Thunderstorm Forecasting Research Articles

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.

Diagnosing Supercell Environments: A Machine Learning Approach

Abstract The importance of discriminating between environments supportive of supercell thunderstorms and those that are not supportive is widely recognized due to significant hazards associated with supercell storms. Previous research has led to forecast indices such as the energy helicity index and the supercell composite parameter to aid supercell forecasts. In this study three machine learning models are developed to identify environments supportive of supercells: a support vector machine, an artificial neural network, and an ensemble of gradient boosted trees. These models are trained and tested using a sample of over 1000 Rapid Update Cycle version 2 (RUC-2) model soundings from near-storm environments of both supercell and nonsupercell storms. Results show that all three machine learning models outperform classifications using either the energy helicity index or supercell composite parameter by a statistically significant margin. Using several model interpretability methods, it is concluded that generally speaking the relationships learned by the machine learning models are physically reasonable. These findings further illustrate the potential utility of machine learning–based forecast tools for severe storm forecasting. Significance Statement Supercell thunderstorms are a type of thunderstorm that are important to forecast because they produce more tornadoes, hail, and wind gusts compared to other types of thunderstorms. This study uses machine learning to create models that predict if a supercell thunderstorm or nonsupercell thunderstorm is favored for a given environment. These models outperform current methods of assessing if a storm that forms will be a supercell. Using these models as guidance forecasters can better understand and predict if atmospheric conditions are favorable for the development of supercell thunderstorms. Improving forecasts of supercell thunderstorms using machine learning methods like those used in this study has the potential to limit the economic and societal impacts of these storms.

ON THE POSSIBILITY OF USING MAPS OF METEOROLOGICAL EVENTS FROM RESEARCH CENTRE «PLANET» FOR STUDYING AND PREDICTING THUNDERSTORMS ON THE URAL TERRITORY

The diagnosis and prediction of convective events are a difficult and challenging process. One of the most promising methods used for such tasks are hydrodynamic modelling and remote sensing. The aim of this research is to make a comparative analysis of meteorological events maps and data from observational network to find the possibility of using the maps from research center «Planet» for studying and predicting thunderstorms on the territory of the Ural. The spatiotemporal features of thunderstorms were identified. The repeatability and success of detection of this event were also determined, depending on the barium field, synoptic situation and specific characteristics of thunderstorms. During the study it was found that about 20 % of thunderstorms observed by weather stations were not predicted on weather maps; the high probability prediction was better than the medium and low probability forecasts of thunderstorms; spatial and temporal shifts were common in the prediction of thunderstorms. For a more detailed and objective analysis, it is recommended to work with a larger sample and use data from other sources like meteorological radiolocators, stormscopes, and others.

The Relationship between High-Presentation Asthma Days in Melbourne, Australia, and Modeled Thunderstorm Environments

Abstract Epidemic asthma events represent a significant risk to emergency services as well as the wider community. In southeastern Australia, these events occur in conjunction with relatively high amounts of grass pollen during the late spring and early summer, which may become concentrated in populated areas through atmospheric convergence caused by a number of physical mechanisms including thunderstorm outflow. Thunderstorm forecasts are therefore important for identifying epidemic asthma risk factors. However, the representation of thunderstorm environments using regional numerical weather prediction models, which are a key aspect of the construction of these forecasts, have not yet been systematically evaluated in the context of epidemic asthma events. Here, we evaluate diagnostics of thunderstorm environments from historical simulations of weather conditions in the vicinity of Melbourne, Australia, in relation to the identification of epidemic asthma cases based on hospital data from a set of controls. Skillful identification of epidemic asthma cases is achieved using a thunderstorm diagnostic that describes near-surface water vapor mixing ratio. This diagnostic is then used to gain insights on the variability of meteorological environments related to epidemic asthma in this region, including diurnal variations, long-term trends, and the relationship with large-scale climate drivers. Results suggest that there has been a long-term increase in days with high water vapor mixing ratio during the grass pollen season, with large-scale climate drivers having a limited influence on these conditions. Significance Statement We investigate the atmospheric conditions associated with epidemic thunderstorm asthma events in Melbourne, Australia, using historical model simulations of the weather. Conditions appear to be associated with high atmospheric moisture content, which relates to environments favorable for severe thunderstorms, but also potentially pollen rupturing as suggested by previous studies. These conditions are shown to be just as important as the concentration of grass pollen for a set of epidemic thunderstorm asthma events in this region. This means that weather model simulations of thunderstorm conditions can be incorporated into the forecasting process for epidemic asthma in Melbourne, Australia. We also investigate long-term variability in atmospheric conditions associated with severe thunderstorms, including relationships with the large-scale climate and long-term trends.

