Abstract

Tornadic thunderstorms rely on the availability of sufficient low-level moisture, but the source regions of that moisture have not been explicitly demarcated. Using the NOAA Air Resources Laboratory HYSPLIT model and a Lagrangian-based diagnostic, moisture attribution was conducted to identify the moisture source regions of tornadic convection. This study reveals a seasonal cycle in the origins and advection patterns of water vapor contributing to winter and spring tornado-producing storms (1981–2017). The Gulf of Mexico is shown to be the predominant source of moisture during both winter and spring, making up more than 50% of all contributions. During winter, substantial moisture contributions for tornadic convection also emanate from the western Caribbean Sea (>19%) and North Atlantic Ocean (>12%). During late spring, land areas (e.g., soil and vegetation) of the contiguous United States (CONUS) play a more influential role (>24%). Moisture attribution was also conducted for nontornadic cases and tornado outbreaks. Findings show that moisture sources of nontornadic events are more proximal to the CONUS than moisture sources of tornado outbreaks. Oceanic influences on the water vapor content of air parcels were also explored to determine if they can increase the likelihood of an air mass attaining moisture that will eventually contribute to severe thunderstorms. Warmer sea surface temperatures were generally found to enhance evaporative fluxes of overlying air parcels. The influence of atmospheric features on synoptic-scale moisture advection was also analyzed; stronger extratropical cyclones and Great Plains low-level jet occurrences lead to increased meridional moisture flux.

Abstract

The paths of tornadoes have long been a subject of fascination since the meticulously drawn damage tracks by Dr. Tetsuya Theodore “Ted” Fujita. Though uncommon, some tornadoes have been noted to take sudden left turns from their previous path. This has the potential to present an extreme challenge to warning lead time, and the spread of timely, accurate information to broadcasters and emergency managers. While a few hypotheses exist as to why tornadoes deviate, none have been tested for their potential use in operational forecasting and nowcasting. As a result, such deviations go largely unanticipated by forecasters. A sample of 102 leftward deviant tornadic low-level mesocyclones was tracked via WSR-88D and assessed for their potential predictability. A simple hodograph technique is presented that shows promising skill in predicting the motion of deviant tornadoes, which, upon “occlusion,” detach from the parent storm’s updraft centroid and advect leftward or rearward by the low-level wind. This metric, a vector average of the parent storm motion and the mean wind in the lowest half-kilometer, proves effective at anticipating deviant tornado motion with a median error of less than 6 kt (1 kt ≈ 0.51 m s⁻¹). With over 25% of analyzed low-level mesocyclones deviating completely out of the tornado warning polygon issued by their respective National Weather Service Weather Forecast Office, the adoption of this new technique could improve warning performance. Furthermore, with over 35% of tornadoes becoming “deviant” almost immediately upon formation, the ability to anticipate such events may inspire a new paradigm for tornado warnings that, when covering unpredictable behavior, are proactive instead of reactive.

Abstract

Hodographs are valuable sources of pattern recognition in severe convective storm forecasting. Certain shapes are known to discriminate between single cell, multicell, and supercell storm organization. Various derived quantities such as storm-relative helicity (SRH) have been found to predict tornado potential and intensity. Over the years, collective research has established a conceptual model for tornadic hodographs (large and “looping,” with high SRH). However, considerably less attention has been given to constructing a similar conceptual model for hodographs of severe hail. This study explores how hodograph shape may differentiate between the environments of severe hail and tornadoes. While supercells are routinely assumed to carry the potential to produce all hazards, this is not always the case, and we explore why. The Storm Prediction Center (SPC) storm mode dataset is used to assess the environments of 8958 tornadoes and 7256 severe hail reports, produced by right- and left-moving supercells. Composite hodographs and indices to quantify wind shear are assessed for each hazard, and clear differences are found between the kinematic environments of hail-producing and tornadic supercells. The sensitivity of the hodograph to common thermodynamic variables was also examined, with buoyancy and moisture found to influence the shape associated with the hazards. The results suggest that differentiating between tornadic and hail-producing storms may be possible using properties of the hodograph alone. While anticipating hail size does not appear possible using only the hodograph, anticipating tornado intensity appears readily so. When coupled with buoyancy profiles, the hodograph may assist in differentiating between both hail size and tornado intensity.

