An Object-Based Method for Tracking Convective Storms in Convection Allowing Models

The steady-state assumption commonly used in object-based tracking algorithms may be insufficient to determine the right track when a convective storm goes through a complicated evolution. Such an issue is exacerbated by the relatively coarse output frequency of current convection allowing model (CAM) forecasts (e.g., hourly), giving rise to many spatially well resolved but temporally not well resolved storms that steady-state assumption could not account for. To reliably track simulated storms in CAM outputs, this study proposed an object-based method with two new features. First, the method explicitly estimated the probability of each probable track based on either its immediate past and future motion or a reliable “first-guess motion” derived from storm climatology or near-storm environmental variables. Second, object size was incorporated into the method to help identify temporally not well resolved storms and minimize false tracks derived for them. Parameters of the new features were independently derived from a storm evolution analysis using 2-min Multi-Radar Multi-Sensor (MRMS) data and hourly CAM forecasts produced by the University of Oklahoma (OU) Multiscale data Assimilation and Predictability Laboratory (MAP) from May 2019. The performance of the new method was demonstrated with hourly MRMS and CAM forecast examples from May 2018. A systematic evaluation of four severe weather events indicated 99% accuracy achieved for over 600 hourly MRMS tracks derived with the proposed tracking method.

Environmental Evolution of Long-Lived Supercell Thunderstorms in the Great Plains

Long-lived supercells (containing mesocyclones persisting for at least 4 hours) are relatively rare, but present significant risk for society as a result of their intensity and associated hazards over an extended time period. The persistence of a rotating updraft is tied to near-storm environmental characteristics; however, given the established prevalence of mesoscale environmental heterogeneity near severe convection, it is unknown to what extent those near-storm characteristics vary over the lifetime of a supercell, nor how quickly the storm responds to such changes. This study examines 147 long-lived, isolated supercells, focusing on the evolution of their near-storm environments using model analysis soundings generated each hour throughout the storm’s lifetime. Environmental variability is quantified via a series of common forecasting parameters, with impacts of measured changes related to production of severe weather and overall storm longevity. The diurnal and maturity-relative distributions of forecasting parameters are examined, along with comparisons among subsets of marginally vs. very long-lived supercells, as well as dissipation before vs. after sunset. The diurnal cycle is a dominant trend over the lifetime of all supercells, with attendant impacts to relevant thermodynamic and kinematic parameters, timing of storm initiation and dissipation, as well as severe weather production. Notably, changes in the near-storm environment are connected to supercell longevity and generation of severe weather reports. The long-term goal of the above analyses is to enhance short-term forecasts of supercells by better anticipating storm evolution as a result of environmental variations.

The Evolution of Hail Production in Simulated Supercell Storms

Hailstorms pose a significant socioeconomic risk, necessitating detailed assessments of how the hail threat changes throughout their lifetimes. Hail production involves the favorable juxtaposition of ingredients, but how storm evolution affects these ingredients is unknown, limiting understanding of how hail production evolves. Unfortunately, neither surface hail reports nor radar-based swath estimates have adequate resolution or details needed to assess evolving hail production. Instead, we use a novel approach of coupling a detailed hail trajectory model to idealized convective storm simulations to better understand storm evolution’s influence on hail production. Hail production varies substantially throughout storms’ mature phases: maximum sizes vary by a factor of two, and the concentration of severe hail more than fivefold during 45-60-min periods. This variability arises from changes in updraft properties, which come from (i) changes in low-level convergence, and (ii) internal storm dynamics, including anticyclonic vortex shedding/storm splitting, and the response of the updraft’s airflow and supercooled liquid water content to these events. Hodograph shape strongly affects such behaviors. Straighter hodographs lead to more prolific hail production through wider updrafts and weaker mesocyclones, and a periodicity in hail size metrics associated with anticyclonic vortex shedding and/or storm splitting. In contrast, a curved hodograph (favorable for tornadoes) led to a storm with a stronger but more compact updraft, which occasionally produced giant (10-cm) hail, but that was a less-prolific severe hail producer overall. Unless storms are adequately sampled throughout their lifecycles, snapshots from ground reports will insufficiently resolve the true nature of hail production.

