Thunderstorm Forecasting Research Articles

A User-Focused Approach to Evaluating Probabilistic and Categorical Forecasts

Abstract A user-focused verification approach for evaluating probability forecasts of binary outcomes (also known as probabilistic classifiers) is demonstrated that (i) is based on proper scoring rules, (ii) focuses on user decision thresholds, and (iii) provides actionable insights. It is argued that when categorical performance diagrams and the critical success index are used to evaluate overall predictive performance, rather than the discrimination ability of probabilistic forecasts, they may produce misleading results. Instead, Murphy diagrams are shown to provide a better understanding of the overall predictive performance as a function of user probabilistic decision threshold. We illustrate how to select a proper scoring rule, based on the relative importance of different user decision thresholds, and how this choice impacts scores of overall predictive performance and supporting measures of discrimination and calibration. These approaches and ideas are demonstrated using several probabilistic thunderstorm forecast systems as well as synthetic forecast data. Furthermore, a fair method for comparing the performance of probabilistic and categorical forecasts is illustrated using the fixed risk multicategorical (FIRM) score, which is a proper scoring rule directly connected to values on the Murphy diagram. While the methods are illustrated using thunderstorm forecasts, they are applicable for evaluating probabilistic forecasts for any situation with binary outcomes. Significance Statement Recently, several papers have presented verification results for probabilistic forecasts using so-called categorical performance diagrams, which summarize multiple verification metrics. While categorical performance diagrams measure discrimination ability, we demonstrate how they can potentially lead to incorrect conclusions when evaluating overall predictive performance of probabilistic forecasts. By reviewing recent advances in the statistical literature, we show a comprehensive approach for the meteorological community that (i) does not reward a forecaster who “hedges” their forecast, (ii) focuses on the importance of the forecast user’s decision threshold(s), and (iii) provides actionable insights. Additionally, we present an approach for fairly comparing the skill of categorical forecasts to probabilistic forecasts.

Machine Learning Investigation of Downburst-Prone Environments in Canada

Abstract Thunderstorms are recognized as one of the most disastrous weather threats in Canada because of their power to cause substantial damage to human-made structures and even result in fatalities. It is therefore essential for operational forecasting to diagnose thunderstorms that generate damaging downdrafts of negatively buoyant air, known as downbursts. This study develops several machine learning models to identify environments supportive of downbursts in Canada. The models are trained and evaluated using 38 convective parameters calculated based on ERA5 reanalysis vertical profiles prior to thunderstorms with (306 cases) and without (19 132 cases) downbursts across Canada. Various resampling techniques are implemented to adjust data imbalance. An increase in the performance of the random forest (RF) model is observed when the support vector machine synthetic minority oversampling technique is utilized. The RF model outperforms other tested models, as indicated by model performance metrics and calibration. Several model interpretability methods highlight that the RF model has learned physical trends and patterns from the input variables. Moreover, the thermodynamic parameters are deemed to have higher impacts on the model outcomes compared to parcel, kinematic, and composite variables. For example, a considerable rise in the downburst probability is detected with an increase in cold pool strength. This study serves as one of the earliest attempts toward the fledgling field of machine learning applications in weather forecasting systems in Canada. The findings suggest that the developed model has the potential to enhance the effectiveness of issuing severe thunderstorm warnings in Canada, although further assessment with operational meteorologists is needed to validate its practical application. Significance Statement Severe thunderstorms demand particular attention in forecasting as their outflow can pose a serious threat to both structures and human life. This study uses machine learning techniques to predict whether or not a thunderstorm generates a damaging outflow in Canada. Atmospheric conditions that could trigger a severe thunderstorm are identified and discussed. Results show that the models have the potential to assist forecasters in better analyzing and predicting thunderstorms that generate destructive winds. Consequently, taking advantage of promising machine learning tools can yield more reliable forecasts of damaging thunderstorms, thereby mitigating the economic and societal burdens of these storms on Canadian communities.