Abstract

During 2013, multiple tornadoes occurred across Australia, leading to 147 injuries and considerable damage. This prompted speculation as to the frequency of these events in Australia, and whether 2013 constituted a record year. Leveraging media reports, public accounts, and the Bureau of Meteorology observational record, 69 tornadoes were identified for the year in comparison to the official count of 37 events. This identified set and the existing historical record were used to establish that, in terms of spatial distribution, 2013 was not abnormal relative to the existing climatology, but numerically exceeded any year in the bureau’s record. Evaluation of the environments in which these tornadoes formed illustrated that these conditions included tornado environments found elsewhere globally, but generally had a stronger dependence on shear magnitude than direction, and lower lifting condensation levels. Relative to local environment climatology, 2013 was also not anomalous. These results illustrate a range of tornadoes associated with cool season, tropical cyclone, east coast low, supercell tornado, and low shear/storm merger environments. Using this baseline, the spatial climatology from 1980 to 2019 as derived from the nonconditional frequency of favorable significant tornado parameter environments for the year is used to highlight that observations are likely an underestimation. Applying the results, discussion is made of the need to expand observing practices, climatology, forecasting guidelines for operational prediction, and improve the warning system. This highlights a need to ensure that the general public is appropriately informed of the tornado hazard in Australia, and provide them with the understanding to respond accordingly.

Abstract

This paper describes the development and analysis of an objective climatology of warm and cold fronts over North America from 1979 to 2018. Fronts are detected by a convolutional neural network (CNN), trained to emulate fronts drawn by human meteorologists. Predictors for the CNN are surface and 850-hPa fields of temperature, specific humidity, and vector wind from the ERA5 reanalysis. Gridded probabilities from the CNN are converted to 2D frontal regions, which are used to create the climatology. Overall, warm and cold fronts are most common in the Pacific and Atlantic cyclone tracks and the lee of the Rockies. In contrast with prior research, we find that the activity of warm and cold fronts is significantly modulated by the phase and intensity of El Niño–Southern Oscillation. The influence of El Niño is significant for winter warm fronts, winter cold fronts, and spring cold fronts, with activity decreasing over the continental United States and shifting northward with the Pacific and Atlantic cyclone tracks. Long-term trends are generally not significant, although we find a poleward shift in frontal activity during the winter and spring, consistent with prior research. We also identify a number of regional patterns, such as a significant long-term increase in warm fronts in the eastern tropical Pacific Ocean, which are characterized almost entirely by moisture gradients rather than temperature gradients.

Abstract

A global climatology for rapid cyclone intensification has been produced from the second NCEP reanalysis (NCEP2), the 25-yr Japanese Reanalysis (JRA-25), and the ECMWF reanalyses over the period 1979–2008. An improved (combined) criterion for identifying explosive cyclones has been developed based on preexisting definitions, offering a more balanced, normalized climatological distribution. The combined definition was found to significantly alter the population of explosive cyclones, with a reduction in “artificial” systems, which are found to compose 20% of the population determined by earlier definitions. Seasonally, winter was found to be the dominant formative period in both hemispheres, with a lower degree of interseasonal variability in the Southern Hemisphere (SH). Considered over the period 1979–2008, little change is observed in the frequency of systems outside of natural interannual variability in either hemisphere. Significant statistical differences have been found between reanalyses in the SH, while in contrast the Northern Hemisphere (NH) was characterized by strong positive correlations between reanalyses in almost all examined cases. Spatially, explosive cyclones are distributed into several distinct regions, with two regions in the northwest Pacific and the North Atlantic in the NH and three main regions in the SH. High-resolution and modern reanalysis data were also found to increase the climatology population of rapidly intensifying systems. This indicates that the reanalyses have apparently undergone increasing improvements in consistency over time, particularly in the SH.

Abstract

The interaction of two growing droplets in a supersaturated atmosphere has been examined, and the temperature and vapor density profiles have been determined. It is found that the smaller droplet tends to “catch up” with the larger at a slower rate than predicted by conventional diffusion theory. Consideration of droplet fallspeeds leads to the conclusion that, under atmospheric conditions, growth interaction becomes significant only for droplet “pairs” having equal or nearly equal radii. The number of such pairs is generally small enough so that the effect on the size distribution is quite small. Of a much greater importance is the possibility of a resulting attractive diffusio-phoretic force between two growing drops which, in turn, gives rise to a net velocity of one drop toward the other. If this diffusion force of attraction becomes sufficiently strong to overcome the hydrodynamic and thermo-phoretic forces acting in the opposite direction, both collision efficiencies and coagulation of small droplets could be further enhanced, thus accounting for departures from monodispersity in actual atmospheric clouds.