Exploring the Usefulness of Downscaling Free Forecasts from the Warn-on-Forecast System

Abstract The National Severe Storms Laboratory (NSSL) Warn-on-Forecast System (WoFS) is an experimental real-time rapidly updating convection-allowing ensemble that provides probabilistic short-term thunderstorm forecasts. This study evaluates the impacts of reducing the forecast model horizontal grid spacing Δx from 3 to 1.5 km on the WoFS deterministic and probabilistic forecast skill, using 11 case days selected from the 2020 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiment (SFE). Verification methods include (i) subjective forecaster impressions; (ii) a deterministic object-based technique that identifies forecast reflectivity and rotation track storm objects as contiguous local maxima in the composite reflectivity and updraft helicity fields, respectively, and matches them to observed storm objects; and (iii) a recently developed algorithm that matches observed mesocyclones to mesocyclone probability swath objects constructed from the full ensemble of rotation track objects. Reducing Δx fails to systematically improve deterministic skill in forecasting reflectivity object occurrence, as measured by critical success index (CSIDET), a metric that incorporates both probability of detection (PODDET) and false alarm ratio (FARDET). However, compared to the Δx = 3 km configuration, the Δx = 1.5 km WoFS shows improved midlevel mesocyclone detection, as evidenced by its statistically significant (i) higher CSIDET for deterministic midlevel rotation track objects and (ii) higher normalized area under the performance diagram curve (NAUPDC) score for probability swath objects. Comparison between Δx = 3 km and Δx = 1.5 km reflectivity object properties reveals that the latter have 30% stronger mean updraft speeds, 17% stronger median 80-m winds, 67% larger median hail diameter, and 28% higher median near-storm-maximum 0–3-km storm-relative helicity.

Iterative Graph Deformation for Aircraft Trajectory Planning Considering Ensemble Forecasting of Thunderstorms

Iterative Graph Deformation for Aircraft Trajectory Planning Considering Ensemble Forecasting of Thunderstorms

Prediction of Thunderstorms based on Atmospheric Instability Indices over Bangladesh using WRF-ARW Model

In this study an attempt has been made to inspect the forecasting of thunderstorms based on two cases (1st case: 17th May, 2019 and 2nd case: 31st March, 2019) over Dhaka using WRF Model. The model is run for 72 hours with 03 nested domain of 09 km, 03 km and 01 km horizontal resolutions using 0.25º X 0.25º six hourly global data assimilation system. For model simulation, Milbrandt-Yau Double-Moment 7-class scheme (9) has been used as microphysics scheme in this study. The model performance is evaluated by calculating hourly instability indices (VTI, TTI, KI, CTI, MCAPE, MCIN, BRN, LI, SI, SWI) value and have been compared with the threshold value of indices. Different meteorological parameters such as MSLP, temperature, winds at upper (300 hPa) and lower (925 hPa) level, relative humidity along with vertical cross section are also studied by the model and compared with the favorable conditions for forming of thunderstorms. Area rage rainfall (hourly) value has been also calculated and compared with indices value to comprehend the nature of thunderstorms. Observing the indices value it is seen that all indices value increase sharply 5-6 hours before of thunderstorm occurring and MCAPE is giving more reliable result. Moreover, this study shows that inner two domains (3 and 1 km resolution) are giving better results than outer one and which indices are more probable in forecasting of thunderstorm for our country as well as giving less Root Mean square Error. From the simulated and validated results, it can be concluded that the model performance of instability indices can be used as forecasting of thunderstorms over Bangladesh.