Space-based optical imaging of blue corona discharges on a cumulonimbus cloud top

The ILAN-ES (Imaging of Lightning And Nocturnal Emissions from Space) experiment was conducted in April 2022 as part of the Axiom company AX-1 private mission to the International Space Station, in the framework of Rakia, an Israeli set of experiments selected for flight by the Ramon Foundation and the Israeli Space Agency. The mission objective was to record transient luminous events from the Cupola window in the ISS, based on preliminary thunderstorm forecasts uploaded to the crew 24–36 h in advance. A Nikon D6 camera with a 50 mm lens was used, in a video mode of 60 fps. During the 12-day mission, 82 different targets were identified for the ISS, of which 20 were imaged by the astronauts, yielding a total harvest >80 TLEs: sprites, Elves and BLUEs (blue corona discharges). We report here on opportune nadir observation of a thunderstorm that produced multiple blue events near the Myanmar-Thailand border on April 21st, 2022, at 21:30 UT. The storm produced many visible blue discharges of varying sizes and durations, in diameters ranging from hundreds of meters to a few km2. The emissions were mostly in blue, however the brightest events had also a conspicuous broadband red component. We used meteorological and ENTLN lightning data to establish the relationship between lightning type and the observable properties of the blue corona discharges.

Kombinace indexů stability pro předpovědi bouřek

Large amount of both historical and modern indices calculated from radio-souding data was developed for the storm prediction. In this work we processed 75 storm indices and other parameters, which could potentially be connected with the storm development. First part of the article is devoted to the effectivity of particular indices with special emphasis on the comparison of various modifications of the convective available potential energy (CAPE). Afterwards there were examined possibilities of the use of various combinations of indices and other parameters, and benefits of these combinations for the thunderstorm forecast. The forecast of the afternoon storms based on midday soundings is analysed in detail, as for this time point, a combination of indices has apparently the biggest benefit.

Thunderstorm prediction during pre-tactical air-traffic-flow management using convolutional neural networks

Thunderstorms can be a large source of disruption for European air-traffic management causing a chaotic state of operation within the airspace system. In current practice, air-traffic managers are provided with imprecise forecasts which limit their ability to plan strategically. As a result, weather mitigation is performed using tactical measures with a time horizon of three hours. Increasing the lead time of thunderstorm predictions to the day before operations could help air-traffic managers plan around weather and improve the efficiency of air-traffic-management operations. Emerging techniques based on machine learning have provided promising results, partly attributed to reduced human bias and improved capacity in predicting thunderstorms purely from numerical weather prediction data. In this paper, we expand on our previous work on thunderstorm forecasting, by applying convolutional neural networks (CNNs) to exploit the spatial characteristics embedded in the weather data. The learning task of predicting convection is formulated as a binary-classification problem based on satellite data. The performance of multiple CNN-based architectures, including a fully-convolutional neural network (FCN), a CNN-based encoder–decoder, a U-Net, and a pyramid-scene parsing network (PSPNet) are compared against a multi-layer-perceptron (MLP) network. Our work indicates that CNN-based architectures improve the performance of point-prediction models, with a fully-convolutional neural-network architecture having the best performance. Results show that CNN-based architectures can be used to increase the prediction lead time of thunderstorms. Lastly, a case study illustrating the applications of convection-prediction models in an air-traffic-management setting is presented.

Deficient Aeronautical Decision-Making Contributions to Fatal General Aviation Accidents.

INTRODUCTION: General aviation (GA), mainly comprised of light (≤12,500 lb) aircraft, maintains an inferior safety record compared with air carriers. To improve safety, aeronautical decision-making (ADM) practices have been advocated to GA pilots since 1991. Herein, we determined the extent to which GA pilots disregard such practices.METHODS: Fatal accidents (1991-2019) involving private pilots (PPLs) in single-engine airplanes were identified (N = 1481) from the National Transportation Safety Board AccessR database. Of these, deficient go/no-go and in-flight ADM-related mishaps were scored using the PAVE (pilot, aircraft, environment, external pressure)/IMSAFE (illness, medicine, stress, alcohol, fatigue, eating) and PPP (perceive, process, perform) models, respectively. Statistical testing used Poisson distributions, Fisher exact tests, and Mann-Whitney U-tests.RESULTS: Of the 1481 accidents, 846 were identified as deficient ADM-related. Electing to depart into a hazardous environment (PAVE), disregarding wellness (IMSAFE), and poor aircraft familiarity (PAVE) represented the most common categories (54%, 21%, and 20%, respectively) of errant go/no-go ADM. A 64% decline in fatal accidents related to errant go/no-go decisions for the environment category was evident over the 30-yr period, with little decrements in the other domains. Within the errant environment-related category accidents, the decision to depart into forecasted adverse weather (e.g., degraded visibility, icing, thunderstorms) constituted the most prevalent subcategory (56%, N = 195). Surprisingly, of this subcategory, accidents were overrepresented by over nine- and threefold for instrument-rated PPLs disregarding icing and thunderstorm forecasts, respectively.CONCLUSION: With little decrement in ADM-related accidents in the pilot, aircraft, and external pressure domains, new strategies to address such deficiencies for PPLs are warranted.Boyd DD, Scharf MT. Deficient aeronautical decision-making contributions to fatal general aviation accidents. Aerosp Med Hum Perform. 2023; 94(11):807-814.

