key fields in severe weather forecasting are investigated: updraft helicity (UH) and simulated composite reflectivity. UH is used to generate severe weather probabilities called surrogate severe fields, which have two tunable parameters: the UH threshold and the smoothing level. Probabilities computed using the UH threshold and smoothing level that give the best area under the receiver operating curve result in very high probabilities, while optimizing the parameters based on the Brier score reliability component results in much lower probabilities. Subjective ratings from participants in the 2018 NOAA Hazardous Weather Testbed Spring Forecasting Experiment (SFE) provide a complementary evaluation source. This work compares the verification methodologies in the context of three CAMs using the Finite-Volume Cubed-Sphere Dynamical Core (FV3), which will be the foundation of the U.S. Unified Forecast System (UFS). Three agencies ran FV3-based CAMs during the five-week 2018 SFE. These FV3-based CAMs are verified alongside a current operational CAM, the High-Resolution Rapid Refresh version 3 (HRRRv3). The HRRR is planned to eventually use the FV3 dynamical core as part of the UFS; as such evaluations relative to current HRRR configurations are imperative to maintaining high forecast quality and informing future implementation decisions.

Learnable Optical Flow Network for Radar Echo Extrapolation

The impact of extreme weather on maintaining flight schedules is becoming more pronounced. Currently, radar echo extrapolation technology is widely used in the nowcasting of severe convection, in which the optical flow method is a representative example of traditional extrapolation algorithms. By training a large number of known samples to find the optimal solution, the deep extrapolation models have gradually become better than the traditional algorithms in recent years. In this study, after examining the optical flow method and other deep learning models, a learnable optical flow deep model with a fully convolutional structure is proposed. Using the convolutional deep learning of optical flow information, this new model can overcome the kernel size limitation of traditional convolutional neural networks, and it can correlate the data history further in time and space. The six-year radar mosaics of Guangdong Province, China, were used as the data set to independently train and verify the new model. The results reveal that the new model outperformed the traditional optical flow method and it is also better than other deep learning models.

Understanding the dynamical-microphysical-electrical processes associated with severe thunderstorms over the Beijing metropolitan region

The Dynamical-microphysical-electrical Processes in Severe Thunderstorms and Lightning Hazards (STORM973) project conducted coordinated comprehensive field observations of thunderstorms in the Beijing metropolitan region (BMR) during the warm season from 2014 to 2018. The aim of the project was to understand how dynamical, microphysical and electrical processes interact in severe thunderstorms in the BMR, and how to assimilate lightning data in numerical weather prediction models to improve severe thunderstorm forecasts. The platforms used in the field campaign included the Beijing Lightning Network (BLNET, consisting of 16 stations), 2 X-band dual linear polarimetric Doppler radars, and 4 laser raindrop spectrometers. The collaboration also made use of the China Meteorological Administration’s mesoscale meteorological observation network in the Beijing-Tianjin-Hebei region. Although diverse thunderstorm types were documented, it was found that squall lines and multicell storms were the two major categories of severe thunderstorms with frequent lightning activity and extreme rainfall or unexpected local short-duration heavy rainfall resulting in inundations in the central urban area, influenced by the terrain and environmental conditions. The flash density maximums were found in eastern Changping District, central and eastern Shunyi District, and the central urban area of Beijing, suggesting that the urban heat island effect has a crucial role in the intensification of thunderstorms over Beijing. In addition, the flash rate associated with super thunderstorms can reach hundreds of flashes per minute in the central city regions. The super (5% of the total), strong (35%), and weak (60%) thunderstorms contributed about 37%, 56%, and 7% to the total flashes in the BMR, respectively. Owing to the close connection between lightning activity and the thermodynamic and microphysical characteristics of the thunderstorms, the lightning flash rate can be used as an indicator of severe weather events, such as hail and short-duration heavy rainfall. Lightning data can also be assimilated into numerical weather prediction models to help improve the forecasting of severe convection and precipitation at the cloud-resolved scale, through adjusting or correcting the thermodynamic and microphysical parameters of the model.