Study of strong tornadoes in Central Europe: Various aspects of Forecasting and Nowcasting

<p>Tornados pose a significant threat to life, property, and economy. Thus, an analysis of tornadoes is of high relevance. An understanding of historical events, e.g. regarding the characteristics of tornadic storms compared to multi-year storm statistics, may help to improve the situational awareness of future tornado events.</p><p>In this study, tornadic storms with a tornado intensity of F2 or stronger on the Fujita scale that occurred in recent years (2016 – 2020) in Germany were analyzed in detail. The four F3 tornadoes (Bützow, Affing, Bonndorf and Roetgen) and sixteen F2 tornadoes, which developed on 17 different days occurred in various parts of Germany. Most of the analysed tornadoes occurred from May to early September. The other three cases are typical winter cases that differ significantly from the summer cases in some aspects that are discussed where applicable. One case which happened in the third decade of September has characteristics form both, summer and winter, and is thus the only hybrid case. The great majority of all cases occurred during the second half of the day, most of them between 12 and 18 UTC. The most active hour was 16 to 17 UTC.</p><p>Regarding forecasting, similarities and differences of the prevailing synoptic and mesoscale conditions are assessed in addition to the convective environment of the events. Furthermore, the type of convection is analysed. The goal is to anticipate typical characteristics that enhance the threat of a potentially dangerous tornado situation. Using these findings may then help to strengthen the awareness of the forecaster. Two situations in mid- and upper-level flow are typical for the occurrence of strong tornadoes. On the majority of the analysed tornadic days, the event happened on the forward flank of a long wave trough that was slowly propagating eastward. The other typical situation is a vivid short wave trough passing rather fast over the area of interest from West to East.</p><p>Regarding nowcasting, a multi-source approach was applied to best analyse the events. For this purpose, radar reflectivity and rotation data were combined with lightning detection in order to analyse the tornadic storms with respect to storm mode and storm evolution as well as lightning and rotation characteristics. In many cases, radar radial wind data showed a persistent rotation track. The automatically detected mesocyclones had a vertical depth between 2.5 and 11 km at the time of the tornado, the diameter was above 8 km. The base of the rotation was low compared to multi-year statistics of all mesocyclonic storms. The lighting activity of the tornadic storms was high. In many cases, a lightning jump occurred between 5 and 120 minutes before the event.</p>

Improving object-oriented radar based nowcast by a nearest neighbour approach

The nowcast of rainfall storms at fine temporal and spatial resolutions is quite challenging due to the erratic nature of rainfall at such scales. Typically, rainfall storms are recognized by radar data, and extrapolated in the future by the Lagrangian persistence. However, storm evolution is much more dynamic and complex than the Lagrangian persistence, leading to short forecast horizons especially for convective events. Thus, the aim of this paper is to investigate the improvement that past similar storms can introduce to the object-oriented radar based nowcast. Here we propose a nearest neighbour approach that measures first the similarity between the “to-be-nowcasted” storm and past observed storms, and later uses the behaviour of the past most similar storms to issue either a single nowcast (by averaging the 4 most similar storm-responses) or an ensemble nowcast (by considering 30 most similar storm-responses). Three questions are tackled here: i) what features should be used to describe storms in order to check for similarity? ii) how to measure similarity between past storms? and iii) is this similarity useful for storm oriented nowcast? For this purpose, individual storms from 110 events in the period 2000–2018 recognized within the Hannover Radar Range (R~115 km2), Germany, were used as a basis for investigation. A “leave-one-event-out” cross-validation is employed to train and validate the nearest neighbour approach for the prediction of the area, mean intensity, the x and y velocity components of the “to-be-nowcasted” storm for lead times up to +3 hours. Prior to the training, two importance analyses methods (Pearson correlation and partial information correlation) are employed to identify the most important predictors. The results indicate that most of storms behave similarly, and the knowledge obtained from such similar past storms can improve considerably the storm nowcast compared to the Lagrangian persistence especially for convective events (storms shorter than 3 hours) and longer lead times (from 1 to 3 hours). The nearest neighbour approach seems promising, nevertheless there is still room for improvement by either increasing the sample size or employing more suitable methods for the predictor identification.