The Development and Initial Capabilities of ThunderCast, a Deep Learning Model for Thunderstorm Nowcasting in the United States

Abstract This paper presents the Thunderstorm Nowcasting Tool (ThunderCast), a 24-h, year-round model for predicting the location of convection that is likely to initiate or remain a thunderstorm in the next 0–60 min in the continental United States, adapted from existing deep learning convection applications. ThunderCast utilizes a U-Net convolutional neural network for semantic segmentation trained on 320 km × 320 km data patches with four inputs and one target dataset. The inputs are satellite bands from the Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) in the visible, shortwave infrared, and longwave infrared spectra, and the target is Multi-Radar Multi-Sensor (MRMS) radar reflectivity at the −10°C isotherm in the atmosphere. On a pixel-by-pixel basis, ThunderCast has high accuracy, recall, and specificity but is subject to false-positive predictions resulting in low precision. However, the number of false positives decreases when buffering the target values with a 15 km × 15 km centered window, indicating ThunderCast’s predictions are useful within a buffered area. To demonstrate the initial prediction capabilities of ThunderCast, three case studies are presented: a mesoscale convective vortex, sea-breeze convection, and monsoonal convection in the southwestern United States. The case studies illustrate that the ThunderCast model effectively nowcasts the location of newly initiated and ongoing active convection, within the next 60 min, under a variety of geographical and meteorological conditions. Significance Statement In this research, a machine learning model is developed for short-term (0–60 min) forecasting of thunderstorms in the continental United States using geostationary satellite imagery as inputs for predicting active convection based on radar thresholds. Pending additional testing, the model may be able to provide decision-support services for thunderstorm forecasting. The case studies presented here indicate the model is able to nowcast convective initiation with 5–35 min of lead time in areas without radar coverage and anticipate future locations of storms without additional environmental context.

Diagnosing Factors Leading to an Incorrect Supercell Thunderstorm Forecast

Abstract On 28 April 2019, hourly forecasts from the operational High-Resolution Rapid Refresh (HRRR) model consistently predicted an isolated supercell storm late in the day near Dodge City, Kansas, that subsequently was not observed. Two convection-allowing model (CAM) ensemble runs are created to explore the reasons for this forecast error and implications for severe weather forecasting. The 40-member CAM ensembles are run using the HRRR configuration of the WRF-ARW Model at 3-km horizontal grid spacing. The Gridpoint Statistical Interpolation (GSI)-based ensemble Kalman filter is used to assimilate observations every 15 min from 1500 to 1900 UTC, with resulting ensemble forecasts run out to 0000 UTC. One ensemble only assimilates conventional observations, and its forecasts strongly resemble the operational HRRR with all ensemble members predicting a supercell storm near Dodge City. In the second ensemble, conventional observations plus observations of WSR-88D radar clear-air radial velocities, WSR-88D diagnosed convective boundary layer height, and GOES-16 all-sky infrared brightness temperatures are assimilated to improve forecasts of the preconvective environment, and its forecasts have half of the members predicting supercells. Results further show that the magnitude of the low-level meridional water vapor flux in the moist tongue largely separates members with and without supercells, with water vapor flux differences of 12% leading to these different outcomes. Additional experiments that assimilate only radar or satellite observations show that both are important to predictions of the meridional water vapor flux. This analysis suggests that mesoscale environmental uncertainty remains a challenge that is difficult to overcome. Significance Statement Forecasts from operational numerical models are the foundation of weather forecasting. There are times when these models make forecasts that do not come true, such as 28 April 2019 when successive forecasts from the operational High-Resolution Rapid Refresh (HRRR) model predicted a supercell storm late in the day near Dodge City, Kansas, that subsequently was not observed. Reasons for this forecast error are explored using numerical experiments. Results suggest that relatively small changes to the prestorm environment led to large differences in the evolution of storms on this day. This result emphasizes the challenges to operational severe weather forecasting and the continued need for improved use of all available observations to better define the atmospheric state given to forecast models.