Vertical Transport, Entrainment, and Scavenging Processes Affecting Trace Gases in a Modeled and Observed SEAC4RS Case Study

AbstractThe convectively driven transport of soluble trace gases from the lower to the upper troposphere can occur on timescales of less than an hour, and recent studies suggest that microphysical scavenging is the dominant removal process of tropospheric ozone precursors. We examine the processes responsible for vertical transport, entrainment, and scavenging of soluble ozone precursors (formaldehyde and peroxides) for midlatitude convective storms sampled on 2 September 2013 during the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) study. Cloud‐resolving simulations using the Weather Research and Forecasting with Chemistry model combined with aircraft measurements were performed to understand the effect of entrainment, scavenging efficiency (SE), and ice physics processes on these trace gases. Analysis of the observations revealed that the SEs of formaldehyde (43–53%) and hydrogen peroxide (~80–90%) were consistent between SEAC4RS storms and the severe convection observed during the Deep Convective Clouds and Chemistry Experiment (DC3) campaign. However, methyl hydrogen peroxide SE was generally smaller in the SEAC4RS storms (4%–27%) compared to DC3 convection. Predicted ice retention factors exhibit different values for some species compared to DC3, and we attribute these differences to variations in net precipitation production. The analyses show that much larger production of precipitation between condensation and freezing levels for DC3 severe convection compared to smaller SEAC4RS storms is largely responsible for the lower amount of soluble gases transported to colder temperatures, reducing the amount of soluble gases which eventually interact with cloud ice particles.

Impact of Model Resolution and Initial/Boundary Conditions in Forecasting Flood-Causing Precipitations

In late summer and autumn Mediterranean coastal regions are quite regularly affected by small-scale, flood-producing convective systems. The complexity of mesoscale triggering mechanisms, related to low-level temperature gradients, moisture convergence, and topographic effects contributes to limit the predictability of such phenomena. In the present work, a severe convection episode associated to a flash flood occurred in Cannes (southern France) in October 2015, is investigated by means of numerical simulations with a state-of-the-art nonhydrostatic mesoscale model. In the modelling configuration operational at the University of Genoa precipitation maxima were underestimated and misplaced. The impact of model resolution as well as initial and boundary conditions on the quantitative precipitation forecasts is analyzed and discussed. In particular, the effect of ingesting a high-resolution satellite-derived sea surface temperature field is proven to be beneficial in terms of precipitation intensity and localization, especially when also associated with the most accurate lateral boundary conditions.

Case Study of a Retrieval Method of 3D Proxy Reflectivity from FY-4A Lightning Data and Its Impact on the Assimilation and Forecasting for Severe Rainfall Storms

As the first Geostationary Satellite with the LMI (Lightning Mapping Imager) instrument aboard running over the eastern hemisphere, FY-4A (Feng-Yun-4A) can better indicate severe convection and compensate for the limitations of radar observation in temporal and spatial resolution. In order to realize the application of FY-4A lightning data in numerical weather prediction (NWP) models, a logarithmic relationship between FY-4A lightning density and maximum radar reflectivity is presented to convert FY-4A lightning data into maximum FY-4A proxy reflectivity. Then, according to the profiles of radar reflectivity, the maximum FY-4A proxy reflectivity is extended to 3D FY-4A proxy reflectivity. Finally, the 3D FY-4A proxy reflectivity is assimilated in RMAPS-ST (Rapid-refresh Multi-scale Analysis and Prediction System—Short Term) to compare with radar assimilation. Four groups of continuous cycling data assimilation and forecasting experiments are carried out for a severe rainfall case. The results demonstrate that cycling assimilation of 3D FY-4A proxy reflectivity can adjust the moisture condition effectively, and indirectly affects the temperature and wind fields, then makes the thermal and dynamic analysis more reasonable. The Fractions Skill Scores (FSSs) show that the rainfall forecasts are improved significantly within 6 h by assimilating 3D FY-4A proxy reflectivity, which is similar to the parallel experiments in assimilating radar reflectivity. In addition, other cycling data assimilation experiments are carried out in mountainous areas without radar data. The improvement of precipitation forecasts in mountainous areas further proves that the application of assimilating 3D FY-4A proxy reflectivity can be considered a useful substitute where observed radar data are missing. Through the two severe rainfall cases, this method could be framed as an example of how to use lightning for data assimilation.