Abstract

Assessments of spatiotemporal severe hailfall characteristics using hail reports are plagued by serious limitations in report databases, including biases in reported sizes, occurrence time, and location. Multiple studies have used Next Generation Weather Radar (NEXRAD) network observations or environmental hail proxies from reanalyses. Previous work has specifically utilized the single-polarization radar parameter maximum expected size of hail (MESH). In addition to previous work being temporally limited, updates are needed to include recent improvements that have been made to MESH. This study aims to quantify severe hailfall characteristics during a 23-yr period, markedly longer than previous studies, using both radar observations and reanalysis data. First, the improved MESH configuration is applied to the full archive of gridded hourly radar observations known as GridRad (1995–2017). Next, environmental constraints from the Modern-Era Retrospective Analysis for Research and Applications, version 2, are applied to the MESH distributions to produce a corrected hailfall climatology that accounts for the reduced likelihood of hail reaching the ground. Spatial, diurnal, and seasonal patterns show that in contrast to the report climatology indicating one high-frequency hail maximum centered on the Great Plains, the MESH-only method characterizes two regions: the Great Plains and the Gulf Coast. The environmentally filtered MESH climatology reveals improved agreement between report characteristics (frequency, location, and timing) and the recently improved MESH calculation methods, and it reveals an overall increase in diagnosed hail days and westward broadening in the spatial maximum in the Great Plains than that seen in reports.

Abstract

Horizontal current and density data fields are analyzed in order to validate, from an experimental point of view, the contribution of the advective and Coriolis accelerations and the hydrostatic pressure gradient term to the balance of horizontal momentum. The relative importance of the vertical advection of horizontal velocity in this balance is estimated by solving the quasigeostrophic (QG) omega equation. The analysis of the balance of horizontal momentum is carried out using data from three consecutive high-resolution samplings of the Atlantic jet (AJ) and western Alboran gyre (WAG) on the eastern side of the Strait of Gibraltar.

The horizontal velocity reached maximum values of 1.30 m s⁻¹ in the AJ at the surface. The ageostrophic velocity field reaches maximum absolute values of 30 cm s⁻¹ at the surface, thus confirming the supergeostrophic nature of the AJ. At the surface the pressure gradient term reaches absolute values of 8–10 (×10⁻⁵ m s⁻²), the Coriolis acceleration 10–12 (×10⁻⁵ m s⁻²), and the advective horizontal acceleration 3 × 10⁻⁵ m s⁻². The vertical advection of horizontal velocity by the QG vertical velocity at 100 m is one order of magnitude smaller [O(10⁻⁶ m s⁻²)].

The geostrophic imbalance (difference between the pressure gradient term and the Coriolis acceleration) reaches 5 × 10⁻⁵ m s⁻² at the surface. The gradient imbalance (defined as the difference between the pressure gradient term and the Coriolis plus advective accelerations) is smaller than the geostrophic imbalance (being of order 2.5 × 10⁻⁵ m s⁻²) making gradient balance the best estimate of the balance of horizontal momentum given the characteristics (synopticity and experimental errors) of the analyzed dataset.

The gradient imbalance is not uniform in the horizontal but rather is larger in the AJ than in the WAG. From this result it is inferred that the AJ current experiences larger variations (larger local acceleration) than the WAG current.

Abstract

We present and evaluate a deep learning first-guess front-identification system that identifies cold, warm, stationary, and occluded fronts. Frontal boundaries play a key role in the daily weather around the world. Human-drawn fronts provided by the National Weather Service’s Weather Prediction Center, Ocean Prediction Center, Tropical Analysis and Forecast Branch, and Honolulu Forecast Office are treated as ground-truth labels for training the deep learning models. The models are trained using ERA5 data with variables known to be important for distinguishing frontal boundaries, including temperature, equivalent potential temperature, and wind velocity and direction at multiple heights. Using a 250-km neighborhood over the contiguous U.S. domain, our best models achieve critical success index scores of 0.60 for cold fronts, 0.43 for warm fronts, 0.48 for stationary fronts, 0.45 for occluded fronts, and 0.71 using a binary classification system (front/no front), whereas scores over the full unified surface analysis domain were lower. For cold and warm fronts and binary classification, these scores significantly outperform prior baseline methods that utilize 250-km neighborhoods. These first-guess deep learning algorithms can be used by forecasters to locate frontal boundaries more effectively and expedite the frontal analysis process.