Observing lightning and transient luminous events from the international space station during ILAN-ES: An astronaut's perspective

The ILAN-ES (Imaging of Lightning And Nocturnal Emissions from Space) experiment was conducted by Israeli astronaut Eytan Stibbe in April 2022 as part of the Axiom Space company AX-1 private mission to the International Space Station, in the framework of Rakia, a set of experiments selected for flight by the Ramon Foundation and the Israeli Space Agency. The mission objective was to manually record lightning and transient luminous events from the Cupola window in the ISS, based on preliminary thunderstorm forecasts uploaded to the crew 24–36 h in advance. A Nikon D6 camera with a 50 mm lens was used, in a video mode of 60 frames per second. During the 15-day mission, 82 different targets were uploaded to the ISS of which 20 were imaged by the astronauts, yielding a total harvest of 45 TLEs: sprites, Elves and Blue Corona Discharges. The methodology and execution by the ISS astronauts are described and recommendation for future observations by on-board human-operated instruments on the ISS are given.

Assimilating All-Sky Infrared Radiance Observations to Improve Ensemble Analyses and Short-Term Predictions of Thunderstorms

The experimental rapid-cycling Ensemble Kalman Filter (EnKF) in the convection-allowing ensemble-based Warn-on-Forecast System (WoFS) at the National Severe Storms Laboratory (NSSL) is used to assimilate all-sky infrared radiance observations from the GOES-16 7.3 μm water vapor channel in combination with radar wind and reflectivity observations to improve the analysis and subsequent forecast of severe thunderstorms (which occurred in Oklahoma on 2 May 2018). The method for radiance data assimilation is based primarily on the version used in WoFS. In addition, the methods for adaptive observation error inflation and background error inflation and the method of time-expanded sampling are also implemented in two groups of experiments to test their effectiveness and examine the impacts of radar observations and all-sky radiance observations on ensemble analyses and predictions of severe thunderstorms. Radar reflectivity observations and brightness temperature observations from the GOES-16 6.9 μm mid-level troposphere water vapor channel and 11.2 μm longwave window channel are used to evaluate the assimilation statistics and verify the forecasts in each experiment. The primary findings from the two groups of experiments are summarized: (i) Assimilating radar observations improves the overall (heavy) precipitation forecast up to 5 (4) h, according to the improved composite reflectivity forecast skill scores. (ii) Assimilating all-sky water vapor infrared radiance observations from GOES-16 in addition to radar observations improves the brightness temperature assimilation statistics and subsequent cloud cover forecast up to 6 h, but the improvements are not significantly affected by the adaptive observation and background error inflations. (iii) Time-expanded sampling can not only reduce the computational cost substantially but also slightly improve the forecast.

Entrainment in a Simulated Supercell Thunderstorm. Part III: The Influence of Decreased Environmental Humidity and General Effects upon Precipitation Efficiency

Abstract Entrainment is a key process that can modulate the intensity of supercells, and a better understanding of its impact could help improve forecasts of thunderstorms and the precipitation they produce. In Part III of this series, the three distinct mechanisms of entrainment identified during the mature stage of idealized supercell thunderstorms in Part I (overturning “ribbons” of horizontal vorticity, “disorganized turbulent eddies,” and the “storm-relative airstream”) are examined as the absolute humidity of the environment is decreased. The existence of these mechanisms in a more realistic simulated storm environment is also established. Entrainment is calculated as fluxes of air across the storm core surface; passive fluid tracers help determine the resulting dilution of the storm updraft. Model microphysical rates are used to examine the direct impacts of entrainment on hydrometeors within the storm updraft as well as precipitation that falls to the ground. Results show that as mixed-layer humidity decreases, the “ribbons” and turbulent eddy mechanisms decrease in intensity, but their effects on precipitation production change little. With decreasing humidity in the 3–4 km AGL layer, the storm-relative airstream entrains less humid low-level air into the storm core, decreasing the vertical mass flux, and therefore the precipitation produced by the storm. When the humidity in the mid- to upper troposphere (4–20 km AGL) is decreased, precipitation is significantly reduced, but not due to the effects of the entrained air. Rather, enhanced evaporation and sublimation of falling precipitation decreases the overall precipitation efficiency of the storm.