Advances in Severe Convection Research and Operation in China

This article reviews the advances in severe convection research and operation in China during the past several decades. The favorable synoptic situations for severe convective weather (SCW), the major organization modes of severe convective storms (SCSs), the favorable environmental conditions and characteristics of weather radar echoes and satellite images of SCW and SCSs, and the forecasting and nowcasting techniques of SCW, are emphasized. As a whole, Chinese scientists have achieved a profound understanding of the synoptic patterns, organization, and evolution characteristics of SCW from radar and satellite observations, and the mechanisms of different types of convective weather in China. Specifically, in-depth understanding of the multiple types of convection triggers, along with the environmental conditions, structures and organization modes, and maintenance mechanisms of supercell storms and squall lines, has been obtained. The organization modes and climatological distributions of mesoscale convective systems and different types of SCW, and the multiscale characteristics and formation mechanisms of large hail, tornadoes, downbursts, and damaging convective wind gusts based on radar, satellite, and lightning observations, as well as the related features from damage surveys, are elucidated. In terms of operational applications, different types of identification and mesoanalysis techniques, and various forecasting and nowcasting techniques using methods such as the “ingredients-based” and deep learning algorithms, have been developed. As a result, the performance of operational SCW forecasts in China has been significantly improved.

Deciphering the extreme rainfall scenario over Indian landmass using satellite observations, reanalysis and model forecast: Case studies

Abstract In this study, we provide a comprehensive analysis of spatio-temporal distribution of extreme rainfall of different intensities (heavy, very heavy and extremely heavy) as well as wet spell over the Indian landmass during monsoon (June–August) of 2016–2018 through satellite observations. Moreover, there were ~ 35 extreme rainfall events during the study period, which has accumulated rainfall > 120 mm hr−1 and the first 12 events were identified (having highest amount of accumulated rainfall in a day out of 35 events) for our study. The event locations were so selected where there is not any point observations [automatic weather station (AWS) or automatic rain gauge (ARG)] at the site of extreme rainfall events, not even within 50 km radius of the event site or if any AWS/ARG site is present but were unrecorded or unreported during the time of extreme rainfall event. 70% of the extreme rainfall events (convective storms) occurred in the afternoon where 75% of the events indicates > 150 mm hr−1 accumulated rainfall and the rest 25% shows the accumulation rate to be > 120 mm hr−1. The factors influencing the extreme rainfall events to occur are obtained to be increased instability parameters (convective available potential energy, K-Index, Total Total's index), meteorological, thermodynamic and dynamical parameters (upper and lower level temperature gradient difference, vertical difference of equivalent potential temperature at 850 and 500 hPa, moisture flux convergence) and decreased temperature saturation deficit. The convective growth started initiating 6–7 h before the occurrence of extreme rainfall, indicating the favourable condition for severe convection. Furthermore, if the diurnal relative humidity range is maximum and there is a sharp increase in specific humidity at a location, there is a maximum probability of extreme rainfall after 5–6 h at that location. Both the Global Forecast System (GFS) and NCMRWF Unified Model (NCUM) captured the extreme rainfall events well for the year 2018 over the Indian region.