Do increasing horizontal resolution and downscaling approaches produce a skillful thunderstorm forecast?

Do increasing horizontal resolution and downscaling approaches produce a skillful thunderstorm forecast?

Informed scenario-based RRT∗ for aircraft trajectory planning under ensemble forecasting of thunderstorms

Thunderstorms represent a major hazard for flights, as they compromise the safety of both the airframe and the passengers. To address trajectory planning under thunderstorms, three variants of the scenario-based rapidly exploring random trees (SB-RRTs) are proposed. During an iterative process, the so-called SB-RRT, the SB-RRT∗ and the Informed SB-RRT∗ find safe trajectories by meeting a user-defined safety threshold. Additionally, the last two techniques converge to solutions of minimum flight length. Through parallelization on graphical processing units the required computational times are reduced substantially to become compatible with near real-time operation. The proposed methods are tested considering a kinematic model of an aircraft flying between two waypoints at constant flight level and airspeed; the test scenario is based on a realistic weather forecast and assumed to be described by an ensemble of equally likely members. Lastly, the influence of the number of scenarios, safety margin and iterations on the results is analyzed. Results show that the SB-RRTs are able to find safe and, in two of the algorithms, close-to-optimum solutions.

The Impact of Assimilating Satellite-Derived Layered Precipitable Water, Cloud Water Path, and Radar Data on Short-Range Thunderstorm Forecasts

AbstractWith the launch of GOES-16 in November 2016, effective utilization of its data in convective-scale numerical weather prediction (NWP) has the potential to improve high-impact weather (HIWeather) forecasts. In this study, the impact of satellite-derived layered precipitable water (LPW) and cloud water path (CWP) in addition to NEXRAD observations on short-term convective-scale NWP forecasts are examined using three severe weather cases that occurred in May 2017. In each case, satellite-derived CWP and LPW products and radar observations are assimilated into the Advanced Research Weather Research and Forecasting (WRF-ARW) Model using the NSSL hybrid Warn-on-Forecast (WoF) analysis and forecast system. The system includes two components: the GSI-EnKF system and a deterministic 3DEnVAR system. This study examines deterministic 0–6-h forecasts launched from the hybrid 3DEnVAR analyses for the three severe weather events. Three types of experiments are conducted and compared: (i) the control experiment (CTRL) without assimilating any data, (ii) the radar experiment (RAD) with the assimilation of radar and surface observations, and (iii) the satellite experiment (RADSAT) with the assimilation of all observations including surface-, radar-, and satellite-derived CWP and LPW. The results show that assimilating additional GOES products improves short-range forecasts by providing more accurate initial conditions, especially for moisture and temperature variables.

The Vice and Virtue of Increased Horizontal Resolution in Ensemble Forecasts of Tornadic Thunderstorms in Low-CAPE, High-Shear Environments

AbstractTornadoes have Lorenzian predictability horizons O(10) min, and convection-allowing ensemble prediction systems (EPSs) often provide probabilistic guidance of such events to forecasters. Given the O(0.1)-km length scale of tornadoes and O(1)-km scale of mesocyclones, operational models running at horizontal grid spacings (Δx) of 3 km may not capture narrower mesocyclones (typical of the southeastern United States) and certainly do not resolve most tornadoes per se. In any case, it requires O(50) times more computer power to reduce Δx by a factor of 3. Herein, to determine value in such an investment, we compare two EPSs, differing only in Δx (3 vs 1 km), for four low-CAPE, high-shear cases. Verification was grouped as 1) deterministic, traditional methods using pointwise evaluation, 2) a scale-aware probabilistic metric, and 3) a novel method via object identification and information theory. Results suggest 1-km forecasts better detect storms and any associated rapid low- and midlevel rotation, but at the cost of weak–moderate reflectivity forecast skill. The nature of improvement was sensitive to the case, variable, forecast lead time, and magnitude, precluding a straightforward aggregation of results. However, the distribution of object-specific information gain over all cases consistently shows greater average benefit from the 1-km EPS. We also reiterate the importance of verification methodology appropriate for the hazard of interest.