The Role of Instability Indices in Forecasting Thunderstorm and Non-Thunderstorm Days across Six Cities in India

Thunderstorms are one of the most damaging natural hazards demanding in-depth understanding and prediction. These convective systems form in an unstable environment which is quantitatively expressed in terms of instability indices. These indices are studied over six locations across the Indian landmass in an attempt to predict thunderstorm activity on any given day. A combination of multiple regression, logistic regression, and range analysis provides new insight into the prediction of these storms. A supervised machine learning-based logistic regression model is developed in this study for thunderstorm prediction over Patna and can be further extended for operational forecasting of Thunderstorms over the region. Critical thresholds for the instability indices are determined over the considered locations providing valuable insight into the domain of Thunderstorm prediction

Towards development of a storm index using satellite observation for near real‐time monitoring of thunderstorm over India

This study focuses on the development of a storm index by integrating Outgoing Longwave Radiation (OLR) from current operational Indian Satellites INSAT3D/3DR and reflectivity from space borne Precipitation Radar (PR) onboard Tropical Rainfall Measuring Mission (TRMM) for near real-time monitoring of thunderstorms. Using a reflectivity threshold for the detection of thunderstorms, an OLR threshold was identified from the relationship between OLR and reflectivity. A storm index has been defined as the OLR threshold divided by actual OLR values. The derived index can be very helpful for operational forecasts of intense and widespread thunderstorms over India and its neighborhood.

Iterative graph deformation for aircraft trajectory planning considering ensemble forecasting of thunderstorms

Convective weather poses a major threat that compromises the safe operation of flights while inducing delay and cost. The aircraft trajectory optimization problem under thunderstorms is addressed, proposing a novel heuristic approach that incorporates uncertainties in the evolution of convective cells. Namely, the Augmented Random Search (ARS) algorithm is used to deform a directed graph iteratively in the search of an optimal path. Flight time and fuel consumption are optimized while avoiding unsafe regions. By means of parallel programming on graphical processing units (GPUs), the computational times are reduced to enable near real-time operation. The suggested method is tested considering a dynamic model of an aircraft flying between two waypoints at constant flight level; the test scenario consists of a real weather forecast described by an ensemble of equiprobable members. The influence of the maximum number of iterations and the relative weights between optimization objectives is analysed. Results show that the methodology is able to avoid dangerous regions and find the balance between total time of flight and fuel consumption.

Doppler Lidar and Mobile Radiosonde Observation-Based Evaluation of Warn-on-Forecast System Predicted Near-Supercell Environments during TORUS 2019

Abstract Observational data collection is extremely hazardous in supercell storm environments, which makes for a scarcity of data used for evaluating the storm-scale guidance from convection allowing models (CAMs) like the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast System (WoFS). The Targeted Observations with UAS and Radar of Supercells (TORUS) 2019 field mission provided a rare opportunity to not only collect these observations, but to do so with advanced technology: vertically pointing Doppler lidar. One standing question for WoFS is how the system forecasts the feedback between supercells and their near-storm environment. The lidar can observe vertical profiles of wind over time, creating unique datasets to compare to WoFS kinematic predictions in rapidly evolving severe weather environments. Mobile radiosonde data are also presented to provide a thermodynamic comparison. The five lidar deployments (three of which observed tornadic supercells) analyzed show WoFS accurately predicted general kinematic trends in the inflow environment; however, the predicted feedback between the supercell and its environment, which resulted in enhanced inflow and larger storm-relative helicity (SRH), were muted relative to observations. The radiosonde observations reveal an overprediction of CAPE in WoFS forecasts, both in the near and far field, with an inverse relationship between the CAPE errors and distance from the storm. Significance Statement It is difficult to evaluate the accuracy of weather prediction model forecasts of severe thunderstorms because observations are rarely available near the storms. However, the TORUS 2019 field experiment collected multiple specialized observations in the near-storm environment of supercells, which are compared to the same near-storm environments predicted by the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast System (WoFS) to gauge its performance. Unique to this study is the use of mobile Doppler lidar observations in the evaluation; lidar can retrieve the horizontal winds in the few kilometers above ground on time scales of a few minutes. Using lidar and radiosonde observations in the near-storm environment of three tornadic supercells, we find that WoFS generally predicts the expected trends in the evolution of the near-storm wind profile, but the response is muted compared to observations. We also find an inverse relationship of errors in instability to distance from the storm. These results can aid model developers in refining model physics to better predict severe storms.