STUDY ON SENSITIVITY OF WIND FIELD VARIATION TO STRUCTURE AND DEVELOPMENT OF CONVECTIVE STORMS

In order to study the impacts of wind field variations in the middle and lower troposphere on the development and structure of storms, we carried out numerical experiments on cases of severe convection in the Jianghuai area under the background of cold vortex on April 28, 2015. The results show that the structure and development of convective storms are highly sensitive to the changes of wind fields, and the adjustment of wind fields in the middle or lower troposphere will lead to significant changes in the development and structure of storms. When the wind field in the middle or lower troposphere is weakened, the development of convective storms attenuates to some extent compared with that in the control experiment, and the ways of attenuation in the two experiments are different. In the attenuation test of wind field at the middle level, convective storms obviously weaken at all stages in its development, while for the wind field at the low level, the convective storms weaken only in the initial stage of storm. On the contrary, the enhancement of the wind field in the middle or lower troposphere is conducive to the development of convection, especially the enhancement in the middle troposphere. In contrast, the convective storms develop rapidly in this test, as the most intensive one. The wind field variations have significant impacts on the structure and organization of the storm. The enhancement of wind field in the middle troposphere facilitates the intension of the middle-level rotation in convective storm, the reduction of the storm scale, and the organized evolution of convective storms. The strengthening of the wind field in the lower troposphere is conducive to the development of the low-level secondary circulation of the storm and the cyclonic vorticity at the middle and low levels on the inflowing side of the storms.

Validation of a Multilag Estimator on NJU-CPOL and a Hybrid Approach for Improving Polarimetric Radar Data Quality

For the estimation of weak echo with low signal-to-noise ratio (SNR), a multilag estimator is developed, which has better performance than the conventional method. The performance of the multilag estimator is examined by theoretical analysis, simulated radar data and some specific observed data collected by a C-band polarimetric radar in previous research. In this paper, the multilag estimator is implemented and verified for Nanjing University C-band polarimetric Doppler weather radar (NJU-CPOL) during the Observation, Prediction and Analysis of Severe Convection of China (OPACC) field campaign in 2014. The implementation results are also compared with theoretical analysis, including the estimation of signal power, spectrum width, differential reflectivity, and copolar correlation coefficient. The results show that the improvement of the multilag estimator is little for signal power and differential reflectivity, but significant for spectrum width and copolar correlation coefficient when spectrum width is less than 2 ms−1, which implies a large correlation time scale. However, there are obvious biases from the multilag estimator in the regions with large spectrum width. Based on the performance analysis, a hybrid method is thus introduced and examined through NJU-CPOL observations. All lags including lag 0 of autocorrelation function (ACF) are used for moment estimation in this algorithm according to the maximum usable lag number. A case study shows that this hybrid method can improve moment estimation compared to both conventional estimator and multilag estimator, especially for weak weather echoes. The improvement will be significant if SNR decreases or the biases of noise power in the conventional estimator increase. In addition, this hybrid method is easy to implement on both operational and non-operational radars. It is also expected that the proposed hybrid method will have a better performance if applied to S-band polarimetric radars which have twice the maximum useable lags in the same conditions with C-band radars.

河北廊坊一次局地强对流天气过程分析

河北廊坊一次局地强对流天气过程分析

基于微波辐射计RPG-HATPRO的夏季午后强对流个例分析

基于微波辐射计RPG-HATPRO的夏季午后强对流个例分析

The cyclonic activity and frequency of dangerous weather phenomena over the territory of Belarus

In recent decades in the world, and in the Republic of Belarus in particular, the question of the impact of weather conditions on the development of sectors of the economy and life of the population has become acute. The sudden changes in weather conditions can lead to adverse and dangerous weather phenomena that cause significant damage to the country’s economy. This paper examines the frequency of dangerous weather phenomena in cyclones of different trajectories that moved through the territory of the Republic of Belarus during the period of 1995–2015. It is identified that southern and western cyclones caused dangerous weather events over the territory of Belarus. The interannual and seasonal frequency of cyclones causing dangerous weather phenomena in Belarus was analyzed. It is shown that the largest number of southern and western cyclones was characteristic mainly for the summer period, as well as the transitional seasons of the year, therefore the dangerous weather phenomena were associated mainly with the development of severe convection on atmospheric fronts. Such phenomena as very heavy rain, snowfall and wind had the highest frequency in cyclones, as in southern as western trajectories. The share of strong sticking of wet snow and large hail were isolated cases and these phenomena were recorded locally over the territory of country.