A new paradigm for short-range forecasting of severe weather over the Indian region

While destruction associated with floods during the monsoon season and cyclones receives wide attention, the extreme weather in the form of hail, lightning and high winds have also caused widespread devastation over India on a small spatial scale in recent years, especially during the period of March to June. India Meteorological Department (IMD) organized a special forecast improvement campaign during the period March to June of 2017–2019 when the weather forecasts at all offices of IMD were targeted towards an accurate forecast of the extreme form of thunderstorms and their associated impact in short range to nowcasting timescale and their dissemination. The purpose of this study is to quantify the improvement in operational thunderstorm forecast accuracy, in short range (24 h Severe Weather Guidance at subdivisional level) and nowcast scale (nowcasts for individual stations valid for 3 h and issued every three hours) during March to June of 2017 to 2019 and compare the same with the accuracy of previous years. As a result of these efforts, there has been a significant jump in forecast accuracy in the 24-h thunderstorm forecast as well as 3-h nowcast guidance for thunderstorms across the country. Probability of Detection (POD) scores for India as a whole for the 24-h thunderstorm forecast has doubled, while the false alarms (FAR) have remained at the same level as before the start of the forecast campaign. The results indicate that since a thunderstorm is a disastrous weather event, the forecasters generally tend towards spatial over-forecasting. However, this is not uniform across the months. There is systematic lower accuracy in the season transition months of March (winter to summer) and June (dry summer to wet summer). While POD decreases in both March and June, FAR decrease throughout the season. The significant evolution of atmospheric parameters (moisture in particular) as the season changes, favours the maturation of thunderstorms to cumulonimbus stage as the season progresses, and the problem of over forecasting in March becomes a problem of under forecasting of thunderstorms in June. Another reason for false alarms is the unconscious linkage of the thunderstorm with the pattern of rainfall occurrence. However, since all rain-giving clouds over India do not necessarily mature to the cumulonimbus stage, and vice versa, the two are not always related. This is particularly true for the more arid regions of the country, especially in March, where false alarms are higher. The poor density of reporting observatories compared to the mesoscale nature of the events may also increase false alarms, especially over the small maritime islands and the arid regions of the mainland. The accuracy of the All India 3 hourly station level nowcast also improved systematically since 2017. Despite these constraints, the improvements at all scales were possible due to (a) augmentation of observation network by the rapid expansion of Doppler radars network throughout the Indian mainland as well as the installation of a ground-based lightning detection network, (b) numerical modeling products introduced in 2019 to provide short-range forecasts for all aspects of convection; both of which are incorporated into the forecast framework through Standard Operating Procedures (SOP) to standardize the forecast procedure throughout the Indian region. A more objective forecast strategy, using data generated from a denser network of DWRs and crowdsourcing methods as well as more accurate mesoscale models will go a long way to further improve the thunderstorm forecasts.

The Impact of Assimilating ZDR Observations on Storm-Scale Ensemble Forecasts of the 31 May 2013 Oklahoma Storm Event

AbstractAssimilation of dual-polarization (dual-pol) observations provides more accurate storm-scale analyses to initialize forecasts of severe convective thunderstorms. This study investigates the impact assimilating experimental sector-scan dual-pol observations has on storm-scale ensemble forecasts and how this impact changes over different data assimilation (DA) windows using the ensemble Kalman filter (EnKF). Ensemble forecasts are initialized after 30, 45, and 60 minutes of DA for two sets of experiments that assimilate either reflectivity and radial velocity only (EXPZ) or reflectivity and radial velocity plus differential reflectivity (EXPZZDR). This study uses the 31 May 2013 Oklahoma event which included multiple storms that produced tornadoes and severe hail, with focus placed on two storms that impacted El Reno and Stillwater during the event.The earliest initialized forecast of EXPZZDR better predicts the evolution of the El Reno storm than EXPZ, but the two sets of experiments become similar at subsequent forecast times. However, the later EXPZZDR forecasts of the Stillwater storm, which organized towards the end of the DA window, produce improved results compared to EXPZ, in which the storm is less intense and weakens. Evaluation of forecast products for supercell mesocyclones (updraft helicity [UH]) and hail show similar results with earlier EXPZZDR forecasts better predicting the UH swaths of the El Reno storm and later forecasts producing improved UH and hail swaths for the Stillwater storm. The results indicate that the assimilation of ZDR over fewer DA cycles can produce improved forecasts when DA windows sufficiently cover storms during their initial development and organization.