How the Environmental Lifting Condensation Level Affects the Sensitivity of Simulated Convective Storm Cold Pools to the Microphysics Parameterization

Abstract Several studies have documented the sensitivity of convective storm simulations to the microphysics parameterization, but there is less research documenting how these sensitivities change with environmental conditions. In this study, the influence of the lifting condensation level (LCL) on the sensitivity of simulated ordinary convective storm cold pools to the microphysics parameterization is examined. To do this, seven perturbed-microphysics ensembles with nine members each are used, where each ensemble uses a different base state with a surface-based LCL between 500 and 2000 m. A comparison of ensemble standard deviations of cold-pool properties shows a clear trend of increasing sensitivity to the microphysics as the LCL is raised. In physical terms, this trend is the result of lower relative humidities in high-LCL environments that increase low-level rain evaporational cooling rates, which magnifies differences in evaporation already present among the members of a given ensemble owing to the microphysics variations. Omitting supersaturation from the calculation of rain evaporation so that only the raindrop size distribution influences evaporation leads to more evaporation in the low-LCL simulations (owing to more drops), as well as a slightly larger spread in evaporational cooling amounts between members in the low-LCL ensembles. Cold pools in the low-LCL environments are also found to develop earlier and are initially more sensitive to raindrop breakup owing to a larger warm-cloud depth. Altogether, these results suggest that convective storms may be more predictable in low-LCL environments, and forecasts of convection in high-LCL environments may benefit the most from microphysics perturbations within an ensemble forecasting system. Significance Statement Computer simulations of thunderstorms can have grid spacings ranging from tens to thousands of meters. Because individual precipitation particles form on scales smaller than these grid spacings, the bulk effects of precipitation processes in models must be approximated. Past studies have found that models are sensitive to these approximations. In this study, we test whether the sensitivity to these approximations changes with the relative humidity in the lowest 1–2 km of the atmosphere. We found that increasing the relative humidity decreases the sensitivity of simulations to the precipitation process approximations. These results can inform meteorologists about the uncertainties surrounding computer-generated thunderstorm forecasts and suggest environmental conditions where using several computer models with different precipitation process approximations may be beneficial.

On the Changes in Convection-Allowing WRF Forecasts of MCS Evolution due to Decreases in Model Horizontal and Vertical Grid Spacing. Part I: Changes in Cold Pool Evolution

Abstract The degree of improvement in convective representation in NWP with horizontal grid spacings finer than 3 km remains debatable. While some research suggests subkilometer horizontal grid spacing is needed to resolve details of convective structures, other studies have shown that decreasing grid spacing from 3–4 to 1–2 km offers little additional value for forecasts of deep convection. In addition, few studies exist to show how changes in vertical grid spacing impact thunderstorm forecasts, especially when horizontal grid spacing is simultaneously decreased. The present research investigates how warm-season central U.S. simulated MCS cold pools for 11 observed cases are impacted by decreasing horizontal grid spacing from 3 to 1 km, while increasing the vertical levels from 50 to 100 in WRF runs. The 3-km runs with 100 levels produced the deepest and most negatively buoyant cold pools compared to all other grid spacings since updrafts were more poorly resolved, resulting in a higher flux of rearward-advected frozen hydrometeors, whose melting processes were augmented by the finer vertical grid spacing, which better resolved the melting layer. However, the more predominant signal among all 11 cases was for more expansive cold pools in 1-km runs, where the stronger and more abundant updrafts focused along the MCS leading line supported a larger volume of concentrated rearward hydrometeor advection and resultant latent cooling at lower levels.