AENN: A GENERATIVE ADVERSARIAL NEURAL NETWORK FOR WEATHER RADAR ECHO EXTRAPOLATION

Abstract. Weather radar echo is one of the fundamental data for meteorological workers to weather systems identification and classification. Through the technique of weather radar echo extrapolation, the future short-term weather conditions can be predicted and severe convection storms can be warned. However, traditional extrapolation methods cannot offer accurate enough extrapolation results since their modeling capacity is limited, the recent deep learning based methods make some progress but still remains a problem of blurry prediction when making deeper extrapolation, which may due to they choose the mean square error as their loss function and that will lead to losing echo details. To address this problem and make a more realistic and accurate extrapolation, we propose a deep learning model called Adversarial Extrapolation Neural Network (AENN), which is a Generative Adversarial Network (GAN) structure and consist of a conditional generator and two discriminators, echo-frame discriminator and echo-sequence discriminator. The generator and discriminators are trained alternately in an adversarial way to make the final extrapolation results be realistic and accurate. To evaluate the model, we conduct experiments on extrapolating 0.5h, 1h, and 1.5h imminent future echoes, the results show that our proposed AENN can achieve the expected effect and outperforms other models significantly, which has a powerful potential application value for short-term weather forecasting.

The College Park, Maryland, Tornado of 24 September 2001

The 24 September 2001 College Park, Maryland, tornado was a long-track and strong tornado that passed within a close range of two Doppler radars. It was the third in a series of three tornadoes associated with a supercell storm that developed in Stafford County, Virginia, and initiated 3–4 km southwest of College Park and dissipated near Columbia, Howard County. The supercell tracked approximately 120 km and lasted for about 126 min. This study presents a synoptic and mesoscale overview of favorable conditions and forcing mechanisms that resulted in the severe convective outbreak associated with the College Park tornado. The results show many critical elements of the tornadic event, including a negative-tilted upper-level trough over the Ohio Valley, a jet stream with moderate vertical shear, a low-level warm, moist tongue of the air associated with strong southerly flow over south-central Maryland and Virginia, and significantly increased convective available potential energy (CAPE) during the late afternoon hours. A possible role of the urban heat island effects from Washington, DC, in increasing CAPE for the development of the supercell is discussed. Satellite imagery reveals the banded convective morphology with high cloud tops associated with the supercell that produced the College Park tornado. Operational WSR-88D data exhibit a high reflectivity “debris ball” or tornadic debris signature (TDS) within the hook echo, the evolution of the parent storm from a supercell structure to a bow echo, and a tornado cyclone signature (TCS). Many of the mesoscale features could be captured by contemporary numerical model analyses. This study concludes with a discussion of the effectiveness of the coordinated use of satellite and radar observations in the operational environment of nowcasting severe convection.

Lower-Tropospheric Influences on the Timing and Intensity of Afternoon Severe Convection over Modest Terrain in a Convection-Allowing Ensemble

AbstractA 50-member convection-allowing ensemble is used to examine effects of daytime PBL evolution and ambient flow interacting with modest terrain features on convection initiation (CI) in the lee of the Rocky Mountains. The examined case (4 June 2015) has isolated supercell storms that initiate during mid- to late afternoon along the northern portion of the Palmer Lake Divide, which is a ~0.5-km-deep zonally oriented terrain feature in east-central Colorado that extends eastward from the Rocky Mountains. To diagnose factors most crucial to storm development, two 10-member subensembles are constructed from the full 50-member ensemble. One subensemble (STRONG) has storm locations with mature storm intensities, and average timing of CI similar to that observed. The other subensemble (WEAK) has fewer storms, with generally weaker intensity, and delayed CI. Environmental composites constructed from these subensembles reveal a stronger surface horizontal convergence zone and moisture gradient in STRONG, resulting from 2–3.5 m s−1 stronger southerly winds on the south flank of the convergence zone. The stronger southerlies result from accelerated PBL growth and momentum mixing in the presence of strong low-to-midtropospheric vertical shear, which is facilitated by reduced above-PBL static stability in the composite STRONG initial condition. Stronger time-averaged low-to-midtropospheric upward motion coincides with the surface convergence zone in STRONG, and individual CI locations occur at the northeastern edge of the composite vertical motion maximum. Trajectory analysis with STRONG members confirms that the CI locations are consistent with large vertical displacements, and corresponding relative humidity increases leading to decreases in convective inhibition, as the southerly airstream ascends across the convergence zone.