Cb‑Fusion – forecasting thunderstorm cells up to 6 hours

As a severe weather phenomenon, thunderstorms can cause casualties and economic loss to the human society. A reliable forecast of these weather events would help to avoid or at least mitigate this damage. To date, the forecasting of thunderstorms however is still a challenge, especially for lead times of one hour and beyond. In this study we present a methodology to forecast deep-convection for several hours lead time: Cb-Fusion estimates the likelihood of thunderstorm occurrence for up to 6 hours in advance over a part of Central Europe, using a data fusion technique that blends data of multiple sources from observations, nowcasts, and numerical weather predictions with a high update rate. The Cb-Fusion is set up to operate in near real time. The skill of Cb-Fusion is evaluated based on 1743 hours of thunderstorm observations collected during the months April to October, 2019. Three categories of thunderstorm size have been distinguished: ‘large’ for a coverage area larger than 5000 km2, ‘medium’ for a coverage area between 5000 km2 and 500 km2, and ‘small’ for a coverage area smaller than 500 km2. Compared to thunderstorm forecasts from numerical models alone, the combination of data from various sources by Cb-Fusion results in a significantly better forecast skill. The study reveals that the forecast is reliable for up to 3 hours lead time for ‘medium’ and ‘large’ scale thunderstorms (median POD of 0.6 – 0.9) but little skill is found for ‘small’ scale thunderstorms and lead times between 3 and 6 hours (median POD of 0.05). It is argued that Cb-Fusion provides meaningful improvements in forecasting thunderstorms for various users.

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.

Impact of Assimilating Future Clear-Air Radial Velocity Observations from Phased-Array Radar on a Supercell Thunderstorm Forecast: An Observing System Simulation Experiment Study

Abstract Phased-array radar (PAR) technology offers the flexibility of sampling the storm and clear-air regions with different update times. As such, the radial velocity from clear-air regions, typically with a lower signal-to-noise ratio, can be measured more accurately. In this work, observing system simulation experiments are conducted to explore the potential value of assimilating clear-air radial velocity observations to improve numerical prediction of supercell thunderstorms. Synthetic PAR observations of a splitting supercell are assimilated at different life cycle stages using an ensemble Kalman filter. Results show that assimilating environmental clear-air radial velocity can reduce wind errors in the near-storm environment and within the precipitation region. Improvements in the forecast are seen at different stages, especially for the forecast after 30 min. After assimilating clear-air radial velocity observations, the probabilities of updraft helicity and precipitation within the corresponding swaths of the truth simulation increase up to 30%–40%. Additional diagnostics suggest that the more accurate track forecast, stronger vertical motion, and better-maintained supercell can be attributed to the better analysis and prediction of the mean environmental winds and linear and nonlinear dynamic forces. Consequently, assimilating clear-air radial velocity produces accurate storm structure (rotating updrafts), updraft size, and storm track, and improves the surface accumulated precipitation forecast. The performance of forecasts with a higher frequency of assimilating clear-air radial velocity does not show systematic improvement. These results highlight the potential of assimilating clear-air radial velocity observations to improve numerical weather prediction forecasts of supercell thunderstorms.