A Novel Approach for the Global Detection and Nowcasting of Deep Convection and Thunderstorms

Thunderstorms are among the most common and most dangerous meteorological hazards in the world. They cause lightning and can lead to strong wind gusts, squall lines, hail and heavy precipitation combined with flooding, and therefore pose a threat to health and life, can cause enormous property damage and also endanger flight safety. Monitoring and forecast of thunderstorms are, therefore, important topics. In this work, a novel method for the detection and forecast of thunderstorms and strong convection is presented. The detection is based on the global GLD360 lightning data in combination with satellite information from the satellite series Meteosat, HIMAWARI and GOES, covering the complete geostationary ring. Three severity levels are defined depending on the occurrence of lightning and the brightness temperature difference of the water vapour channels and the infrared window channel (∼10.8 μm). The detection of thunderstorms and strong convection is the basis for the nowcasting up to 2 h, which is performed with the optical flow method TV-L1. This method provides the needed atmospheric motion vectors for the extrapolation of the thunderstorm movement. Both, the validation results as well as the feedback of the customers show the great value of the new NowCastSat-Aviation (NCS-A) method. For example, the Critical Success Index (CSI) is, with 0.64, still quite high for the 60 min forecast of severe thunderstorms. The method is operated 24/7 by the German Weather Service (DWD), and is used to provide thunderstorm information to aviation customers and the central weather forecast unit of DWD.

Sensitivity analysis of weather research and forecasting (WRF) model output variables to the thunderstorm lifecycle and its application

Accurate and timely forecasts of thunderstorms at lead times of more than 6 h can greatly improve the efficiency of air traffic flow management. To achieve this goal, thunderstorms occurring at Urumqi International Airport in 2020 were investigated using Weather Research and Forecasting (WRF) model output variables. An attempt was made to study the sensitivity of WRF output variables to the different stages (cumulus, mature, and dissipating) of the thunderstorm lifecycle. The variables considered in this paper include the wind speed (WSPD), composite radar reflectivity, echo top height, convective available potential energy (CAPE), convective inhibition (CIN), and lift index (LI). It was found that CIN is the most sensitive to an approaching thunderstorm. WSPD is extremely sensitive to the thunderstorm occurrence, closely followed by CIN. CAPE, CIN, and LI are all sensitive to dissipating thunderstorms. To improve thunderstorm forecasts, a simple and practical objective thunderstorm forecasting method, i.e., the thunderstorm probability (TSP) forecasting method, based on the combination of the above-mentioned variables, was proposed. Comparison of objective TSP forecasts and manual subjective forecasts indicated that TSP forecasts performed much better than did manual forecasts over the summer period of 2020.

Aerosynoptic conditions for the formation of winter thunderstorms on the example of the Minsk-2 airfield

In the conditions of modern climate warming, there is an increase in the number of hazardous weather phenomena, including thunderstorm activity, not only in the warm, but also in the cold season. The article analyses the occurrence of winter thunderstorms (October – March) and their connections with aerosynoptic conditions are revealed on the example of the Minsk-2 airfield for the period from 1989 to 2020. The determination of these relationships is necessary to make a qualitative forecast of thunderstorms as one of the hazardous weather phenomena for aviation. It was found that for the formation of winter thunderstorms requires the arrival of warm (with an air temperature above 0 °C) and moist air mass from the south or southwest, associated with the displacement of intensively deepening North Atlantic or southern cyclones in the stage of a young cyclone; the passage of primary and secondary active cold fronts with speeds of more than 30 km/h, cold fronts with waves and occlusion fronts, which provide the rise of warm and moist air of the lower troposphere; the existence of convective instability in the atmosphere; the presence tongue of warm and humid air, which is replaced by a trough of cold, at the level of 850 mb; the presence of low-jet currents and positive vorticity at the level of 700 mb and level of 500 mb, indicating the rise of an air particle; the presence of powerful jet currents of the western quarter at the level of 300 mb, enhancing convective processes. The obtained results can be used to replenish the methodological base on the forecasting of hazardous convective phenomena in Belarus, and also as recommendations for the forecast of thunderstorms in the cold season in the operational work of weather forecasters.