Storm-Time Features of the Ionospheric ELF/VLF Waves and Energetic Electron Fluxes Revealed by the China Seismo-Electromagnetic Satellite

This study reports the temporal and spatial distributions of the extremely/very low frequency (ELF/VLF) wave activities and the energetic electron fluxes in the ionosphere during an intense storm (geomagnetic activity index Dst of approximately −174 nT) that occurred on 26 August 2018, based on the observations by a set of detectors onboard the China Seismo-Electromagnetic Satellite (CSES). A good correlation of the ionospheric ELF/VLF wave activities with energetic electron precipitations during the various storm evolution phases was revealed. The strongest ELF/VLF emissions at a broad frequency band extending up to 20 kHz occurred from the near-end main phase to the early recovery phase of the storm, while the wave activities mainly appeared at the frequency range below 6 kHz during other phases. Variations in the precipitating fluxes were also spotted in correspondence with changing geomagnetic activity, with the max values primarily appearing outside of the plasmapause during active conditions. The energetic electrons at energies below 1.5 MeV got strong enhancements during the whole storm time on both the day and night side. Examinations of the half-orbit data showed that under the quiet condition, the CSES was able to depict the outer/inner radiation belt as well as the slot region well, whereas under disturbed conditions, such regions became less sharply defined. The regions poleward from geomagnetic latitudes over 50° were found to host the most robust electron precipitation regardless of the quiet or active conditions, and in the equatorward regions below 30°, flux enhancements were mainly observed during storm time and only occasionally in quiet time. The nightside ionosphere also showed remarkable temporal variability along with the storm evolution process but with relatively weaker wave activities and similar level of fluxes enhancement compared to the ones in the dayside ionosphere. The ELF/VLF whistler-mode waves recorded by the CSES mainly included structure-less VLF waves, structured VLF quasi-periodic emissions, and structure-less ELF hiss waves. A wave vector analysis showed that during storm time, these ELF/VLF whistler-mode waves obliquely propagated, mostly likely from the radiation belt toward the Earth direction. We suggest that energetic electrons in the high latitude ionosphere are most likely transported from the outer radiation belt as a consequence of their interactions with ELF/VLF waves.

Delayed Tornadogenesis Within New York State Severe Storms

Past observational research into tornadoes in the northeast United States (NEUS) has focused on integrated case studies of storm evolution or common supportive environmental conditions. A repeated theme in the former studies is the influence that the Hudson and Mohawk Valleys in New York State (NYS) may have on conditions supportive of tornado formation. Recent work regarding the latter has provided evidence that environments in these locations may indeed be more supportive of tornadoes than elsewhere in the NEUS. In this study, Weather Surveillance Radar–1988 Doppler data from 2008 to 2017 are used to investigate severe storm life cycles in NYS. Observed tornadic and non-tornadic severe cases were analyzed and compared to determine spatial and temporal differences in convective initiation (CI) points and severe event occurrence objectively within the storm paths. We find additional observational evidence supporting the hypothesis the Mohawk and Hudson Valley regions in NYS favor the occurrence of tornadogenesis: the substantially longer time it takes for storms that initiate in western NYS and Pennsylvania to become tornadic compared to storms that initiate in either central or eastern NYS. An analysis of approximate near-storm environments using the 13-km Rapid Refresh (RAP) is used to confirm that the long-lived storms encounter more tornado-favorable conditions leading up to tornadogenesis in the NYS valley regions.