Progress in Severe Convective Weather Forecasting in China since the 1950s

Located in the Asian monsoon region, China frequently experiences severe convective weather (SCW), such as short-duration heavy rainfall (SDHR), thunderstorm high winds, hails, and occasional tornadoes. Progress in SCW forecasting in China is closely related to the construction and development of meteorological observation networks, especially weather radar and meteorological satellite networks. In the late 1950s, some county-level meteorological bureaus began to conduct empirical hail forecasting based on observations of clouds and surface meteorological variables. It took over half a century to develop a modern comprehensive operational monitoring and warning system for SCW forecast nationwide since the setup of the first weather radar in 1959. The operational SCW forecasting, including real-time monitoring, warnings valid for tens of minutes, watches valid for several hours, and outlooks covering lead times of up to three days, was established in 2009. Operational monitoring and forecasting of thunderstorms, SDHR, thunderstorm high winds, and hails have been carried out. The performance of operational SCW forecasting will be continually improved in the future with the development of convection-resolving numerical models (CRNMs), the upgrade of weather radar networks, the launch of new-generation meteorological satellites, better understanding of meso-γ and microscale SCW systems, and further application of artificial intelligence technology and CRNM predictions.

Detection of Thunderstorm Using Indian Navigation Satellite NavIC

Navigation satellites provide a good opportunity to monitor both ionospheric and tropospheric conditions by using propagation delays in the L-band signal. However, the availability of S-band frequency in the Indian Regional Navigation Satellite System, Navigation with Indian Constellation (NavIC), provides a unique opportunity to utilize signal strength for monitoring thunderstorms. This article, for the first time, demonstrates that NavIC satellite signal strength measurements can be used to detect thunderstorms. A dynamic time warping technique has been used to detect the presence of thunderstorm cloud systems. The method has been tested with several case studies in Kolkata using one-year measurements. The results indicate suitability of such techniques for monitoring and forecasting of thunderstorms using a navigation satellite.

Probabilistic thunderstorm forecasting by blending multiple ensembles

In numerical weather prediction models, point thunderstorm forecasts tend to have little predictive value beyond a few hours. Thunderstorms are difficult to predict due largely to their typically small size and correspondingly limited intrinsic predictability. We present an algorithm that predicts the probability of thunderstorm occurrence by blending multiple ensemble predictions. It combines several post-processing steps: spatial neighbourhood smoothing, dressing of probability density functions, adjusting sensitivity to model output, ensemble weighting, and calibration of the output probabilities. These operators are tuned using a machine learning technique that optimizes forecast value measured by event detection and false alarm rates. An evaluation during summer 2018 over western Europe demonstrates that the method can be deployed using about a month of historical data. Post-processed thunderstorm probabilities are substantially better than raw ensemble output. Forecast ranges from 9 hours to 4 days are studied using four ensembles: a three-member lagged ensemble, a 12-member non-lagged limited area ensemble, and two global ensembles including the recently implemented ECMWF thunderstorm diagnostic. The ensembles are combined in order to produce forecasts at all ranges. In most tested configurations, the combination of two ensembles outperforms single-ensemble output. The performance of the combination is degraded if one of the ensembles used is much worse than the other. These results provide measures of thunderstorm predictability in terms of effective resolution, diurnal variability and maximum forecast horizon.

Thunderstorm Classification Functions Based on Instability Indices and GNSS IWV for the Sofia Plain

Bulgaria is a country with a high frequency of hail and thunderstorms from May to September. For the May–September 2010–2015 period, statistical regression analysis was applied to identify predictors/classification functions that contribute skills to thunderstorm forecasting in the Sofia plain. The functions are based on (1) instability indices computed from radiosonde data from Sofia station F1, and (2) combination of instability indices and Integrated Water Vapor (IWV), derived from the Global Navigation Satellite System (GNSS) station Sofia-Plana, F2. Analysis of the probability of detection and the false alarm ratio scores showed the superiority of the F2 classification function, with the best performance in May, followed by June and September. F1 and F2 scores were computed for independent data samples in the period 2017–2018 and confirmed the findings for the 2010–2015 period. Analysis of IWV and lightning flash rates for a multicell and supercell thunderstorm in June and July 2014 showed that the monthly IWV thresholds are reached 14.5 and 3.5 hours before the thunderstorm, respectively. The supercell IWV peak registered 40 min before the thunderstorm, followed by a local IWV minimum corresponding to a peak in the flash rate. In both cases, an increase of IWV during severe hail was registered, which is likely related to the hydrometeor contribution to GNSS path delay. The results of this study will be integrated into the Bulgarian Integrated NowCAsting tool for thunderstorm forecasting in the warm/convective season.